cse 599 lecture 5: neurobiology
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CSE 599 Lecture 5: Neurobiology. Why study neurobiology (if you are a computer scientist/software engineer/hardware designer)? Animal brains routinely solve problems that we would like computers to solve, e.g. computer vision, speech understanding, robot navigation, etc. - PowerPoint PPT PresentationTRANSCRIPT
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1R. Rao, Week 5: Neurobiology
CSE 599 Lecture 5: Neurobiology
Why study neurobiology (if you are a computer scientist/software engineer/hardware designer)?
Animal brains routinely solve problems that we would like computers to solve, e.g. computer vision, speech understanding, robot navigation, etc.
Neurobiology provides a model for machine intelligence and learning, e.g. artificial neural networks, learning algorithms, etc.
Neurobiology can provide new paradigms for computer design, e.g. parallel computing, graceful degradation, fault-tolerant computing, etc.
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2R. Rao, Week 5: Neurobiology
“I suspect that a deeper mathematical study of the nervous system will affect our understanding of the aspects of mathematics itself that are involved. In fact, it may alter the way in which we look on mathematics and logics proper.”
-- John von Neumann (The Computer and the Brain, 1958)
A quotable quote…
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The Church-Turing Thesis and Neurobiology
Church-Turing Thesis
No general model of algorithmic computation is more powerful than a Turing machine
Are animal brains algorithmic computers?
“There is no doubt that the brain can perform algorithmic computations, but that does not mean that its underlying computational mechanism is algorithmic. It is quite possible that the brain can perform computations not expressible with Turing machines”
Fundamentals of the Theory of ComputationRay Greenlaw and H. James Hoover, p. 85
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Physical basis of computation in animal brains
Animal brains are physical machines Built from hydrocarbons and ionic solutions Basic processing elements are called neurons
Animal brains represent information using physical quantities Real-valued electrical and chemical signals, with noise Transmitted on real organic wires or in real chemical solutions
Do a small set of primitives underlie neuronal computing? Digital computers
Transistors switches Boolean algebra symbol processing machines
Neurobiology ??? neurons? networks? ... adaptive sensory-motor
machines
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Neurobiology and Digital Computing
Comparing neurobiology and silicon-based digital computers Device count:
Human brain: 1011 neurons and 1015 synapses (connections) between neurons (each neuron ~ 104 connections)
IC: 109 transistors with sparse connectivity Device speed:
Biology has 100µs temporal resolution Digital circuits will have a 100ps clock (10 GHz)
Computing paradigm: Animal brains employ massively parallel computation with local
and global feedback, and adaptive connectivity Digital computers: sequential information processing via CPU
with fixed connectivity Capabilities:
Digital computers will always be better at math… Will animal brains always be better at speech or vision?
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A starting point
Hypothesis:Hypothesis: Digital computers and animal brains are efficient Digital computers and animal brains are efficient at computing in their respective domains, as a consequence of at computing in their respective domains, as a consequence of their underlying information representationtheir underlying information representation
Hypothesis:Hypothesis: The primitives underlying neuronal computation The primitives underlying neuronal computation are simpleare simple Neuronal structure and action-potential (spike-based) signaling are
conserved across the animal kingdom Just like transistor switches and Boolean algebra are conserved
across all digital computers
A starting point: Neurons, action-potentials, and synapses
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Primary computational units: Neurons
From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991, pg. 21
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Basic Input-Output Transformation
Input Spikes
Output Spike
(Excitatory Post-Synaptic Potential)
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Example of signaling in a sensory neuron
From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991, pg. 28
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Mechanisms of electrical signaling in neurons
Neuron cell membrane is a lipid bilayer Impermeable to charged ion species
such as Na+, Cl-, K+, and Ca2+
Each neuron maintains a potential difference across its membrane Typically –70 to –80 mV [Na+], [Cl-] and [Ca2+] higher
outside the cell; [K+] and organic
anions [A-] higher insideFrom Kandel, Schwartz, Jessel, Principles of Neural
Science, 3rd edn., 1991, pg. 67
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Membrane proteins allow current flow
Proteins in membranes act as pores or channels that are ion-specific E.g. Pass K+ but not Cl- or Na+
These ionic channels are gated Voltage gated: Probability of opening
depends on membrane voltage Chemically gated: Neurotransmitter
binding causes channel to open e.g. Electron microscope picture of
an ACh (acetyl choline) channel Mechanically gated: Sensitive to
pressure or stretch
Ionic pump expels Na+ ions out of cell and takes K+ ions in (consumes ATP) Maintains –70mV potential difference From Kandel, Schwartz, Jessel, Principles of Neural
Science, 3rd edn., 1991, pgs. 68 & 137
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Channel opening is probabilistic and discrete
Patch clamping (recording from an isolated patch) allows measurement of current through a single channel
Current influx is probabilistic and determined by: Probability of opening Duration of opening
Population of channels conveys smooth, graded membrane currents
From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991, pg. 71
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Gated channels allow neuronal signaling
Inputs to a neuron change its local membrane potential via chemically-gated channels at “synapses” (connections).
