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It turns out that my work in nonlinear dynamics directly maps to Integrate and Fire models and could even provide a new way to control them (manipulate the firing rates). However, it is unclear to me if my findings are interesting or just old news.
I am looking for literature on the intersection of control and neuroscience. Specifically I am interested in any research, theoretical, experimental or simulation-based that dwells on the control of neural systems.
I am especially interested in any work that takes a mathematical point of view on this issue and on any work that summarises the state of art in this field.
Forgive me is this seems trivial to you, but coming form complex systems theory I did my best and found nothing.
The activity of motor cortex (MI) neurons conveys movement intent sufficiently well to be used as a control signal to operate artificial devices 1,2,3 , but until now this has called for extensive training or has been confined to a limited movement repertoire 2,3 . Here we show how activity from a few (7–30) MI neurons can be decoded into a signal that a monkey is able to use immediately to move a computer cursor to any new position in its workspace (14° × 14° visual angle). Our results, which are based on recordings made by an electrode array that is suitable for human use 4,5 , indicate that neurally based control of movement may eventually be feasible in paralysed humans.
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Download CBSE class 11th revision notes for Chapter 21 Neural Control and Coordination class 11 Notes Biology in PDF format for free. Download revision notes for Neural Control and Coordination class 11 Notes Biology and score high in exams. These are the Neural Control and Coordination class 11 Notes Biology prepared by team of expert teachers. The revision notes help you revise the whole chapter in minutes. Revising notes in exam days is on of the best tips recommended by teachers during exam days.
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Neural Control and Coordination class 11 Notes Biology
- Coordination is the process through which two or more organs interact and complement the function of each other.
- Neural system provides an organized network of point to point connection for quick coordination. The endocrine system provides chemical integration through hormones.
- Neural system of animals is composed of specialized cells called neuron, which can detect, receive and transmit different kinds of stimuli. In hydra neural system is composed of network of neuron. In insects it consists of brain and a number of ganglia. Vertebrates have highly developed neural system.
- Central nervous system (CNS) includes brain and spinal cord. It is the site for information processing and control.
- Peripheral nervous system includes all nerves associated with CNS. There are two types of nerve fibres-
- Afferent fibres- transmit impulses from tissue/organ to CNS.
- Efferent fibres- transmit regulatory impulses from CNS to concerned peripheral organs.
Somatic neural systems relay impulses from CNS to skeletal muscles. Autonomic neural system transmits impulses from CNS to involuntary system and smooth muscles.
Neuron as Structural and Functional Unit of Neural System
Neuron is made up of three major parts- cell body, dendrite and axon.
- Cell body contains cytoplasm, cell organelles and Nissl’s granules. Short fibres projecting out from cell body is called dendrites. The axon is long fibre having branched structure at the end that terminates into knob like structure called synaptic knob.
- Based on number of axon and dendrites neuron are of three types-
- Multipolar– one axon and two or more dendrite found in cerebral cortex.
- Bipolar– one axon and one dendrite found in retina of eyes.
- Unipolar– cell body with only one axon found in embryonic stage.
- There are two types of axon-
- Myelinated– fibres are enveloped with Schwann cells to form myelin sheath around the axon. The gap between two myelin sheaths is called nodes of Ranvier. Found in spinal and cranial nerves.
- Unmyelinated- fibre is enclosed by Schwann cells that do not form myelin sheath around the axon. Found in autonomous and somatic neural system.
Generation and Conduction of Nerve Impulse
- Ion channels are present in neural membrane which is selectively permeable to different ions. When neuron is not conducting impulse (resting), axonal membrane is more permeable to K+ ions and impermeable to Na+ ions.
- Ionic gradient across the resting membrane is maintained by active transport of ions by sodium-potassium pump. This will develop positive charge outside the axonal membrane and negative charge on inner side.
- The electrical potential difference across the resting membrane is called resting potential.
- When stimulus is applied at site A, the membrane becomes permeable to Na+ ions to make rapid influx of Na+ ions to create outer surface negatively charged and inner membrane positively charged that create Action Potential or nerve impulse.
- The nerve impulse from A moves to B in inner surface and B to A on outer surface. This process is repeated several times to transmit the impulse.
- Nerve impulse is transmitted from one neuron to another neuron through synapse.
- There are two types of synapse-
- Electrical synapse- the membrane of pre and post synaptic neuron is very close to each other and current flow directly from one neuron to another.
- Chemical synapse- pre and post synaptic neuron is separated by fluid filled space called synaptic cleft. Neurotransmitters are involved in transmission of impulses.
Central Neural System –Brain is the central information processing organ of our body and act as command and control centre. Human brain is protected by skull (cranium) and three layers of cranial meninges- outer dura mater, middle arachnoid and inner pia mater.
Brain can be divided 3 parts- forebrain, midbrain and hindbrain.
Forebrain– consists of cerebrum, thalamus and hypothalamus. Cerebrum is divided into left and right cerebral hemispheres which are covered by cerebral cortex (grey matter). Cerebral cortex contains sensory neuron, motor neuron and association area. Association area controls complex functions like intersensory associations, memory and
Thalamus– cerebrum wraps around a structure called thalamus. It is a major
coordinating centre for sensory and motor signaling.
Hypothalamus controls the urge for eating, drinking and body temperature. They also release hypothalamic hormones. Limbic system is involved in controlling sexual behavior and expression of emotional reactions.
Midbrain is located between hypothalamus and pons of hindbrain. Dorsal portion consists of four round lobes called corpora quadrigemina. They are involved in relay of impulses back and forth between cerebrum, cerebellum, pons and medulla.
Hind brain consists of pons, medulla oblongata and cerebellum.
Pons consists of fibre tracts that interconnect different regions of the brain.
The medulla contains centres which control respiration, cardiovascular reflexes and gastric secretions.
Cerebellum controls balance and posture.
Reflex action is a spontaneous autonomic mechanical response to a stimulus without the will of the organism. It is controlled by spinal cord. The afferent neuron receives the signal from sensory organs and transmits the impulse to CNS. The efferent neuron carries the impulse from CNS to effector. Ex- knee-jerk reflex. The path followed by reflex action is called reflex arc.
Human Eye – spherical structure consists of three layers, external layer is sclera whose anterior part is called cornea, middle layer choroid and innermost layer is called retina.
Retina contains three layers of cells – inner ganglion cells, middle bipolar cells and outer photoreceptor cells.
There are two types of photoreceptor cells called rods and cones. The daylight (photopic) vision and colour vision are functions of cones. The twilight (scotopic) vision is the function of the rods.
Mechanism of Vision
The light rays of visible wavelength fall on retina through cornea and lens to generate impulses in rods and cones. Photosensitive pigments opsin and retinal get dissociated due to light to change its shape. Change in shape of opsin cause change of permeability to generate action potential that is transmitted to brain via optic nerve.
Divided into three regions: outer ear, middle ear and inner ear.
The middle ear contains three ossicles called malleus, incus and stapes. The fluid
filled inner ear is called the labyrinth, and the coiled portion of the labyrinth is called cochlea.
The organ of corti contains hair cells that act as auditory receptors and is located on the basilar membrane.
Mechanism of Hearing
External ear receives the sound wave and directs them to ear drum. Vibration of ear drum leads to vibration of ear ossicles. The vibration reaches cochlea that generate wave in lymph. The waves generate ripples in basilar membrane and hair cells in them. As a result, nerve impulses are generated in afferent neuron that passes to brain via auditory nerves.
Neural Control and Coordination
Olfactory Bulb :- Olfactory bulbs receive impulses pertaining to smell from the olfactory epithelium.
- Cerebrum forms major part of brain.
- The cerebrum is divided into two hemispheres by prominent longitudinal fissure. The two hemi-spheres are connected by a bundle of trans-verse fibres called corpus callosum. or colossal commissureThe left half of thebrain controls the right side of the body and vice versa.
- Each cerebral hemisphere is divided by other grooves into four lobes namely, frontal, tem-poral, parietal and
- Cerebral cortex is the outer layer of the It is made of grey matter. Neuronal cell bodies are concentrated in cerebral cortex.
- The surface of each cerebral hemisphere shows many convolutions called gyri (singular gyrus) the deepest and shallower grooves between the folds are called fissures and sulci respectively.
- The sulci and gyri increase the surface area of the cortex.
- In cerebral hemispheres three types of functional areas are present.
- Sensory areas receive and interpret sensory impulses from receptors.
- Motor areas which control voluntary muscular movements.
- Association areas which are neither clearly sensory nor motor in function and they deal with more complex ‘integrative functions’ such
- The main parts of the diencephalon are the epithalamus. thalamus and hypothalamus
- Epithalamus : It is the roof of the dincephalon. It is a non-nervous part which is fused with the pia mater to form the anterior choroid plexus.
- Just behind the anterior choroid plexus, the epithelium of the epithalamus forms a pinealstalk, which ends in a rounded structure called pineal body.
- Thalamus : It lies superior to the mid brain. It is the major coordinating centre for sensory and motor signalling.
- Hypothalamus (the thermostat of the body) :
It lies at the base of the thalamus.
- The hypothalamus forms a funnel shaped down-ward extension called infundibulum connecting the hypothalamus with the pituitary gland.
- Hypothalamus contains neural centres for hunger, thirst, satiety and groups of
neurosecretory cells, which secrete hormones called hypothalamic hormones.
- Hypothalamus controls and integrates the activities of the autonomous nervous system.
- Limbic system : The inner parts of the cerebral hemispheres and a group of associated deep structures like the hippocampus, the amygdala, etc.
- The limbic system along with hypothalamus is involved in the regulation of sexual behaviour and expression of emotional reactions.
Mid Brain (Meseccephalon)
- Mid brain is located between thalamus/hypo thalamus of the fore brain and pons of the hind brain.
- A canal called the cerebral aqueduct passes through the mid brain.
- It contains four rounded lobes, the corporaquadrigemina. Its chief structures are superior colliculi and inferior colliculi.
- The anterior, larger superior pair of colliculi are concerned with vision.
- The posterior, smaller inferior pair of colliculi are concern with auditory functions.
- The ventral portion of the midbrain consists of a pair of longitudinal bands of nervous tissue called cerebral peduncles or Crura cerebri.
Hind Brain or Rhombencephalon
- The hind brain comprises pons, cerebellum and medulla (Medulla oblongata).
- The cerebellum is the second-largest part of the brain. It is wedged between cerebral hemi-spheres and brain stem. The cerebellum is divided into two hemispheres and central
- Each cerebellar hemisphere consists of three lobes namely anterior, posterior and floccular lobes.
- The surface of each hemisphere is made up of gray matter surrounding a large mass of white matter .
- The cerebellum is solid. It has a braching tree-like core of white matter called arborvitae surrounded by a sheath of grey matter.
- The cerebellum is vital to the control of rapid muscular activities, such as running, typing and even talking.
- Pons appears as a rounded bulge on the underside of the brain stem.
- It separates the midbrain from the medulla oblongata.
- It consists of nerve fibres which form a bridge between the two cerebellar hemispheres
- It is a relay station between the cerebellum, spinal cord and the rest of the brain.
- Pneumotaxic area and apneustic area are involved in the control of the respiratory muscles
- Pons serves as a neuronal link between the cerebral cortex and the cerebellum.
Medulla oblongata passes out of the foramen magnum and joins the spinal cord
- It has a very thin , vascular folded structure called posterior choroid plexus
- The medulla oblongata is the lower portion of the brainstem. It is inferior to the pons. It controls autonomic functions like respiration, cardiovascular reflexes and gastric secretions, swallowing vomiting, sneezing, hiccuping etc.
- The medulla oblongata, the pons, and mid-brain lie in a portion of the brain known as brain stem connecting the fore brain and the spinal cord.
- These structures include many tracts of nerve fibres and masses of grey matter called nuclei.
Ventricles of The Human Brain
- Human brain consists of four ventricles.
- The first and second ventricles ( lateral ventricles or paracoels) are present in the right and left cerebral hemisheres respectively.
- The two paracoels are connected to the median diocoel individually by the two foraminaof Monro ( Interventricular foramina)
- The third ventricle ( diocoel) occurs in the
- The fourth ventricle ( myelocoel) is present in the medulla.
- The myelocoel and the diocoel are connected by a narrow canal called iter or aqueduct ofsylvius/ cereral aqueduct.
- The metacoel is continuous with the central canal of the spinal cord
- The ventricles of the brain, and the subrachnoid space are filled with Cerebro-spinal fluid(CSF) .
