CSE 599 Lecture 5: Neurobiology

1. 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.

2. A quotable quote… “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)

3. 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 Computation Ray Greenlaw and H. James Hoover, p. 85

4. 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

5. 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?

6. A starting point • Hypothesis: Digital computers and animal brains are efficient at computing in their respective domains, as a consequence of their underlying information representation • Hypothesis: The primitives underlying neuronal computation are 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

7. Primary computational units: Neurons From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991, pg. 21

8. Basic Input-Output Transformation Input Spikes Output Spike (Excitatory Post-Synaptic Potential)

9. Example of signaling in a sensory neuron From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991, pg. 28

10. 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 inside From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991, pg. 67

11. 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

13. 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

14. 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.

15. Neuronal Output: Action potentials • Voltage-gated channels cause action-potentials (spikes) • Rapid Na+ influx causes rising edge • Na+ pores deactivate • K+ outflux restores membrane potential • Positive feedback causes spike • Na+ influx depolarizes membrane, causing more Na+ influx From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991, pg. 110

16. Here is another illustration of the same concept. The permeability changes result in the large swings of membrane potential that are shown above.

17. 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.

19. [lower resistance]

20. 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

21. Schwann cells (glia) enable long-range spike communication Active wire allows lossless signal propagation, unlike electric signals in a copper wire Active Wiring: Myelination of axons From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991, pgs. 23 & 44

22. 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

23. Distribution of synapses on a real neuron…

24. postsynaptic element, such as another neuron

26. Synaptic biochemistryNote: even this isa gross simplification! From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991

27. Electron micrographs of synapses Synaptic Vesicles and Synaptic Cleft Fusion of vesicles and release of neurotransmitter Retrieval and formation of new vesicles From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991

28. 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

29. 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 observed when the same presynaptic input is activated before and after LTP

30. 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

31. Types of Synaptic Plasticity

32. Examples of measured synaptic plasticity Hebbian LTP - and + are thresholds for LTD and LTP respectively

33. Spike-Timing Dependent Plasticity • Amount of increase/decrease in synaptic strength (LTP/LTD) depends on relative timing of pre/postsynaptic spikes pre after post pre before post LTP LTD

34. 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…

35. 5 minute break… Next: Brain organization and information processing in networks of neurons

36. Brain Spinal Cord Somatic Autonomic Organization of the Nervous System Central Nervous System Peripheral Nervous System

37. Skeletal/Somatic Nervous System Nerves that connect to voluntary skeletal muscles and to sensory receptors Afferent Nerve Fibers Axons that carry info away from the periphery to the CNS Efferent Nerve Fibers Axons that carry info from the CNS outward to the periphery

38. Autonomic and Central Nervous System • Autonomic: Nerves that connect to the heart, • blood vessels, smooth muscles, and glands • CNS: Brain + Spinal Cord • Spinal 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

39. T h a l a m u s l C t H y p o t h a l a m u s C o r p u s c o l l o s u m P o n s C e r e b e l l u m M e d u l l a S p i n a l c o r d Major Brain Regions: Brain Stem Medulla Breathing, muscle tone and blood pressure Pons Connects brainstem with cerebellum & involved in sleep and arousal C e r r Cerebellum Coordination of voluntary movements and sense of equilibrium

40. T h a l a m u s l C H y p o t h a l a m u s C o r p u s c o l l o s u m P o n s C e r e b e l l u m M e d u l l a S p i n a l c o r d Major Brain Regions: Brain Stem Midbrain Eye movements, visual and auditory reflexes • Reticular Formation • Modulates muscle reflexes, breathing & pain perception. Also regulates sleep, wakefulness & arousal Midbrain

41. T h a l a m u s r e b r a l C o r t e x H y p o t h a l a m u s C o r p u s c o l o s P o n s C e r e b e l l u m M e d u l l a d S p i n a l c o r Major Brain Regions: Diencephalon Thalamus Relay station for all sensory info (except smell) to the cortex Hypothalamus Regulates basic needs fighting, fleeing feeding, and mating e Corpus callosum

42. T h a l a m u s r e b r a l C o r t e x H y p o t h a l a m u s C o r p u s c o l l o s u m P o n s C e r e b e l l u m M e d u l l a S p i n a l c o r d 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 Cerebrum/Cerebral Cortex

43. Cerebral cortex comprises a sheet of neurons • 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 From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991, pgs.

44. Summary From Kandel, Schwartz, Jessel, Principles of Neural Science, 3rd edn., 1991

45. 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

46. Broca’s Area Important in the production of speech Wernicke’s Area Important in the comprehension of language The brain is specialized by region: Language

47. Specialization of function in Cerebral Cortex somatosensory cortex Motor Planning, Higher cognitive functions Spatial reasoning and motion Visual and auditory recognition Visual Processing

48. 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

49. 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

50. 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