Changes in local membrane potential are integrated spatially and temporally in dendrites and soma of the neuron.
Changes in membrane potential trigger opening/closing of voltage-gated channels in dendrites, soma, and axon, causing depolarization (positive change in voltage) and hyperpolarization (negative change).
When a large positive change in membrane potential occurs, crossing a particular voltage threshold, a spike or action potential is generated and transmitted to other neurons.
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Neuronal Output: Action potentials
Voltage-gated channels cause action-potentials (spikes)Rapid Na+ influx causes
rising edgeNa+ pores deactivateK+ outflux restores
membrane potential
Positive feedback causes spikeNa+ influx depolarizes
membrane, causing more Na+ influx From Kandel, Schwartz, Jessel, Principles of Neural
Science, 3rd edn., 1991, pg. 110
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Here is anotherillustration of thesame concept.
The permeability changesresult in the large swingsof membrane potential that are shown above.
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An increase in permeability at one location of the membrane can spread to neighboring locations
Axons have very large concentrations of voltage-gated Na+ channels, causing the excitation to actively travel forward.
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[lower resistance][lower resistance]
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Vertebrates have developed another method of speeding up spike propagation, by adding a wrapping of myelin. This forces the electric current further down the axon, as it can only conduct where the resistance is low ---that is, at the node of Ranvier
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Active Wiring: Myelination of axons
Schwann cells (glia) enable long-range spike communication Active wire allows lossless signal
propagation, unlike electric signals in a copper wire
From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991, pgs. 23 & 44
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Communication between neurons: Synapses
Synapses: Connections between neurons Electrical synapses (gap junctions) Chemical synapses (use
neurotransmitters)
Synapses can be excitatory or inhibitory
Synapses are integral to memory and learning
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Distribution of synapses on a real neuron…
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postsynaptic element, such asanother neuron
postsynaptic element, such asanother neuron
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Synaptic biochemistry
Note: even this isa gross simplification!
From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991
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Electron micrographs of synapses
From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991
Synaptic Vesicles and
Synaptic Cleft
Fusion of vesicles and release of
neurotransmitter
Retrieval andformation of new vesicles
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Importance of Synapses
The gap between the axon and postsynaptic membrane (the synaptic cleft) allows electrical isolation of neurons
Hypothesis: Synaptic plasticity forms the basis of learning and memory
From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991
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Synaptic plasticity: Adapting the connections
Long Term Potentiation (LTP): Increase in synaptic strength that lasts for several hours or more Measured as an increase in the excitatory postsynaptic potential
(EPSP) caused by a presynaptic spike
Increase in size of EPSP observedwhen the same presynaptic input is activated before and after LTP
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Types of Synaptic Plasticity
Hebbian Long Term Potentiation: synaptic strength increases after prolonged pairing of presynaptic and postsynaptic spiking (correlated firing of two connected neurons).