- Cerebro-spinal fluid (CSF) . is an alkaline, colourless fluid which is filtered from the choroid plexuses into the ventricles of the brain
- CSF serves as shock absorbing medium CSF is recycled (flushed) 4 times per day in order to clear out metabolites and toxins
IMPORTANT FUNCTIONS OF BRAIN
FOREBRAIN Olfactory region
Thinking, Intelligence, Memory, Ability to learn
Reasoning, Conscious, Control, Speech
micturition (passing of urine)
Defecation, voluntary forced breathing, voluntary muscular coordination etc.
Integrates the activities of the autonomic nervous
system, has control centres for hunger. Thirst,
Sweating, appetite hunger, satiety etc. and regulate body temperature so thermostat of the body.
MIDBRAIN Reflex centre of visual and auditory sensation HINDBRAIN Cerebellum Involuntary muscular coordination. Maintain
posture, Orientation and equilibrium of the body.
Medulla Oblongata Regulate heart rate, involuntary breathing
respiratory centre, Blood pressure (Vasoconstriction and vasodilation). Gut
peristalsis, food swallowing, vomiting, Gland secretion.
- Spinal cord is located in the vertebral canal (neural canal) of the vertebral column. It is an elongated cylindrical structure which lies in the neural canal of the vertebral column and is continued with the medulla oblongata through foramen magnum of the skull.
- In the adult, it extends from the medulla oblongata to the superior border of the second lumbar vertebra.
- In the neck region, a thickening in the spinal cord is called the superior cervical enlargement.
- Inferior enlargement in the Spinal cord is called the lumbar enlargement
- Just inferior to the lumbar enlargement, The spinal cord tapers to a conical portion known as the conus medullaris.
- Conus medullaris ends at the level of the intervertebral disc between the first and second lumbar vertebrae in an adult.
- The extension of the conus medullaris as the non-nervous fibrous tissue to the coccyx is called ‘filum terminale’.
Internal anatomy of spinal cord
- Unlike the brain, grey matter of the spinal cord is located centrally, surrounded by outer white matter.
- In a cross section, spinal cord consists of a H or a butterfly shaped central core of grey matter surrounding a central canal and an outer layer, the white matter. The spinal cord is divided into right and left halves by two grooves namely
a) posterior, median dorsal sulcus
b) Anterior ventral median fissure
- Narrow longitudinal cavity of Spinal cord is called central canal.
- central canal is lined by the ependymal epithelium, continues with 4 th ventricle of medulla oblongata.
- The grey matter is subdivided into regions called ‘anterior and posterior horns’.
White matter: White matter consists of bundles of myelinated axons and it is organized into the regions called anterior (ventral) funiculus, posterior (dorsal) funiculus and lateral funiculi.
- The spinal cord acts as a coordinating centre for simple spinal reflexes. It also acts as the
- ‘middle man’ between the receptors and the effectors
5. PERIPHERAL NERVOUS SYSTEM (PNS)
- It consists of nerves connected to or arising from the central nervous system.
- It has cranial and spinal nerves.
- In man 12 pairs of cranial nerves are present.
- In all anamniotes the total no of cranial nerves are 10 pairs.
- In all amniotes the total no of cranial nerves are 12 pairs except snakes.
Note : Refer to chart
- Spinal nerves arise from the grey matter of spinal cord.
- There are 31 pairs of spinal nerves in man.
- Spinal nerves are mixed in nature because each spinal nerve is formed of 2 roots
a) sensory (afferent) root
b) motor (efferent) root.
- Each spinal nerve is divided into three rami
a) Ramus dorsalis
b) Ramus ventralis
c) Ramus communicans – it joins sympathetic ganglion of autonomic nervous system.
- The spinal nerves in man are divided into five groups
- Spinal nerves in man – 31 pairs
1) Cervical nerves [C] – 8 pairs – neck region.
2) Thoracic nerves [T] – 12 pairs – thoracic region.
3) Lumbar nerves [L] – 5 pairs – upper part of abdomen.
4) Sacral nerves [S] – 5 pairs – lower part of abdomen.
5) Coccygeal nerves [CO] – 1 pair
- Spinal nerve formula can be written as C8,T12,L5,S5,CO1.
- The first pair of cervical spinal nerves emerge between the atlas and occipital bone of the
- All other spinal nerves emerge from the vertebral column through the intervertebral foramina
- The lumbar, the sacral and the caudal nerves extend back along with the filum terminale forming a thick bundle of nerves called cauda equina
- Certain spinal nerves are joined to form networks called plexuses
- 1. Cervical plexus( 1 st to 4 th cervical nerves)
2) Brachial plexus ( 5 th to 8 th cervical and I st thoracic nerves)
3) lumbar plexu s (1 st lumbar and a branch h of the 4 lumbar nerve)
4) Sacral plexus ( The 1 st three Sacral a branch of the 4 th lumbar and the 5 th lumbar nerves)
5) Coccygeal plexus (4 th and 5 th sacral and coccygeal nerve).
CRANIAL NERVES OF Man AT A GLANCE
S.No Name Nature Origin Distribution Function I Olfactory Nerves Sensory Olfactory lobe Sensory epithelium of olfactory sacs Receive stimuli from the sensory epithelium of olfactory sac and carry them to olfactory lobes II Optic nerves Sensory In retina of eye Lateral geniculate nuclei of thalamus Stimulus of light is carried to optic occipital lobe of cerebral cortex III Occulomotor nerves Motor Crura cerebri (mid brain) Eye ball muscles like superior rectus, medial rectus, inferior rectus and inferior oblique, except superior oblique muscle and external rectus Movement of eye lids and eye ball IV Trochlear nerves Motor From in between the optic lobes and cerebellum Superior oblique muscle of eye ball Movement of eye ball V Trigeminal nerves Mixed From the gassarion ganglia situated on the lateral side of pons – – (i) Ophithalimic Nerve Sensory “ Skin of lips, upper eye lid, lacrimal, gland (ii)Maxillary Sensory “ Upper lip, skin of nose, lower eye lid. Upper teeth Carry the stimuli from these organs to brain (III) Mandibular Nerve Mixed “ Lower lip and skin of Jaw Carry the stimuli from these organs to brain VI Abducens nerves Motor Pons Eye muscles external rectus Movement of eye ball VII Facial nerves Mixed Pons – – (i) Palatinus Sensory – In the roof of mouth cavity Carry the impulses from roof of mouth cavity (ii) Hyomandibular Motor – Muscles of lower jaw, muscles of neck and pinna (external ear) Carrythe impulses from brain muscles of lower jaws, neck and pinna (iii) Chordatympani Mixed – In salivary glands and taste buds Receives the stimuli from the taste buds and carry the stimulus to salivary gland VIII Auditory nerves Sensory Medulla, pons – – (i) Vestibular nerve Sensory – Semicircular canals, saccule, utricle. Receives impulses from the internal ear and carry to brain for equilibrium (ii) Cochlear nerve Sensory – Cochlea Impulses associate with hearing IX Glossopharyngeal nerve Mixed In medulla Taste buds present in tongue and muscles of oesophagus Secretion of saliva, taste muscle sense (proprioception) X Vagus nerve Mixed Arising from medulla, 9th and 10th cranial nerves unites to form vagus nerve but become separate and divide into branches – – (i) Superior laryngeal nerve Motor – Glottis, trachea, lung muscle (1) Smooth muscles contraction and relaxation
(2) Secretion of digestive juice
(3) Muscle sense (proprioception)
(4) Sensation of visceral Organs
(ii) Recurrent Laryngeal nerve Motor – Glottis, trachea, lung muscle (iii) Cardiac nerve Motor Heart Muscles From brain to heart muscles (iv)Pneumogastric Motor – In the abdominal cavity, in stomach and lungs Carry impulse from these organs to brain and from brain to muscles of these organs (v) Depressor nerve Motor – Diaphragm Carry the impulse to diaphragm XI Spinal accessory Motor Medulla Muscles of neck and shoulders, voluntary muscles of pharynx, larynx, and soft palate Swallowing movements, movement of head XII Hypoglossal nerve Motor Medulla Muscles of tongue and neck Movement of tongue during speech, and swallowing, proprioception (Muscle sense)
Functionally, the PNS is divided into two divisions called Somatic and Autonomic neural systems.
Somatic neural system (SNS)
- SNS includes both sensory and motor neurons.
- From somatic receptors the sensory neurons conduct sensory impulses to CNS.
- These sensations are consciously perceived.
- The motor neurons of SNS have single myelinated axons and they innervate the skeletal muscles and produce voluntary movements.
- The result is always excitation
6. AUTONOMIC NERVOUS SYSTEM
- ANS controls and coordinates the activities of visceral organs by controlling visceral sensation and visceral movements.
- Autonomic nervous system usually operates without conscious control.
- Autonomic nervous system is entirely motor.
- All autonomic axons are efferent fibres.
- The centres in brain like cerebral cortex, hypothalamus and medulla oblongata regulates the autonomic nervous system.
- ANS consists of two divisions
a) sympathetic division
b) para sympathetic division
- The preganglionic neurons arise from the thoracic and lumbar regions of the spinal cord, hence it is called as thoracolumbar
- It consists of two sympathetic chains one on either side of the dorsal aorta and beneath the vertebral column on either side of dorsal aorta extending from base of the skull to the pelvis of the body.
- The axons of sympathetic preganglionic neurons are called thoracolumbar outflow.
- In sympathetic division preganglionic nerves are small where as post ganglionic nerves are long.
- Each sympathetic chains bears a series of trunk ganglia/chain ganglia Besides trunk ganglia,
- three collateral ganglia lie outside sympathetic chain close to large abdominal arteries below diaphragm
- Collateral ganglia are coeliac, superior mesentric and inferior mesenteric ganglia.
- Preganglionic nerve secretes acetyl choline and postganglionic nerve secretes sympathin (nor-epinephrine)
- It is called as cranio-sacral division.
- The parasympathetic neural system is said to exhibit ‘Cranio-sacral outflow’
- Cranial nerve fibres of III,VII,IX and X along with 2,3,4 sacral nerve fibres form parasympathetic nervous system.
- Parasympathetic ganglia lie in head, neck and sacral region.
- Preganglionic nerve is long whereas postganglionic nerve is small.
- Parasympathetic ganglia are located close to or within the wall of visceral organs. Hence they are called terminal ganglia.
- These nerve fibres secrete acetyl choline only.
- The sympathetic and parasympathetic nervous systems are antagonistic in their action.
- Sympathetic nerve fibres are adrenergic while parasympathetic nerve fibres are cholinergic in their mode of action.
7. COMPARISON OF SOMATIC AND AUTONOMIC
Nervous Systems Somatic Nervous System Autonomic Nervous System Sensory input From somatic senses and special senses Mainly from interoceptors Control of motor output Voluntary Involuntary Motor neuron pathway One-neuron pathway Two neuron pathway Neurotran- smitter Ach Ach and NE Effectors Skeletal muscles Smooth muscles, cardiac muscles and glands
8. FUNCTIONS OF SYMPATHETIC AND PARASYMPATHETIC NERVOUS SYSTEM
Cognitive Neural Systems Lab
In the Cognitive Neural Systems Lab we study the perceptual and cognitive brain mechanisms that allow us to interact effectively with our environment. We primarily use a combination of innovative paradigm designs, neurophysiology, and computational modelling.
Dec 2020: Simon’s paper “Neurocomputational mechanisms of prior-informed perceptual decision-making in humans” has just been published in Nature Human Behaviour: https://rdcu.be/ccbWx
Oct 2020: Kieran’s paper “Modulation of the earliest component of the human VEP by spatial attention: an investigation of task demands” has just been published in Cerebral Cortex Communications!
Sep 2020: the lab welcomes M.E. students Emma Bailey, Meg Brennan, Sean Hasset and Aideen Caulfield, who will complete their final year projects with us
Apr 2020: We’ve been awarded a Tripartite R01 grant jointly funded by the US National Institute of Mental Health, Science Foundation Ireland, and HSC Northern Ireland, to investigate the neural architecture of decisions abstracted from movements, in collaboration with Redmond O’Connell, KongFatt Wong-Lin, Stephan Bickel and Mike Shadlen.
Feb 2020: Simon has been awarded a Wellcome Trust Investigator Award in Science. We’ll be hiring soon.
Oct 2019: Catch 3 posters from our group at this year’s Society for Neuroscience Meeting: 1st authors John Egan, Elaine Corbett and Stephan Bickel
Sep 2019: The lab welcomes new postdoc Anna Geuzebroek
Sep 2019: The lab welcomes M.E. students David Gaynor, Rose Lynch, Rachel DeKeyser and Sophie Perry, who will complete their final year thesis projects with us
Jul 2019: Isabel’s paper, “Altered dynamics of visual contextual interactions in Parkinson’s disease” is out in NPJ Parkinson’s Disease
Feb 2019: Huge congratulations to Elaine Corbett for landing a Marie Sklodowska-Curie Individual Fellowship!
Dec 2018: Dave McGovern’s paper, “Reconciling age-related changes in behavioural and neural indices of human perceptual decision making” is out in Nature Human Behaviour
Nov 2018: Catch four posters from four PhD students in the lab at this year’s Society for Neuroscience Meeting: Kivi Afacan, Kieran Mohr, John Egan and Alexandra Martinez-Rodriguez
Nov 2018: Our new review paper with Mike Shadlen and KongFatt Wong-Lin, “Bridging Neural and Computational Viewpoints on Perceptual Decision-Making,” is just out in Trends in Neurosciences
Sep 2018: The lab welcomes Louisa Spence as a research assistant
Sep 2018: The lab welcomes M.E. students Ludovic Piret, Sai Bhanu Prasad Lingala and Niall McGovern, and B.E. student Rachel Georgel who will complete their final year thesis projects with us
Aug 2018: Natalie’s paper, “Decisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy” is just out in Nature Communications
Feb 2018: Kivi’s paper, “Dynamic interplay of value and sensory information in high-speed decision making” is just out in Current Biology.
Jan 2018: Ger’s paper, “Antagonistic interactions between microsaccades and evidence accumulation processes during decision formation” is just out in Journal of Neuroscience.
Nov 2017: Annabelle’s paper “Neural signature of value-guided sensorimotor prioritization in humans” just came out in Journal of Neuroscience.
Nov 2017: Catch two posters from the lab at Society for Neuroscience – first authors Hannah Craddock and Kivilcim Afacan-Seref.
Nov 2017: The lab welcomes Ger Loughnane as a postdoc!
Sep 2017: Welcome Alexandra Martinez-Rodriguez, new PhD student!
Sep 2017: The lab welcomes M.E. students Niamh Carr, Arjun Mukundan, Louise O’Connor, and Megan O’Brien, who will complete their thesis projects with us.
Aug 2017: Congratulations to Isabel Vanegas for successfully defending her PhD thesis “Novel Stimulation Paradigms to Study Visual Response Modulations in Health and Disease,” and thanks to her committee, Lucas Parra, Marom Bikson, Jonathan Levitt and Horacio Kaufmann.
Jul 2017: The lab welcomes Elaine Corbett as our new Postdoc!
Apr 2017: Nicolas’s paper, “A Resource for Assessing Information Processing in the Developing Brain Using EEG and Eye Tracking” has just come out in Scientific Data.
Apr 2017: Simon’s last postdoc paper, finally out! “Parietal neurons encode expected gains in instrumental information” in PNAS.
Nov 2016: Congratulations to Natalie Steinemann for successfully defending her PhD thesis “Perceptual decision making in humans: Neural correlates along the sensorimotor hierarchy,” and thanks to her committee: Lucas Parra, Marom Bikson, Jacek Dmochowski and Mike Shadlen.
Oct 2016: New paper by Gebodh, Vanegas and Kelly accepted for publication in Brain Topography: “Effects of stimulus size and contrast on the initial primary visual cortical response in humans”
Sep 2016: John Egan and Kieran Mohr start their PhDs with us – welcome!
Sep 2016: The lab welcomes M.E. students Daire Corley-Carmody, Hannah Craddock, Ronan McCormack, and Killian McManus who will conduct their final-year research projects with us for the year.
Aug 2016: Simon has been awarded a Career Development Award from Science Foundation Ireland for research on the integration of reward information into perceptual decision making.
Jul 2016: Kieran Mohr has just been awarded an IRC PhD fellowship to join the lab
Jul 2016: New paper published at The Journal of Neuroscience, “Abstract and Effector-Selective Decision Signals Exhibit Qualitatively Distinct Dynamics before Delayed Perceptual Reports”
May 2016: UCD M.E. students Lisa Sherin and Eoin Brady, and B.E. student Gavin Foran successfully defend their final project theses for research conducted in the lab
May 2016: Natalie Steinemann has just published the first paper from her PhD thesis, “Tracking neural correlates of successful learning over repeated sequence observations” in the journal NeuroImage.
Jan 2016: New paper accepted at Current Biology, Loughnane et al, “Target selection signals influence perceptual decisions by modulating the onset and rate of evidence accumulation”
Jan 2016: Postdoc Nicolas Langer has just started his own gig in University of Zurich, as an Assistant Professor
Dec 2015: Congratulations to CCNY student Nigel Gebodh for successfully defending his Masters thesis, “Characterization of the effects of stimulus size and contrast on the initial afferent response in human primary visual cortex.”
Feb 2015: Isabel Vanegas has just published her second paper in the lab, “Electrophysiological indices of surround suppression in humans,” in Journal of Neurophysiology.
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J. P. Newman, M. Fong, D. C. Millard, C. J. Whitmire, G. B. Stanley, S. M. Potter, Optogenetic feedback control of neuronal firing, eLife, 2015. PDF
H. J. V. Zheng, Q. Wang, and G. B. Stanley, Adaptive Shaping of Cortical Response Selectivity in the Vibrissa Pathway, J Neurophysiol, 2015
A. Srinivasan, M. Tahilramani, J. T. Bentley, R. K. Gore, D. C. Millard, V. J. Mukhatyar, A. Joseph, A. S. Haque, G. B. Stanley, A. W. English, R. V. Bellamkonda, Microchannel-based regenerative scaffold for chronic peripheral nerve interfacing in amputees, Biomaterials 41, 151-165, 2015 PDF
C. J. Shephard, G. B. Stanley, The needle in the haystack, Nature neuroscience 17 (6), 752-753, 2014 PDF
D. R. Ollerenshaw, H. J. V. Zheng, Q. Wang, and G. B. Stanley, The adaptive trade-off between detection and discrimination in cortical representations and behavior, Neuron ., Mar 581(5):1152-64, 2014.PDF
D. C. Millard, Q. Wang, C. A. Gollnick, and G. B. Stanley. System identification of the nonlinear dynamics in the thalamocortical circuit in response to patterned thalamic microstimulation in-vivo, J Neural Eng ., Dec10(6):066011, 2013.PDF
S. T. Kelly, J. Kremkow, J. Jin, Y. Wang, Q. Wang, J. M. Alonso, G. B. Stanley. The Role of Thalamic Population Synchrony In the Emergence of Cortical Feature Selectivity, PLoS Comput Biol ., Jan10(1):e1003418, 2014.PDF
G. B. Stanley, Reading and writing the Neural Code, Nature Neurosci., 16(3):259-263, 2013. PDF
L. Karumbaiah, T. Saxena, D. Carlson, K. Patil, R. Patkar, E. A. Gaupp, M. Betancur, G. B. Stanley, L. Carin, R. V. Bellamkonda, Relationship between intracortical electrode design and chronic recording function, Biomaterials, Nov34(33):8061-74, 2013.PDF
T. Saxena, L. Karumbaiah, E. A. Gaupp, R. Patkar, K. Patil, M. Betancur, G. B. Stanley, R. Bellamkonda, The impact of chronic blood-brain barrier breach on intracortical electrode function, Biomaterials, 34(20), 4703-13, 2013.PDF
J. Wulff, D. J. Butler, G. B. Stanley, M. J. Black, Lessons and insights from creating a synthetic optical flow benchmark, In European Conf. on Computer Vision (ECCV), Springer-Verlag,Part II, LNCS 7577, pages 168-177, October 2012.PDF
D. J. Butler, J. Wulff, G. B. Stanley and M. J. Black, A naturalistic open source movie for optical flow evaluation, In European Conf. on Computer Vision (ECCV), Springer-Verlag, Part IV, LNCS 7577, pages 611-625, October 2012. PDF
D. R. Ollerenshaw, B. A. Bari, D. C. Millard, L. E. Orr, Q. Wang, and G. B. Stanley, Detection of tactile inputs in the rat vibrissa pathway, J. Neurophysiol., 108, 479-490, 2012. PDF
Q. Wang, D. C. Millard, H. J. V. Zheng, G. B. Stanley. Voltage sensitive dye imaging reveals improved topographic activation of cortex in response to manipulation of thalamic microstimulation paramters, J. Neural Engineering, 9, 026008, 2012. PDF
G. B. Stanley, J. Z. Jin, Y. Wang, G. Desbordes, M. J. Black, J. M. Alonso. Visual orientation and direction selectivity through thalamic synchrony, J. Neurosci., 32, 9073-9088, 2012. PDF
M. Ding, Q. Wang, E. H. Lo, and G. B. Stanley. Cortical excitation and inhibition following focal traumatic brain injury, J. Neurosci., 31(40):14085-14094, 2011. PDF
G. Desbordes, J. Z. Jin, J. M. Alonso, G. B. Stanley. Modulation of temporal precision in thalamic population responses to natural visual stimuli, Frontiers in Systems Neuroscience, 4:151, 2010. PDF
Q. Wang, R. M. Webber, and G. B. Stanley. Thalamic synchrony and the adaptive gating of information flow to cortex, Nature Neuroscience, 13(12):1534-1541, 2010. PDF, Supplement
D. A. Butts, G. Desbordes, C. Weng, J. Z. Jin, J. M. Alonso, and G. B. Stanley. The episodic nature of spike trains in the early visual pathway, J. Neurophysiol., 104:3371-3387, 2010. PDF
A. S. Boloori, R. A. Jenks, Gaelle Desbordes, and G. B. Stanley. Encoding and decoding cortical representations of tactile features in the vibrissa system, J. Neurosci., 30(30):9990-10005, 2010. PDF
R. A. Jenks, A. Vaziri, A. S. Boloori, and G. B. Stanley. Self-motion and the shaping of sensory signals, J. Neurophysiol., 103: 2195-2207, 2010. PDF
G. B. Stanley. It’s not you, it’s me. Really, Nature Neuroscience News & Views, 12(4), 374-375, 2009. PDF
G. Desbordes, C. Weng, J. Jin, N. A. Lesica, G. B. Stanley, and J. M. Alonso. Timing precision in population coding of natural scenes in the early visual system, PLOS Biology, 6(12), 2672-2682, 2008. PDF, Supplement
G. B. Stanley. Au naturel, Neuron Preview, 58:467-468, 2008. PDF
N. A. Lesica, T. Ishii, G. B. Stanley, and T. Hosoya. Estimating receptive fields from responses to natural stimuli with asymmetric intensity distributions, PLOS One, 3(8), e3060, 2008. PDF
M. Ding, E. H. Lo, and G. B. Stanley. Sustained focal cortical compression reduces electrically-induced seizure threshold, Neuroscience, 154:551-555, 2008. PDF
D. A. Butts, C. Weng, J. Z. Jin, C. I. Yeh, N. A. Lesica, J. M. Alonso, and G. B. Stanley. Temporal precision in the neural code and the time scales of natural vision, Nature, 449:92-95, 2007. PDF, Supplement
C. W. Clifford, M. A. Webster, G. B. Stanley, A. A. Stocker, A. Kohn, T. O. Sharpee, and O. Schwartz. Visual adaptation: neural, psychological and computational aspects, Vision Research, 47:3125-3131, 2007. PDF
A. Vaziri, R. A. Jenks, A. S. Boloori, and G. B. Stanley. Flexible probes for surface topology: from biology to technology, Experimental Mechanics, 47: 417-425, 2007. PDF
N. A. Lesica, J. Z. Jin, C. Weng, C. I. Yeh, D. A. Butts, G. B. Stanley, and J. M. Alonso. Adaptation to stimulus contrast and correlations during natural visual stimulation, Neuron, 55:479-491, 2007. PDF
L. M. Frank, E. N. Brown, and G. B. Stanley. Hippocampal and cortical place cell plasticity: implications for episodic memory, Hippocampus, 16:775-784, 2006. PDF
A. S. Boloori and G. B. Stanley. The dynamics of spatiotemporal response integration in vibrissa representation in the primary somatosensory cortex, J. Neurosci., 26:3767-3782, 2006. PDF
R. M. Webber and G. B. Stanley. Transient and steady-state dynamics of cortical adaptation, J. Neurophys., 95:2923-2932, 2006. PDF
N. A. Lesica, C. Weng, J. Jin, C. Yeh, J. M. Alonso, and G. B. Stanley. Dynamic encoding of natural luminance patterns by LGN bursts, PLOS Biology, 4(7), e209, 2006. PDF
N. A. Lesica and G. B. Stanley. Decoupling functional mechanisms of adaptive encoding in the visual pathway, Network: Computation in Neural Systems, 17:43-60, 2006. PDF
N. A. Lesica and G. B. Stanley. An LGN inspired detect/transmit framework for high fidelity relay of visual information with limited bandwidth, 1st International Symposium on Brain, Vision, and Artificial Intelligence, Naples, 2005. PDF
N. A. Lesica and G. B. Stanley. Functional identification of adaptive visual encoding, Neural Engineering/Signal Processing, edited by Metin Akay, IEEE/Wiley, 2005.