Long Term Depression (LTD): Reduction in synaptic strength that lasts for several hours or more Anti-Hebbian LTD: Correlated spiking of two connected neurons
decreases the strength of their connecting synapse Homosynaptic LTD: Presynaptic spiking without postsynaptic
spiking Heterosynaptic LTD: Postsynaptic spiking without presynaptic
spiking
Spike-Timing Dependent Plasticity: LTP/LTD depends on relative timing of pre/postsynaptic spiking
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Types of Synaptic Plasticity
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Examples of measured synaptic plasticity
- and + are thresholds for LTD and LTP respectively
Hebbian LTP
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Spike-Timing Dependent Plasticity
Amount of increase/decrease in synaptic strength (LTP/LTD) depends on relative timing of pre/postsynaptic spikes
LTP
LTD
pre before postpre after post
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The mechanisms of neuronal structure and function are being rapidly unraveled via molecular, imaging, and electrophysiological techniques
But our knowledge of how networks of neurons give rise to perception, action, cognition, and
consciousness remains sketchy at best.
We will review some of this next…
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5 minute break…
Next: Brain organization and information processing in networks of neurons
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Organization of the Nervous System
CentralCentralNervous SystemNervous System
BrainBrain Spinal CordSpinal Cord
PeripheralPeripheralNervous SystemNervous System
SomaticSomatic AutonomicAutonomic
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Skeletal/Somatic Nervous System
Nerves that connect to voluntary skeletal muscles and to sensory receptors
Afferent Nerve FibersAfferent Nerve FibersAxons that carry info away from the
periphery to the CNS
Efferent Nerve FibersEfferent Nerve FibersAxons that carry info from
the CNS outward to the periphery
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Autonomic and Central Nervous System
Autonomic: Nerves that connect to the heart,blood vessels, smooth muscles, and glands
CNS: Brain + Spinal CordSpinal Cord:
• Local feedback loops control reflexes• Descending motor control signals from
the brain activate spinal motor neurons• Ascending sensory axons transmit
sensory feedback information from muscles and skin back to brain
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Major Brain Regions: Brain Stem
Cer r Thalamus
Corpus collosumHypothalamus
l C t
Pons
Medulla
Spinal cord
Cerebellum
MedullaMedullaBreathing, muscle tone
and blood pressure
PonsPonsConnects brainstem withcerebellum & involved
in sleep and arousal
CerebellumCerebellumCoordination of voluntary
movements andsense of equilibrium
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Major Brain Regions: Brain Stem
MidbrainMidbrainEye movements, visual and
auditory reflexes
Thalamus
Corpus collosumHypothalamus
l C
Pons
Medulla
Spinal cord
Cerebellum
Midbrain
Reticular FormationReticular FormationModulates muscle
reflexes, breathing & pain perception. Also
regulates sleep, wakefulness &
arousal
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e Thalamus
Corpus co losHypothalamus
rebral Cortex
Pons
Medulla
Spinal cord
Cerebellum
Major Brain Regions: Diencephalon
ThalamusThalamusRelay station for all sensory
info (except smell)to the cortex
HypothalamusHypothalamusRegulates basic needs
fighting, fleeingfighting, fleeingfeeding, andfeeding, and
matingmating
Corpus
callosum
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Major Brain Regions: Cerebral Hemispheres
Consists of: Cerebral cortex, basal ganglia, hippocampus, and amygdala
Involved in perception and motor control, cognitive functions, emotion, memory, and learning
Thalamus
Corpus collosumHypothalamus
rebral Cortex
Pons
Medulla
Spinal cord
Cerebellum
Cerebrum/Cerebral CortexCerebrum/Cerebral Cortex
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Cerebral cortex comprises a sheet of neurons
From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991, pgs.
Cerebral Cortex: Convoluted surface of cerebrum about 1/8th of an inch thick
Six layers of neurons
Approximately 30 billion neurons + 270 billion Glial cells
Each nerve cell makes about 10,000 synapses: approximately 300 trillion connections in total
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Summary
From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991
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How do these brain regions interact to produce cognition and behavior?