G. B. Stanley. Neural System Identification, in Neural Engineering, edited by Bin He, Springer, 2005. PDF
N. A. Lesica and G. B. Stanley. Improved tracking of time-varying encoding properties of visual neurons by extended recursive least-squares, IEEE Trans. Neural Sys. Rehab., 2005. PDF
N. A. Lesica and G. B. Stanley. Encoding of natural scene movies by tonic and burst spikes in the lateral geniculate nucleus, J. Neurosci., 24:10731-10740, 2004. PDF
L. M. Frank, G. B. Stanley, and E. N. Brown. Hippocampal plasticity across multiple days of exposure to novel environments, J. Neurosci., 24: 7681-7689, 2004. PDF
R. M. Webber and G. B. Stanley. Nonlinear encoding of tactile patterns in the barrel cortex, 91: 2010-2022, J. Neurophys., 2004. PDF
E. A. Goldstein, J. T. Heaton, J. B. Kobler, G. B. Stanley, and R. E. Hillman. Design and implementation of a hands-free electrolarynx device controlled by neck strap muscle electromyographic activity, IEEE Trans. Biomed. Eng., 51:325-332, 2004. PDF
N. A. Lesica, A. S. Boloori, and G. B. Stanley. Adaptive encoding in the visual pathway, Network: Comput. in Neural Systems, 14:119-135, 2003. PDF
G. B. Stanley and R. W. Webber. A point process analysis of sensory encoding, J. Comput. Neurosci., 15:321-333, 2003.PDF
G. B. Stanley. Adaptive spatiotemporal receptive field estimation in the visual pathway, Neural Computation, 14:2925-2946, 2002. PDF
B. Lau, G. B. Stanley, and Y. Dan. Computational subunits of visual cortical neurons revealed by artificial neural networks, PNAS, 99(13):8974-8979, 2002. PDF
G. B. Stanley, K. Poolla, and R. A. Siegel. Threshold modeling of autonomic control of heart rate variability, IEEE Trans. Biomed. Eng., 49(9):1147-1153, 2000. PDF
G. B. Stanley, F. F. Li, and Y. Dan. Reconstruction of natural scenes from ensemble responses in the LGN, J. Neurosci., 19(18):8036-8042, 1999. PDF
G. Stanley, D. Verotta, N. Craft, R. A. Siegel, and J. B. Schwartz. Age effects on interrelationships between lung volume and heart rate during standing, Amer. J. Physiol., 42:H2128-H2134, 1997. PDF
G. Stanley, D. Verotta, N. Craft, R. A. Siegel, and J. B. Schwartz. Age and autonomic effects on interrelationships between lung volume and heart rate, Amer. J. Physiol., 39:H1833-H1840, 1996.
Conference Abstracts & Proceedings
R. A. Jenks, A. Vaziri, A. S. Boloori, and G. B. Stanley. Representation of tactile features by the vibrissa system in awake behaving rats, SFN, Atlanta, 2006. PDF
A. S. Boloori, D. A. Butts, R. A. Jenks, and G. B. Stanley. Coding of tactile features by layer 4 of the rat S1 cortex, SFN, Atlanta, 2006. PDF
R. M. Webber and G. B. Stanley. Adaptation dynamically alters cortical sensitivity to thalamic input precision, SFN, Atlanta, 2006. PDF
D. A. Butts, C. Weng, J. Z. Jin, C. I. Yeh, N. A. Lesica, J. A. Alonso, and G. B. Stanley. The role of temporal precision in the neural code and its relationship to the time scales of natural stimuli, SFN, Atlanta, 2006. PDF
A. S. Boloori and G. B. Stanley. How does joint activity across a population of neurons influence the precision of coding in the cortex?, AREADNE, Santorini, Greece, 2006. PDF
D. A. Butts, C. Weng, J. Z. Jin, C. I. Yeh, N. A. Lesica, J. M. Alonso, G. B. Stanley. Contrast adaptation and the role of temporal precision in the visual code, COSYNE, Salt Lake City Utah, 2006.
R. M. Webber and G. B. Stanley. Nonlinear cortical responses can be generated by precise timing of thalamic spikes in the rat vibrissa system, COSYNE, Salt Lake City Utah, 2006. PDF
D. A. Butts, C. Weng, J. Z. Jin, C. I. Yeh, N. A. Lesica, J. A. Alonso, and G. B. Stanley. Changes in functional properties of LGN neurons with contrast and their significance in information transmission, SFN, Washington DC, 2005. PDF
N. A. Lesica, J. Z. Jin, C. Weng, C. I. Yeh, D. A. Butts, G. B. Stanley, and J. A. Alonso. The effects of contrast adaptation during natural stimulation, SFN, Washington DC, 2005. PDF
M. C. Ding, E. Tejima, E. H. Lo, and G. B. Stanley, Electrophysiology of the rat barrel cortex following traumatic brain injury, SFN, Washington DC, 2005. PDF
R. A. Jenks and G. B. Stanley. Stimulus-driven response dynamics of the whisker/barrel-cortex system in awake behaving rats, SFN, Washington DC, 2005. PDF
A. Boloori and G. B. Stanley. Spatiotemporal interactions influence precision of coding by single- and multiple neurons in the rat barrel cortex, SFN, Washington DC, 2005. PDF
J. R. Chen, L. M. Frank, E. N. Brown, G. B. Stanley. Theta waves in the hippcampus and the deep layer of the entorhinal cortex become more phase-locked with increasing locomotive speed, SFN, Washington DC, 2005. PDF
G. B. Stanley, A. Boloori, R. A. Jenks, R. M. Webber. A nonlinear dynamical model of excitatory-inhibitory interactions in the vibrissa system, SFN, Washington DC, 2005. PDF
R. M. Webber and G. B. Stanley. Thalamocortical transformations of temporal stimulation patterns in the rat vibrissa system, SFN, Washington DC, 2005. PDF
N. A. Lesica and G. B. Stanley. Signal detection of salient visual features by the early visual pathway, Annual Meeting of the BMES, Baltimore, 2005.
R. M. Webber and G. B. Stanley. Thalamocortical transformations of tactile patterns in the rat vibrissa system, Annual Meeting of the BMES, Baltimore, 2005.
J. R. Chen, L. M. Frank, E. N. Brown, and G. B. Stanley. Theta in the entorhinal-hippocampus axis becomes more phase-locked as locomotive speed increases, Annual Meeting of the BMES, Baltimore, 2005.
L. M. Frank, G. B. Stanley, and E. N. Brown. Hippocampal Plasticity across Multiple Days of Exposure to Novel Environments, Annual Meeting of the BMES, Baltimore, 2005.
N. A. Lesica and G. B. Stanley. Signal detection of salient visual features by the early visual pathway, Annual IEEE EMBS Meeting, Shanghai, 2005. PDF
N. A. Lesica and G. B. Stanley. An LGN inspired detect/transmit framework for high fidelity relay of visual informatoin with limited bandwidth, 1st International Symposium on Brain, Vision, and Artificial Intelligence, Naples, 2005. PDF
R. M. Webber and G. B. Stanley. Post-excitatory suppression dictates the dynamics of adaptation in barrel cortex, COSYNE, Salt Lake City, 2005. PDF
D. A. Butts and G. B. Stanley. The important aspects of visual encoding: relating receptive fields to mutual information rates in visual neurons, COSYNE, Salt Lake City, 2005. PDF
N. A. Lesica and G. B. Stanley. LGN bursts enhance detection of specific stimulus features, COSYNE, Salt Lake City, 2005.PDF
R. M. Webber and G. B. Stanley. Transient adaptation properties in rat barrel cortex are affected by the initial direction of vibrissa deflection, SFN, San Diego, 2004. HTML
N. A. Lesica and G. B. Stanley. The role of LGN bursts in natural vision, SFN, San Diego, 2004. HTML
A. S. Boloori and G. B. Stanley. Nonlinear center-surround interactions in the barrel cortex, SFN, San Diego, 2004. HTML
G. B. Stanley, R. M. Webber, and A. S. Boloori. Nonlinear spatiotemporal encoding in the rat vibrissa system, Barrel Cortex Meeting, EPFL, Lausanne, Switzerland, 2004. HTML
N. A. Lesica and G. B. Stanley. The role of LGN bursts in natural vision, COSYNE, Cold Spring Harbor Laboratories, 2004.HTML
A. S. Boloori and G. B. Stanley. Nonlinear center-surround interactions in the barrel cortex, COSYNE, Cold Spring Harbor Laboratories, 2004. POSTSCRIPT
D. A. Butts, A. E. Desjardins, and G. B. Stanley. Quick and accurate information calculations based on linear characterizations of sensory neurons, SFN, New Orleans, 2003. HTML
N. A. Lesica and G. B. Stanley. Adaptive interactions in the visual pathway, SFN, New Orleans, 2003. HTML
R. A. Jenks, T. Warren, L. M. Frank, and G. B. Stanley. Texture detection and discrimination by the whisker/barrel-cortex system in awake behaving rats, SFN, New Orleans, 2003. HTML
R. M. Webber and G. B. Stanley. Prediction of responses to periodic and aperiodic stimuli in the rat barrel cortex, SFN, New Orleans, 2003. HTML
D. A. Butts, A. E. Desjardins, and G. B. Stanley. Model based information calculations for neuronal encoding, Computational Neuroscience (CNS), Alicante, Spain, 2003.
E. A. Goldstein, J. T. Heaton, J. B. Kobler, G. B. Stanley, and R. E. Hillman. Design and implementation of a hands-free electrolarynx device controlled by neck strap muscle electromyographic activity, 1st Annual IEEE-EMBS Conference on Neural Engineering, Capri Island, 2002.
G. B. Stanley and R. M. Webber. A point process analysis of sensory processing, 1st Annual IEEE-EMBS Conference on Neural Engineering, Capri Island, 2002.
R. M. Webber and G. B. Stanley. Temporal dynamics in somatosensory cortex, 32nd annual meeting of the Society for Neuroscience, Orlando, 2002.
N. A. Lesica and G. B. Stanley. Estimation of adaptive encoding in the visual pathway, IEEE-EMBS, Houston, 2002.
A. S. Boloori and G. B. Stanley. Stimulus reconstruction from nonlinear neuronal encoding, CNS, Chicago, 2002.
G. B. Stanley. Adaptive estimation of time-varying dynamics in the visual pathway, NIPS Workshop on Information and Statistical Structure in Spike Trains, 2001.
G. B. Stanley and A. SeyedBoloori. Decoding in neural systems: stimulus reconstruction from nonlinear encoding, Engineering in Medicine and Biology Society, Istanbul, 2001.
G. B. Stanley. Wiener kernel estimation for neural systems with natural inputs, Proceedings of the 10th Annual Computational Neuroscience Meeting, Monterey, 2001.
A. SeyedBoloori and G. B. Stanley. Stimulus reconstruction as the inverse of the neural encoding process: a Wiener series approach, 5th International Conference on Cognitive and Neural Systems, Boston, 2001.
N. Lesica and G. B. Stanley. A computational analysis of Wiener kernel estimation in neural systems, 5th International Conference on Cognitive and Neural Systems, Boston, 2001.
G. B. Stanley, Adaptive Estimation of Time-varying Dynamics in the Visual Pathway, NIPS Workshop on Information and Statistical Structure in Spike Trains, 2001.
G. B. Stanley. Time-varying properties of neurons in visual cortex: estimation and prediction, 30th annual meeting of the Society for Neuroscience, New Orleans, 2000.
G. B. Stanley, Algorithms for real-time prediction in neural systems, Engineering in Medicine and Biology Society, Chicago, 2000.
G. B. Stanley. Recursive stimulus reconstruction algorithms for real-time implementation in neural ensembles, Annual Computational Neuroscience Meeting, Brugge, Belgium, 2000.
G. B. Stanley, F.F. Li, and Y. Dan. Predicting Neuronal Responses in the Striate Cortex with Neural Networks, 29th annual meeting of the Society for Neuroscience, Miami, 1999.
G. Stanley, F.F. Li, and Y. Dan. Reconstruction of natural scenes from ensemble responses in the LGN, 28th annual meeting of the Society for Neuroscience, Los Angeles, 1998.
G. Stanley, K. Poolla, and R.A. Siegel. Threshold modeling of autonomic control of heart rate variability, Biomed. Eng. Society, San Diego, 1997.
G. Stanley and R. Siegel. Threshold modeling of respiratory sinus arrhythmia, American Association of Pharmaceutical Scientists Western Regional, San Francisco, 1996.
G. Stanley, D. Verotta, N. Craft, R. Siegel, and J. Schwartz. Aging and/or autonomic blockade attenuation of lung
volume to heart rate transfer functions, Exp. Biology, Atlanta, 1996.
Yang Y., Qiao S., Sani O. G., Sedillo I. J., Ferrentino B., Pesaran B., Shanechi M. M., “Modelling and prediction of the dynamic responses of large-scale brain networks during direct electrical stimulation”. Nature Biomedical Engineering, Feb. 2021. (link) (USC Viterbi story) (Nature Behind the Paper Story) (Nature News and Views article and editorial article) (Selected as journal cover article)
Abbaspourazad H., Choudhary M., Wong Y.T., Pesaran B., Shanechi M. M., “Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior”. Nature Communications, Jan. 2021. (link) (story)
Sani O. G., Abbaspourazad H., Wong Y.T., Pesaran B., Shanechi M. M., “Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification”, Nature Neuroscience, Nov. 2020 (link) (story)
Yang Y., Ahmadipour P., Shanechi M. M., “Adaptive latent state modeling of brain network dynamics with real-time learning rate optimization” Journal of Neural Engineering, Nov. 2020. (link)
Ahmadipour P., Yang Y., Chang E. F., Shanechi M. M., “Adaptive tracking of human ECoG network dynamics”. Journal of Neural Engineering, Aug. 2020 (link)
Abbaspourazad H., Choudhary M., Wong Y. T., Pesaran B., Shanechi M. M., “Multiscale low-dimensional neural dynamics explain naturalistic 3D movements”, Computational and Systems Neuroscience (COSYNE), Feb. 27–Mar. 1, 2020, Denver, CO. (link)
Sani O., Pesaran B., Shanechi M. M., “Modeling behaviorally relevant neural dynamics with a novel preferential subspace identification (PSID)”, Computational and Systems Neuroscience (COSYNE), Feb. 27–Mar. 1, 2020, Denver, CO. (link)
Yang Y., Qiao S., Sani O., Sedillo I., Ferrentino B., Pesaran B., Shanechi M. M., “Modeling large-scale brain network dynamics in response to electrical stimulation”, Computational and Systems Neuroscience (COSYNE), Feb. 27–Mar. 1, 2020, Denver, CO. (link)
Sani, O. G., Pesaran B., Shanechi, M. M., “Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification (PSID)”, bioRxiv, Oct. 2019 (link)
Shanechi M. M., “Brain-machine interfaces from motor to mood”, Nature Neuroscience, Sep. 2019 (link) (Focus on Learning and Memory Collection)
Sadras N., Pesaran B., Shanechi M. M., “A point-process matched filter for event detection and decoding from population spike trains”, Journal of Neural Engineering, Aug. 2019 (link)
Yang Y., Sani O. G., Chang E. F., Shanechi M. M., “Dynamic network modeling and dimensionality reduction for human ECoG activity” Journal of Neural Engineering, May 2019 (link).
Bighamian R., Wong Y., Pesaran B., Shanechi M. M., “Sparse model-based estimation of functional dependence in high-dimensional field and spike multiscale networks”, Journal of Neural Engineering, May 2019 (link).
Abbaspourazad H., Hsieh H., Shanechi M. M., “A Multiscale Dynamical Modeling and Identification Framework for Spike-Field Activity”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Apr. 2019 (link)
Wang C., Shanechi M. M., “Estimating Multiscale Direct Causality Graphs in Neural Spike-Field Networks”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Mar. 2019 (link)
Yang Y., Guidera J., Vlasov K., Pei J., Brown E.N., Solt K., Shanechi M. M., “Developing a personalized closed-loop controller of medically-induced coma in a rodent model” Journal of Neural Engineering, Mar. 2019 (link)
Song C., Hsieh H., Shanechi M. M., “Decoder for switching state-space models with spike-field observations”, International IEEE EMBS Conference On Neural Engineering (NER), 20–23 Mar. 2019, San Francisco, CA. (link)
Ahmadipouranari P., Yang Y., Shanechi M. M., “Investigating the effect of forgetting factor on tracking non-stationary neural dynamics”, International IEEE EMBS Conference On Neural Engineering (NER), 20–23 Mar. 2019, San Francisco, CA. (link)
Rao V.R., Sellers K.K., Wallace D.L., Lee M.B, Bijanzadeh M, Sani O.G., Yang Y, Shanechi M.M., Dawes H.E., Chang E.F., “Direct Electrical Stimulation of Lateral Orbitofrontal Cortex Acutely Improves Mood in Individuals with Symptoms of Depression”, Current Biology, Nov. 2018 (link).
Hsieh H, Wong Y, Pesaran B, Shanechi M.M., “Multiscale Modeling and Decoding Algorithms for Spike-Field Activity”, Journal of Neural Engineering, Oct. 2018 (link)
Sani O. G., Yang Y., Lee M. B., Dawes H. E., Chang E.F., Shanechi M. M., “Mood variations decoded from multi-site intracranial human brain activity”, Nature Biotechnology, Sep. 2018 (link) (USC News story) (“Behind the paper” story in Nature Communities) (Excerpts from Select Media Coverage: NewScientist, The Wall Street Journal, New Atlas) (Selected as journal cover article)
Yang Y., Connolly A. T., Shanechi M. M., “A control-theoretic system identification framework and a real-time closed-loop clinical simulation testbed for electrical brain stimulation.” Journal of Neural Engineering 15:066007, Sep. 2018 (link) Media Coverage: Science News
Hsieh H., Shanechi M. M., “Optimizing the Learning Rate for Adaptive Estimation of Neural Encoding Models”, PLoS Computational Biology 14(5): e1006168, May 2018 (link)
Abbaspourazad H., Wong Y., Pesaran B., Shanechi M. M., “Identifying Multiscale Hidden States to Decode Behavior”, in Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC) Conference, Honolulu, HI, 2018.
Bighamian R., Shanechi M. M., “Estimation of Functional Dependence in High-Dimensional Spike-Field Activity”, in Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC) Conference, Honolulu, HI, 2018.
Sadras, N., Shanechi M. M., “Decoding Spike Trains from Neurons with Spatio-Temporal Receptive Fields”, in Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC) Conference, Honolulu, HI, 2018.
Wang C., Shanechi M.M., “An Information-Theoretic Measure of Multiscale Causality for Spike-Field Activity”, in Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC) Conference, Honolulu, HI, 2018.
Hsieh, H., Wong Y.T., Pesaran B., Shanechi M. M., “Multiscale modeling and decoding of spike-field activity during a naturalistic reach-to-grasp task”, Computational and Systems Neuroscience (Cosyne), 1–4 Mar. 2018, Denver, CO.
Yang Y., Sani O., Sellers K.K., Chang E. F., Shanechi M. M., “A novel framework for dynamic modeling of brain-network response to electrical stimulation”, Computational and Systems Neuroscience (Cosyne), 1–4 Mar. 2018, Denver, CO.
Sani O., Yang Y., Lee M., Dawes H., Chang E. F., Shanechi M. M., “Decoding mood state from multisite ECoG activity in human subjects”, Computational and Systems Neuroscience (Cosyne), 1–4 Mar. 2018, Denver, CO.
Shanechi M. M., “Brain-machine interfaces”, Dynamic Neuroscience, Ed. S. Sarma, Ed. Z. Chen, Springer International Publishing.
Shanechi M.M., Orsborn A.L., Moorman H., Gowda S., Dangi S, Carmena J.M., “Rapid control and feedback rates enhance neuroprosthetic control”, Nature Communications, 8:13825, Jan. 2017 (link)
Abbaspourazad H., Shanechi M. M., “Multiscale modeling of dependencies between spikes and fields”, in Proceedings of Asilomar Conference on Signals, Systems, and Computers, 29–31 Oct. 2017, Pacific Grove, CA. (link)
Yang Y., Chang E.F., Shanechi M.M., “Dynamic tracking of non-stationarity in human ECoG activity”, in Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC) Conference, Jeju Island, Korea, 2017 (link)
Hsieh H., Wong Y.T., Pesaran B., Shanechi M.M., “Multiscale decoding for reliable brain-machine interface performance over time”, in Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC) Conference, Jeju Island, Korea, 2017 (link)
Abbaspourazad H., Shanechi M.M., “An unsupervised learning algorithm for multiscale neural activity”, in Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC) Conference, Jeju Island, Korea, 2017 (link)
Hsieh, H., Wong Y.T., Pesaran B., Shanechi M. M., “Multiscale decoding of spike-field activity to improve brain-machine interface robustness and longevity”, Annual Meeting, Society for Neuroscience (SFN), 11–15 Nov. 2017, Washington, DC.
Abbaspourazad H., Shanechi M. M., “Learning the dependencies between spikes and fields in multiscale modeling”, Annual Meeting, Society for Neuroscience (SFN), 11–15 Nov. 2017, Washington, DC.
Yang Y., Sellers K.K., Chang E. F., Shanechi M. M., “Modeling dynamic brain network responses to electrical stimulation”, Annual Meeting, Society for Neuroscience (SFN), 11–15 Nov. 2017, Washington, DC.
Sani O., Yang Y., Chang E. F., Shanechi M. M., “Real-time decoding of mood from human large-scale ECoG activity”, Annual Meeting, Society for Neuroscience (SFN), 11–15 Nov. 2017, Washington, DC.
Yang Y., Shanechi M.M., “An adaptive and generalizable closed-loop system for control of medically-induced coma and other states of anesthesia”, Journal of Neural Engineering, 13(6):066019, Nov. 2016 (link)
Shanechi M.M., Orsborn A.L., Carmena J.M., “Robust brain-machine interface design using optimal feedback control modeling and adaptive point process filtering”, PLoS Computational Biology 12(4): e1004730, Apr. 2016. (link).
Abbaspourazad H., Shanechi M.M., “A new modeling framework for multiscale neural activity underlying behavior”, Society for Neuroscience (SFN), San Diego, CA, 2016,
Hsieh, H., Shanechi M.M., “Adaptive multiscale brain-machine interface decoders”, Society for Neuroscience (SFN), San Diego, CA, 2016
Yang Y., Shanechi M.M., “Adaptive identification of high-dimensional brain network dynamics to track non-stationarity and plasticity”, Society for Neuroscience (SFN), San Diego, CA, 2016
Yang Y., Shanechi M.M., “A framework for identification of brain network dynamics using a novel binary noise modulated electrical stimulation pattern”, in Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC) Conference, Milan, Italy, 2015 (link)
– Top three winners of the IEEE EMBC student paper competition (link)
Hsieh H., Shanechi M.M, “Optimal calibration of the learning rate in closed-loop adaptive brain-machine interfaces”, in Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC) Conference, Milan, Italy, 2015 (link)
Yang Y., Shanechi M.M., “A generalizable adaptive brain-machine interface architecture for closed-loop control of anesthesia”, in Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC) Conference, Milan, Italy, 2015 (link)
Connolly A., Yang Y., Chang E.F., Shanechi M.M., “Modeling brain network dynamics underlying mood disorders”, Society for Neuroscience (SFN), Chicago, Il., 2015
Yang Y., Connolly A., Shanechi M.M., “A novel binary noise modulated electrical stimulation pattern for identification of brain network dynamics”, Society for Neuroscience (SFN), Chicago, Il., 2015
Hsieh, H., Shanechi M.M., “A general framework for optimal selection of the learning rate in closed-loop brain-machine interfaces”, Society for Neuroscience (SFN), Chicago, Il., 2015
Shanechi M.M., Orsborn A.L., Moorman H., Gowda S., Dangi S, Carmena J.M., “Rapid sensorimotor control and feedback rates enhance neuroprosthetic control”, Cell Symposia: Engineering the Brain, Chicago, Il., 2015
Yang Y., Shanechi M.M., “An adaptive and robust brain-machine interface architecture for closed-loop control of anesthesia”, International Anesthesia Research Society (IARS) meeting, Honolulu, HI, 2015.