Current knowledge is based on electrophysiological, imaging, molecular, and psychophysical techniques in conjunction with anatomical and lesion (brain damage) studies
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The brain is specialized by region: Language
Broca’s Area
Important inthe
productionof speech
Wernicke’sArea
Important inthe
comprehensionof language
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Specialization of function in Cerebral Cortex
VisualProcessing
somatosensorycortex
Motor Planning,Higher cognitive
functions
Visual and auditoryrecognition
Spatial reasoning
and motion
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Specialization is based on connectivity
Brain regions perform elementary operations Basic computing units are neurons and synapses Specialization arises from differences in local connectivity, numbers
of neurons, and input-output connectivity to/from areas
Complex behavior arises from the interaction of multiple brain regions Example: Damage to Broca’s area
Person can understand language Person can say words or sing Person can’t speak or write grammatically
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Regional activity is problem-dependent
PET scan of human brain during visual stimulation PET scan measures local blood glucose uptake
Visual stimulation excites visual cortex Regional activation depends on stimulus complexity
From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991, p. 315
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The brain tackles complexity hierarchically
Example: Motor system Reflexive responses are handled by the spinal cord Movement is handled by the cerebellum Activity is scheduled by the cortex
Example: Speech learning by children Babies learn sounds (phonemes), then letters Toddlers learn words, then sentences Children learn grammar Teenagers learn composition
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Hierarchical processing in the visual system
Retina Thalamus (LGN)
Primary Visual Cortex (V1)
V2 …
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Hierarchical Organization of Visual Cortex
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Hierarchical Visual Processing
Photoreceptors are sensitive to lightRGB wavelength color sensitivity
Retinal ganglion cells are sensitive to simple patterns
Figure: Color-opponent retinal cells detect color spots in visual field
Successively higher levels of visual processing detect successively more complex features
First in LGN, then in cortex
First spots/lines/bars and movement, then complex features and motion, then objects/faces and spatial reasoning
From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991, pg. 473
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The Visual ProcessingHierarchy
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Predictive coding in the retina
From Ward, Sensation and Perception, 3rd edn., 1989
Retinal Ganglion cells act as predictive coders:
On-center cell: Sensitive to light turning on in the center of the field
Off-center cell: Sensitive to light turning off in the center
Predictive coding: only relative differences matter - predict the central pixel values from surrounding pixels, and send the difference. “Efficient Coding”
Why does it work? Correlations in natural images.
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Visual Illusion 1
Which ring is brighter?
From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991, pgs. 411
Both rings have the same hue: visual system measures brightness relative to background
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Neurobiology uses relative measures
A Key point:
The brain doesn’t use absolute measures or values
Relative values, adaptation, and habituation appear at all levels of neuronal processing
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Visual Illusion 2
Both women are the same size in both photosOur brain compares objects using relative perspective
measures
From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991, pgs. 411 & 444
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An important open question
Does the timing of a spike matter or is a stimulus encoded in terms of the firing rates of neurons (no. of spikes/second)?
Traditional Answer: Firing rate Motor neurons in spinal cord appear to use a rate code High variability of a neuron’s response: for the same stimulus, a
neuron emits spikes at different times but at the same firing rate Is the variability stochastic noise (synapses, channels, etc.) or is our
definition of “stimulus” too restrictive?? Maybe we are not considering other inputs to the neuron?
Recent results: Spike timing important too? Spike-timing dependent plasticity Certain auditory areas do use spike timing for localization Reliable spiking if we inject fluctuating current into a neuron in slice In some animals, correlations between spiking neurons are crucial
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Example: Spike correlations allow recognition
Locust olfactory system
A Neuronal spiking phase-locks when insect detects an odor
B Toxins end the synchronization, but not the spiking
Figures courtesy Kate MacLeod, Caltech
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Conclusions
The brain computes! Information storage: The physical/chemical structure (synapses,
wires) Information transmission: Electrical and chemical signaling Computing elements: Neurons Computational basis (information coding, functions): Unknown
We can understand neuronal computation by discerning the underlying primitives A starting model: Artificial neural networks
Types of artificial neural networks Learning rules
More sophisticated models: Compartmental/biophysical models Computational models of specific brain areas and their interactions
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Next week: Neural networks and models
DNA computing homework due next class…
Reading: See on-line chapters/articles for more information on today’s topics (Textbook/MIT on-line encyclopedia)
Mini-Project: Project ideas are up on the web site. Select one of these or come up
with your own topic – send email to check with instructor and TA Sign-up for 10-min presentation time slot on class web site
Have a great weekend!