Orsborn A. L., Mooreman H. G., Overduin S. A., Shanechi M. M., Dimitrov D. F., Carmena J. M., “Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control”, Neuron 82(6), Jun. 2014. (link)
Dangi S., Gowda S., Moorman H.G., Orsborn A.L., So K., Shanechi M. M., Carmena J.M., “Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfaces”, Neural Computation, 26:9, Sep. 2014. (link)
Shanechi M. M., Hu R., Williams, Z.M., “A cortical-spinal prosthesis for targeted limb movement in paralyzed primate avatars”, Nature Communications, 5:3237, Feb. 2014 (pdf) (link)
Yang Y., Shanechi M.M., “An adaptive brain-machine interface algorithm for control of burst suppression in medical coma”, in Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC) Conference, Chicago, Il, 2014 (link)
Shanechi M.M., Orsborn A.L., Moorman H., Gowda S., Carmena J.M., “High-performance brain-machine interface enabled by an adaptive optimal feedback-controlled point process decoder”, in Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC) Conference, Chicago, Il., 2014 (link)
Chang Y. H., Chen M., Shanechi M. M., Carmena J. M., Tomlin C., “A design of neural decoder by reducing discrepancy between manual control and brain control”, in Proceedings of European Control Conference (ECC), Strasbourg, France, 2014 (link)
Shanechi M.M., Orsborn A.L., Moorman H., Gowda S., Dangi S., Carmena J.M., “Spike-by-spike control using an adaptive optimal feedback-controlled point process decoder improves BMI performance”, Society for Neuroscience (SFN), Washington DC, 2014
Shanechi M. M., Chemali J., Liberman M., Solt K., Brown E. N., “Control of burst-suppression in a rodent model of medical coma using a brain-machine interface”, International Anesthesia Research Society (IARS) meeting, Montreal, Canada, 2014.
-Best paper award in Technology, Computation, and Simulation
Shanechi M. M., Chemali J., Liberman M., Solt K., Brown E. N. “A brain-machine interface for control of medically-induced coma”, PLOS Computational Biology, 9(10), Oct. 2013 (pdf) (link)
Shanechi M. M., Williams Z. M., Wornell G. W., Hu R. C., Powers M., Brown E. N. “A real-time brain-machine interface combining motor target and trajectory intent using an optimal feedback control design”, PLOS ONE 8 (4), Apr. 2013. (pdf) (link)
Shanechi M. M., Carmena J. M. “Optimal feedback-controlled point process decoder for adaptation and assisted training in brain-machine interfaces”, in Proceedings of IEEE international conference on neural engineering (NER), San Diego, CA, 2013 (link)
Shanechi M. M., Orsborn A., Gowda S., Carmena J. M. “Proficient BMI control enabled by closed-loop adaptation of an optimal feedback-controlled point process decoder”, in Translational and Computational Motor Control (TCMC) Meeting, San Diego, CA, 2013
Shanechi M. M., Chemali J., Liberman M., Solt K., Brown E. N. “A brain-machine interface for control of burst suppression in medical coma”, in Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC) Conference, Osaka, Japan, 2013 (link)
Shanechi M. M., Chemali J., Liberman M., Solt K., Brown E. N. “A brain-machine interface for control of medically-induced coma”, in Computational and Systems Neuroscience (COSYNE) Meeting, Salt Lake City, 2013.
Shanechi M. M., Chemali J., Liberman M., Solt K., Brown E. N. “A brain-machine interface for control of medically-induced coma”, Society for Neuroscience (SFN), San Diego, CA, 2013
Shanechi M. M., Hu R., Powers M., Wornell G. W., Brown E. N., Williams Z. M. “Neural population partitioning and a concurrent brain-machine interface for sequential motor function”, Nature Neuroscience, 15 (12), Dec. 2012. (pdf) (link)
-Nature Research Highlights: “Brain–machine does the two-step”, Nature, 491 (305), Nov. 2012 (pdf) (link)
Shanechi M. M., Wornell G. W., Williams Z. M., Brown E. N. “Feedback-controlled parallel point process filter for estimation of goal-directed movements from neural signals”, IEEE Trans. on Neural Syst. Rehabil. Eng., Oct. 2012. (pdf) (link)
Shanechi M. M., Hu R., Powers M., Wornell G. W., Brown E. N., Williams Z. M. “A concurrent brain-machine interface for enhanced motor function”, in Computational and Systems Neuroscience (COSYNE), Salt Lake City, USA, 2012
2011 and earlier
Shanechi M. M., Porat R., Erez U. “Comparison of practical feedback algorithms for multiuser MIMO”, IEEE Transactions on Communications, 58 (8), Aug. 2010. (pdf) (link)
Shi G., Shanechi M. M., Aarabi, P. “On the importance of phase in human speech recognition”, IEEE Transactions on Audio, Speech and Language Processing, 14 (5), Sep. 2006. (pdf) (link)
Mavandadi S., Aarabi P., Mohajer K., Shanechi M. M. “Post recognition speech localization”, International Journal of Speech Technology, 8 (2), Jun. 2005.
Shanechi M. M., Wornell G. W., Williams Z. M., Brown E. N. “A parallel point-process filter for estimation of goal-directed movements from neural signals”, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (Dallas, USA, 2010) (pdf) (link)
Shanechi M. M., Porat R., Erez U. “Comparison of practical feedback algorithms for multiuser MIMO”, in Proceedings of IEEE Vehicular Technology Conference (VTC), (Barcelona, Spain, 2009) (pdf)
Shanechi M. M., Erez U., Wornell G. W. “Time-invariant rateless codes for MIMO channels”, in Proceedings of IEEE International Symposium on Information Theory (ISIT), (Toronto, Canada, 2008) (pdf) (link)
Shanechi M. M., Erez U., Wornell G. W. “Universal coding for parallel Gaussian channels”, in Proceedings of IEEE International Zurich Seminar on Communications (ETH), (Zurich, Switzerland, 2008) (pdf) (link)
Shanechi M. M., Aarabi P.: “Structural analysis of multisensor arrays for speech separation applications”, in Proceedings of Sensor Fusion: Architectures, Algorithms, and Applications VII, (Orlando, USA, 2003)
Shanechi M. M., Hu R., Powers M., Wornell G. W., Brown E. N., Williams Z. M. “A real-time concurrent brain-machine interface for performing sequential movements”, in 41st Annual Meeting, Society for Neuroscience (SFN), Washington, USA, 2011
Shanechi M. M., Hu R., Powers M., Wornell G. W., Brown E. N., Williams Z. M. “A brain-machine interface combining target and trajectory information using optimal feedback control”, in Computational and Systems Neuroscience (COSYNE) Meeting, Salt Lake City, USA, 2011.
Shanechi M. M., Williams Z. M., Wornell G. W., Brown E. N. “Combining plan and peri-movement activities improves the performance of brain-machine interfaces”, in 40th Annual Meeting, Society for Neuroscience (SFN), San Diego, USA, 2010
Shanechi M. M., Williams Z. M., Wornell G. W., Brown E. N. “A real-time brain-machine interface combining plan and peri-movement activities”, in Research in Encoding And Decoding of Neural Ensembles Conference (AREADNE), Santorini, Greece, 2010
Shanechi M. M. “Real-time brain-machine interface architectures: neural decoding from plan to movement”, PhD Dissertation, MIT, April 2011. (pdf)
Aarabi P., Shi G., Shanechi M. M., Rabi S., “Phase-based speech processing”, World Scientific, 2006.
CBSE Class 11 Biology Chapter 21 Neural Control and Coordination Study Materials
The human body has several organs. These organs cannot perform their functions independently. In order to maintain homeostasis for the normal physiology of the human body, functions of these organs/organ systems in our body must be coordinated, so that they can work in proper manner.
Topic 1 Nervous System : An Overview
Coordination is the process through which two or more organs interact and complement the functions of one another. On the other hand, integration is a process, which makes two or more organs to work as a functional unit in harmony.
For instance, when we do exercise, we observe significant increase in the rate of respiration, heart beat, blood flow, sweating, etc., to meet enhanced need of nutrients and energy for increased activities of lungs, heart, muscles and many other body organs, when we stop exercising, we witness that the increased activities of lungs, heart, nerves, kidneys, muscles, etc., gradually return to normal. Thus, during exercise, functions of various organs of the body are coordinated and integrated.
In higher animals (including human), two types of systems have been developed for the control, coordination and integration, i.e., nervous system and endocrine system. The nervous system provides an organised network of point to point connections for quick neural coordination. The endocrine system provides chemical integration through hormones.
The neural system is the control system of the body which consists of highly specialized cells called neurons. The sensory neurons detect and receive information from different sense organs (receptors) in the form of stimuli and transmit the stimuli to the Central Neural System (CNS) through sensory nerve fibers. In CNS the processing of information is done and a conclusion is drawn.
The conclusion is sent to different organs (effectors) through motor nerves. These effectors then show the response accordingly.
The neural or nervous system is present in most of the multicellular animals. Its complexity increases from lower to higher animals.
Invertebrates have relatively simpler nervous system than the vertebrates.
Human Neural System
The whole nervous system of human being is derived from embryonic ectoderm.
The human neural system is divided into two parts
(i) the Central Neural System (CNS)
(ii) the Peripheral Neural System (PNS)
The CNS includes the brain and the spinal cord and is the site of information processing and control.
The PNS comprises of all the nerves of the body associated with the CNS (brain and spinal cord).
The nerve fibres of the PNS are of two types
(a) Afferent Fibres They transmit impulses from tissues/organs to the CNS.
(b) Efferent Fibres They transmit regulatory impulses from the CNS to the concerned peripheral tissues/organs.
The PNS is divided into two divisions i.e., somatic neural system and autonomic neural system.
The somatic neural system relays impulses from the CNS to skeletal muscles while, the autonomic neural system transmits impulses from the CNS to the involuntary organs and smooth muscles of the body.
The autonomic neural system is further classified into sympathetic neural system and parasympathetic neural system.
Neuron (Structural and Functional Unit of Neural System)
Neurons are the longest cells in the body. Human neural system has about 100 billion neurons. Majority of the neurons occur in the brain. Fully formed neurons never divide and remain in interphase throughout life.
A neuron is a microscopic structure composed of three major parts
1. Cell Body (Cyton or Soma)
Like a typical cell it consists of cytoplasm, nucleus and cell membrane. The cytoplasm has typical cell organelles like mitochondria, Golgi apparatus, rough endoplasmic reticulum, ribosomes, lysosomes, certain granular bodies, neurofibrils, neurotubules and Nissl’s granules.
Presence of neurofibrils and Nissl’s granules is the characteristic to all neurons. Neurofibrils play a role in the transmission of impulses.
2. Dendrites (Dendrons)
Dendrites are usually shorter, tapering and much branched processes that project out of the cell body. They also contain Nissl’s granules and may be one to several in number.
They conduct nerve impulses towards the cell body and are called afferent processes (receiving processes).
Axon is a single, usually very long process of uniform thickness. The part of cyton from where the axon arises is called axon hillock (most sensitive part of neuron).
The axon contains neurofibrils and neurotubules but does not have Nissl’s granules, cell organelles and granular bodies. The axon ends (distal end) in a group of branches, the terminal arborization (axon terminals).
When terminal arborisations of the axon meet the dendrites of another neuron to form a synapse, each branch terminates as a bulb-like structure called synaptic knobs, which possess mitochondria and secretory vesicles (containing chemicals called neurotransmitters). The axons transmit nerve impulses away from the cell body to a synapse or to a neuromuscular junction.
There are two types of axon
In myelinated nerve fibres Schwann cells form myelin sheath around the axon. The gaps between two adjacent myelin sheaths are called nodes of Ranvier. Myelinated nerve fibres are found in cranial and spinal nerves.
In non-myelinated nerve fibres Schwann cell does not form myelin sheath around the axon and are without nodes of Ranvier. They are commonly found in autonomous and somatic neural systems.
Types of Neurons on the Basis of Structure
Based on the number of axon and dendrites, the neurons are divided into three types
(i) Multipolar neurons These neurons have several dendrites and an axon. They are found in cerebral cortex.
(ii) Bipolar neurons These neurons have one dendrite and one axon. They are present in the retina of eye.
(iii) Unipolar neurons These neurons have cell body with one axon only. These are found usually in the embryonic stage.
Main Properties of Neural Tissue
The neural tissue has two outsandingproperties
(a) Excitability It is the ability of nerve cells to generate an electrical impulse in response to a stimulus by altering the normal potential difference across their plasma membrane.
(b) Conductivity It is the ability of nerve cells to rapidly transmit the electrical impulse as a wave from the site of its origin along their length in a particular direction.
Functions of Neural System
The nervous system serves the following important functions
(i) Control and coordination Nervous system controls and coordinates the working of all parts of the body so that it functions as an integrated unit. This is achieved by three overlapping processes, i.e., sensory input, integration and motor output.
(ii) Memory Nervous system stores the impressions of previous stimuli and retrieves (recalls) these impressions in future. These impressions are referred to as the experiences or memory.
(iii) Homeostasis Nervous system helps in the maintenance of the body’s internal environment, i.e., homeostasis.
Generation and Conduction of Nerve Impulse
Nerve impulse is a wave of bioelectric/electrochemical disturbance that passes along a neuron during conduction of an excitation.
Impulse conduction depends upon
(i) Permeability of axon membrane (axolemma).
(ii) Osmotic equilibrium (electrical equivalence) between the axoplasm and Extracellular Fluid (ECF) present outside the axon.
The generation of a nerve impulse is the temporary reversal of the resting potential in the neuron.
It occurs in following three steps
Polarisation (Resting Potential)
In a resting nerve fibre (a nerve fibre that is not conducting an impulse), the axoplasm (neuroplasm of axon) inside the axon contains high concentration of K + and negatively charged proteins and low concentration of Na + .
(i) In contrast, the fluid outside axon contains a low concentration of K + and a high concentration of Na + and thus form a concentration gradient.
(ii) These ionic gradients across the resting membrane are maintained by the active transport of ions by the sodium-potassium pump, which transports 3Na + out wards and 2K + inwards (into the cell).
(iii) As a result, the outer surface of the axonal membrane possesses a positive charge, while its inner surface becomes negatively charged and therefore, is polarised.
(iv) The electrical potential difference across the resting plasma membrane is called as the resting potential. The state of the resting membrane is called polarised state.
Depolarisation (Action Potential)
When a stimulus of adequate strength (threshold stimulus) is applied to a polarised membrane, the permeability of the membrane to Na + ions is greatly increased at the point of stimulation (site A).
(i) This leads to a rapid influx of Na + followed by the reversal of the polarity at that site, i.e., the outer surface of the membrane becomes negatively charged and the inner side becomes positively charged. The polarity of the membrane at the site A is thus, reversed and said to be depolarised.
(ii) The electrical potential difference across the plasma membrane at the site A is called the action potential, another name of nerve impulse.
(iii) At adjacent sites, e.g., site B, the membrane (axon) has positive charge (still polarised) on the outer surface and a negative charge on its inner surface.
(iv) The stimulated negatively charged point on the outside of the membrane sends out an electrical current to the positive point next to it. As a result, a current flows on the outer surface from site B to site A, while on the inner surface current flows from site A to site B.
This process (reversal) repeats itself over and over again and a nerve impulse is conducted through the length of the neuron.
(i) The rise in the stimulus-induced permeability to Na + is extremely short-lived. It is quickly followed by a rise in permeability to K + .
(ii) Within a fraction of a second, Na + influx stops and K + outflow begins until the original resting state of ionic concentration is achieved. Thus, resting potential is restored at the site of excitation, which is called repolarisation of the membrane. This makes the fibre once more responsive to further stimulation.
(iii) In fact until repolarisation occurs neuron cannot conduct another impulse. The time taken for this restoration is called refractory period.
* When an impulse travels along a myelinated neuron, depolarisation occurs only at the nodes of Ranvier. It leaps over the myelin sheath from one node to the next. This process, is called saltatory conduction.
* This process accounts for the greater speed of an impulse travelling along a myelinated neuron than along a non-myelinated one. It is upto 50 times faster than the non-myelinated nerve fibre.
A nerve impulses is transmitted from one neuron to another through junctions called synapses. It is formed by the membranes of a pre-synaptic neuron and a post-synaptic neuron.
There are mainly two types of synapses
(i) The membranes of pre and post-synaptic neurons are in very close proximity (i.e., in continuity). The continuity is provided by the gap junction (small protein tubular structures) between the two neurons.
(ii) In electrical synapse, there is minimal synaptic delay because of the direct flow of electrical current from one neuron into the other across these synapses.
Thus, impulse transmission across an electrical synapses is always faster than that across a chemical synapse. In such synapses, transmission of impulse is very similar to impulse conduction along a single axon.
(iii) Electrical synapses are rarely found in our system. It is found in cardiac muscle fibres, smooth muscle fibres of intestine and the epithelial cells of lens.
The membranes of pre and post-synaptic neurons are separated by a fluid-filled space called synaptic cleft.
A brief description of the mechanism of synaptic transmission is given below
(i) When an impulse (action potential) arrives at a pre-synaptic knob, calcium ions from the synaptic cleft enter the cytoplasm of the pre-synaptic knob.
(it) The calcium ions cause the movement of the synaptic vesicles to the surface of the knob.
The synaptic vesicles are fused with the pre-synaptic (plasma membrane and get ruptured (exocytosis) to discharge their contents (neurotransmitter) into the synaptic cleft.
(iii) The neurotransmitter of the synaptic cleft binds with specific protein receptor molecules, present on the post-synaptic membrane.
(iv) This binding action changes the membrane potential of the post-synaptic membrane, opening channels in the membrane and sodium ions to enter the cell. This causes the depolarisation and generation of action potential in the post-synaptic membrane. Thus, the impulse is transferred to the next neuron.
(v) The new potential developed may be either excitatory or inhibitory.
Topic 2 Human Nervous System
The human neural system can be categorised to
(a) Central Nervous System (CNS)
(b) Peripheral Nervous System (PNS)
Central Nervous System (CNS)
It is the integrating and command centre of the nervous system which consists of the brain and spinal cord (as discussed earlier).
The brain is the central information processing organ of our body and acts as the ‘command and control system’.
It controls the following activities
(i) The voluntary movements and balance of the body.
(ii) Functioning of vital involuntary organs, e.g., Lungs, heart, kidneys, etc.
(iii) Thermoregulation, hunger and thirst.
(iv) Circardian (24 hrs) rhythms of our body.
(u) Activities of several endocrine glands and human behaviour.
(vi) It is also the site for processing of vision, hearing, speech, memory, intelligence, emotions and thoughts.
The brain is the anterior most part of the central neural system, which is located in the cranium
(cranial cavity) of the skull.
Protective Coverings of the Brain
It is covered by three membranes or meninges (cranial meninges)
(i) The outermost membrane, the duramater is the tough fibrous membrane adhering close to the inner side of the skull.
(ii) The middle very thin layer called arachnoid membrane (arachnoid mater).
(iii) The innermost membrane, the piamater is thin, very
delicate, which is in contact with the brain tissue.
The human brain weights from 1200-1400 g. The human neural system has about 100 billion neurons, majority of them occur in the brain.
The human brain is divisible into three parts
(i) Forebrain (ii) Midbrain (iii) Hindbrain
i. The forebrain
It consists of Olfactory lobes The anterior part of the brain is formed by a pair of short club-shaped structures, the olfactory lobes. These are concerned with the sense of smell.
Cerebrum It is the largest and most complex of all the parts of the human brain. A deep cleft divides the cerebrum longitudinally into two halves, which are termed as the left and right cerebral hemispheres connected by a large bundle of myelinated fibres the corpus callosum.
* The outer cover of cerebral hemisphere is called cerebral cortex. The cerebral cortex is referred to as the grey matter due to its greyish appearance (as neuron cell bodies are concentrated here).
The cerebral cortex is greatly folded. The upward folds, gyri, alternate with the downward grooves or sulci. Beneath the grey matter there are millions of medullated nerve fibers, which constitute the inner part of the cerebral hemisphere. The large concentration of medullated nerve fibres gives this tissue an opaque white appearance. Hence, it is called the white matter.
* Lobes A very deep and a longitudinal fissure, separates the two cerebral hemispheres. Each cerebral hemisphere of the cerebrum is divided into four lobes, i.e., frontal, parietal, temporal and occipital lobes.
In each cerebral hemisphere, there are three types of junctional areas
* Sensory areas receive impulses from the receptors and motor areas transmit impulses to the effectors.
* Association areas are large regions that are neither clearly sensory nor motor in junction. They interpret the input, store the input and initiate a response in light of similar past experience. Thus, these areas are responsible for complex functions like memory, learning, reasoning and other intersensory associations.
Distction the posterioventral part of forebrain.
Its main parts are as follows
* Epithalamus is a thin membrane of non-nervous tissue. It is the posterior segment of the diencephalon.
* The cerebrum, wraps around a structure called thalamus, which is a major coordinating center for sensory and motor signalling.
The hypothalamus, that lies at the base of thalamus contains a number of centres, which control body temperature, urge for eating and drinking. It also contains several groups of neurosecretory cells, which secrete hormones called hypothalamic hormones.
The inner parts of cerebral hemispheres and a group of associated deep structures like amygdala, hippocampus, etc., form a complex structure (limbic lobe or limbic system) that are involved in the regulation of sexual behaviour,expression of emotional reactions, e.g„ excitement, pleasure,rage and fear and motivation,
The midbrain is located between the thalamus hypothalamus of the forebrain and pons of the hindbrain. A canal called the cerebral aqueduct passes through, the midbrain.
The dorsal portion of the midbrain mainly consists of two pairs (i.e., four) of rounded swellings (lobes) called corpora quadrigemina.
The hindbrain consists of
(a) Pons consists of fibre tracts that interconnect different regions of the brain.
(b) Cerebellum is the second largest part of the human brain (means litde cerebrum). It has very
convoluted surface in order to provide the additional space for many more neurons.
(c) Medulla (oblongata) is connected to the spinal cord and contains centres, which control respiration, cardiovascular reflexes and gastric secretions.
* Midbrain and hindbrain form the brain stem. It is the posterior part of the brain that continues with the spinal cord.
* Out of the twelve pairs of cranial nerves (in higher vertebrates), ten pairs come from the brain stem.
(i) It forms the posterior part of the CNS, running mid-dorsally in the neural canal of the vertebral column. In an adult, the spinal cord is about 42-45 cm long. Its diameter varies at different levels.
(ii) The spinal cord is formed of two types of nervous tissue, i.e., grey matter and white matter.
(iii) The grey matter is surrounded by white matter, which consists of groups of myelinated axons.
(iv) The spinal nerve tracts are divisible into two, ascending (conducting sensory impulses towards brain) and descending (conducting motor impulses from brain).
(v) Spinal cord conducts impulses to and from the brain and controls most of the reflex activities and provides a means of communication between spinal nerves and the brain.
Reflex Action and Reflex Arc
The entire process of response to a peripheral nervous stimulation, that occurs involuntarily, i.e., without conscious effort or thought and requires the involvement of a part of the central nervous system is called a reflex action. The nervous pathway taken by nerve impulses in a reflex action is called reflex arc.
Types of Reflexes
Reflexes are categorised into two
(i) Unconditioned (inborn reflexes and transmitted through heredity) breast feeding and swallowing.
(ii) Conditioned (acquired after birth, i.e., adopted during the course of life time.) e.g., Withdrawl of a body part (like limb) which comes in contact with objects that are extremly hot, cold, pointed or animals that are scary or poisonous.
Mechanism of Reflex Action
(i) The reflex pathway comprises atleast, one afferent (receptor) neuron and one efferent (effector) neuron arranged in a series.
(ii) The afferent neuron receives signal from a sensory organ and transmits the impulse via a dorsal nerve root into the CNS (at the level of spinal cord).
(iii) The efferent neuron then carries signals from CNS to the effector. The stimulus and response in this way forms a reflex arc, e.g., Knee jerk reflex as shown above in the diagram.
Peripheral Nervous System (PNS)
The peripheral nervous system consists of
1. Somatic Neural System (SNS)
2. Autonomic Neural System (SNS)
1. Somatic Neural System
The somatic neural system contains nerves which relay impulses from CNS to skeletal muscles. These can be further categorised into cranial (from brain) and spinal nerves on the basis of their origin.
These nerves emerge specifically from the forebrain and brain stem.
* Trochlear is smallest and thinnest nerve and possess difficulty in surgical operations.
* Trigeminal is also called dentist nerve. It is the largest cranial nerve. At its origin it is associated with ‘Gasserian Ganglion’.
* Facial nerve is associated with geniculate ganglion at its origin.
Their functions in comparative manner in a nut shell are given below
ii. Spinal Nerves
All spinal nerves are mixed, having sensory and motor fibres in approximately equal numbers. In humans, 31 pairs of spinal nerves are present as Cervical (8 pairs), Thoracic (12 pairs), Lumber (5 pairs), Sacral (5 pairs), Coccygeal (1 pair).
There are 10 pairs of cranial nerves in fishes and amphibians and 12 pairs in rest of the higher chordates.
There are 10 pairs of spinal nerves found in fishes and amphibians and 31 pairs in humans.
Based on their functions, the nerve fibres of PNS are divided into two groups, i.e., afferent fibres and efferent fibres.
The afferent nerve fibres transmit sensory impulses from tissues/organs to the CNS and form the sensory or afferent pathway. The efferent nerve fibres transmit motor impulses from CNS to the concerned tissues/organs and form the motor or efferent pathways.
2. The Autonomic Neural System (ANS)
The autonomic neural system consists of the sympathetic and parasympathetic nervous system. The former is called thoraco-lumber outflow and the latter is called craniosacral outflow depending upon their origin.
Topic 3 Sensory Reception and Processing
The sensory organs (receptors) enable us to detect all types of changes in the environment and send appropriate signals to the CNS, where all the inputs are processed and analysed. Signals are then sent to different centres of the brain.
The most complex sensory receptors consist of numerous sense cells, sensory neurons and associated accessory structures. For example, eye (sensory organ for vision) and the ear (sensory organ for hearing).
The organ of sight are a pair of eyes in human.
The eyes are situated in the deep protective bony cavities, called the orbits or eye sockets of the skull.
Parts of an Eye
The adult human eye ball is nearly spherical in structure. It consists of tissues present in three concentric layers
(i) Outermost fibrous layer composed of sclera and cornea.
(ii) Middle layer consists of choroid, ciliary body and iris.
(iii) Innermost layer consists df retina.
(i) Sclera is an opaque outermost covering, composed of dense connective tissue that maintains the shape of the eyeball and protects all the inner layers of the eye.
(ii) Cornea is a thin transparent, front part of sclera, which lacks blood vessels but is rich in nerve endings.
(i) Choroid is a pigmented layer (bluish) present beneath the sclera. It contains numerous blood vessels and nourishes the retina. The choroid layer is thin over the posterior two-thirds of the eye ball, but it becomes, thick in the anterior part to form the ciliary body.
(ii) The eye ball contains a transparent crystalline structure called lens. Ciliary body holds the lens in position, stretching and relaxation of ciliary body changes the focal length of the lens for accomodation.
(iii) Iris forms a pigmented circle of muscular diaphragm attached to the ciliary body in front of the lens. Its pigment gives eye its colour.
The movement of muscle fibres of iris controls the size (diameter) of pupil.
(iv) Pupil is the aperture surrounded by the iris. It contains two types of smooth muscles, circular muscles (sphincters) and radial muscles (dilators) of ectodermal origin.
(v) Sympathetic stimulation causes the radial muscles to contract and the pupil to dilate or get larger. Parasympathetic stimulation causes the circular muscles to contract and the pupil to constrict.
The inner layer is the retina and it contains three layers of cells from inside to outside, i.e., ganglion cells, bipolar cells and photoreceptor cells.
The photoreceptors or visual cells are of two types, i.e., rods (rod cells) and cones (cone cells). Both of these cells contain light sensitive proteins called the photopigments.
The twilight (scotopic) vision is the function of the rods. These cells contain a purplish-red protein called the rhodopsin (visual purple), which contains a derivative of vitamin-A.
The daylight (photopic) vision and colour vision are functions of cones. There are three types of cones, which possesses characteristic photopigments that respond to red, green and blue lights.
The sensation of different colours are produced by various combinations of these cones and their photopigments. In case of equal stimulation of these cones, a sensation of white light is produced.
The optic nerves are connected with the brain. These nerves leave the eye and the retinal blood vessels enter it at a point medial to and slightly above the posterior pole of the eye-ball. Photoreceptor cells (rods and cones) are not present in that region and hence, it is called blind spot, as no image is formed at this spot.
Macula Lutea and Fovea Centralis
At the posterior pole of the eye lateral to the blind spot, there is a small oval, yellowish area of the retina called the macula lutea or yellow spot, which has at its middle a shallow depression, the fovea centralis (fovea).
The fovea is a thinned out portion of the retina where only the cones are densly packed. It is the point where the visual acuity (resolution) is the greatest.
Contents of the Eye
(i) Aqueous Humour The space between the cornea and lens is called the aqueous chamber, which contains a thin watery fluid called aqueous humour.
(ii) Vitreous Humour The space between the lens and retina is called the vitreous chamber, which is filled with a transparent get called the vitreous humour.
Mechanism of Vision
In human eyes, the vision is called binocular vision (i.e., both the eyes can be focused on a common object).
(i) Retina receives light rays (in visible wavelength) through the cornea and lens generate impulses in rods and cones.
(ii) The photosensitive compounds (photopigments) in the human eye are composed of opsin (a protein) and retinal (an aldehyde of vitamin-A).
(iii) The received light induces dissociation of the retinal from opsin resulting in changes in the structures of the opsin. This causes the changes in the permeability of membrane.
As a result, the potential differences are generated in the photoreceptor cells. This produces a signal that generates action potential in the ganglion cells through the bipolar cells.
(iv) These impulses (action potentials) are transmitted by the optic nerves to the visual cortex of the brain.
(v) In brain, neural impulses are analysed and the image formed on the retina is recognised (based on earlier
memory and experience).
(i) Cataract This is a eye disease generally occur in older people (above 60 years). Lens becomes opaque due to disease or ageing. It leads to blindness. It can be corrected by wearing suitable glasses or by replacing the defective lens with a normal lens from a donor.
(ii) Myopia (near or short sightedness) It occurs due to convexity of lens or longer eye ball, which results in an image of distant objects being formed in front of the retina, and can be corrected by wearing spectables or concave lenses.
(iii) Hypermetropia (far or long sightedness.) The image of nearer object becomes blurred. It is due to image being formed beyond the retina due to eye ball being short or lens being flattened. It can be corrected by wearing convex or convergent lenses.
(iv) Presbiopia It generally occurs after 40 years. The loss of elasticity in the eye lens occurs so that near objects (written or printed words) are not correcdy visible. It can be correct’d by convex/bifocal lenses.
Ears are a pair of statiocoustic organs meant for both sensory functions, i.e., hearing and maintenance of body balance.
The ears are located on the sides of the head.
In most mammals, the ear is a flap of tissue also called pinna. It is a part of auditory system.
The mammalian ear can be anatomically divided into three major sections
1. External Ear
The external ear consists of pinna and the auditory canal (external auditory meatus), which collect sound waves and channel them to tympanic membrane (ear drum) separating the outer ear from the middle ear.
The auditory canal leads inwards and extends upto the tympanic membrane (the ear drum).
There are very fine hairs and wax-secreting sebaceous glands in the skin of the pinna and the meatus. The tympanic membrane is composed of connective tissues covered with skin outside and with mucus membrane inside.
2. Middle Ear
The middle ear contains three ossicles called malleus (hammer), incus (anvil) and stapes (stirr-up), which are attached to one another in a chain-like fashion.
The malleus is attached to the tympanic membrane and the stapes is attached to the oval window (a membrane beneath the stapes) of cochlea.
These ossicles increase the efficiency of transmission of sound waves to the inner ear.
The middle ear also opens into the Eustachian tube, which connects with the pharynx and maintains the pressure on either sides of the ear drum. It also enables you to ‘pop’ your ears when you change altitude.
3. Inner Ear
The inner ear consist of a labyrinth of fluid-filled chambers within the temporal bone of the skull. The labyrinth consists of two parts the bony and membranous labyrinths. The bony labyrinth is a series of channels. Inside the channels, membranous labyrinth lies, which is surrounded by a fluid called perilymph.
The membranous labyrinth is filled with a fluid called endolymph. The coiled portion of the labyrinth is called cochlea.
The membranes constituting cochlea (the Reissner’s and basilar), divide the bony labyrinth into two large canals, i.e., an upper vestibular canal (scala vestibuli) and a lower tympanic canal (scala tympani).
These (both) canals are separated by a small cochlear duct called scala media. The vestibular and tympanic canals contain and the cochlear duct is filled with endolymph.
At the base of the cochlea, the scala vestibuli ends at the oval window while, the scala tympani terminates at the round window, which opens to the middle ear.
Organ of Corti
The floor of the cochlear duct, the basilar membrane bears the organ of Corti. It contains the mechanoreceptors of the ear. The hair cells are present in rows on the internal side of the organ of Corti, that act as auditory receptors. The basal end of the hair cell is in close contact with the afferent nerve fibres.
A large number of processes called stereo cilia are projected from the apical part of each hair cell. Above the rows of hair cells is a thin elastic membrane called tectorial membrane.
(i) The inner ear also contains a complex system called vestibular apparatus (located above the cochlea). It is composed of three semicircular canals and the otolith organ consisting of the saccule and utricle.
(ii) Each semicircular canal lies in a different plane at right angles to each other. The membranous canals are suspended in the perilymph of the bony canals. The base of canals is swollen and is called ampulla, which contains a projecting ridge called crista ampullaris, which has hair cells.
(iii) The saccule and utricle contain a projecting ridge called macula. The crista and macula are the specific receptors of the vestibular apparatus responsible for the maintenance of balance of the body and posture.
Mechanisms of Hearing
(i) Sound waves from the environment are received by the external ear and it directs them to the ear drum.
(ii) The ear drum vibrates due to sound waves and the vibrations are send to oval window through the ear ossicles (malleus, incus and stapes).
(iii) The vibrations are passed through the oval window on to the fluid of the cochlea, where they generate waves in the lymph.
(iv) The waves in the lymph induce a ripple in the basilar membrane.
(v) These movements of the basilar membrane bend the hair cells, pressing them against the tectorial membrane. Due to this, the nerve impulses are
generated in the associated afferent neurons. These impulses are transmitted by the afferent fibres via auditory nerves to the auditory cortex of the brain, where the impulses are analysed and the sound is recognised.
(i) Meniere’s Syndrome It is a hearing loss due to pathological distension of membranous labyrinth.
(ii) Eustachitis It occurs due to inflammation of Eustachian tube.
(iii) Tympanitis It is due to inflammation of ear drum.
(iv) Otalgia Pain occurs in ear.
(v) Otitis media Acute infection in middle ear.
Long Jin received the B.E. degree and the Ph.D. degree from Sun Yat-sen University, Guangzhou, China, in 2011 and in 2016, respectively. He is currently a full professor with the School of Information Science and Engineering, Lanzhou University, Lanzhou, China. Before joining Lanzhou University in 2017, he was a postdoctoral fellow with the Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. His main research interests include neural networks, robotics and intelligent information processing.
Shuai Li received the B.E. degree in Precision Mechanical Engineering from Hefei University of Technology, China in 2005, the M.E. degree in Automatic Control Engineering from University of Science and Technology of China, China in 2008 , and the Ph.D. degree in Electrical and Computer Engineering from Stevens Institute of Technology, USA in 2014 . He is currently a research assistant professor with Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. He is on the editorial board of the International Journal of Distributed Sensor Networks. His current research interests include dynamic neural networks, wireless sensor networks, robotic networks, machine learning, and other dynamic problems defined on a graph.
Jiguo Yu received his Ph.D. degree in School of Mathematics from Shandong University in 2004. He became a full professor in the School of Computer Science, Qufu Normal University, Shandong, China in 2007. Currently he is a full professor in the School of Information Science and Engineering, Qufu Normal University. His main research interests include privacy-aware computing, wireless networking, distributed algorithms, peer-to-peer computing, and graph theory. Particularly, he is interested in designing and analyzing algorithms for many computationally hard problems in networks. He is a senior member of the CCF (China Computer Federation).
Jinbo He received the B.S. degree in internet and multimedia technologies from The Hong Kong Polytechnic University, Hong Kong. He is currently a research assistant with The Hong Kong Polytechnic University, Hong Kong. His current research interests include computer graphics, artificial intelligence, neural networks, and distributed system.
This work is supported by the National Natural Science Foundation of China (with numbers 61703189 and 61401385), by the Fundamental Research Funds for the Central Universities (no. lzujbky-2017-37), by Hong Kong Research Grants Council Early Career Scheme (with no. 25214015), by Hong Kong Polytechnic University (with numbers G-YBMU, G-UA7L, 4-ZZHD, F-PP2C, 4-BCCS), by the Hunan Provincial Natural Science Foundation of China (with numbers 2017JJ3257 and 2017JJ3258), and also by the Research Foundation of Education Bureau of Hunan Province, China (nos. 17B215 and 17C1299). Kindly note that L. Jin and S. Li are jointly of the first authorship.