1. 08.11.051 CS 621 Artificial Intelligence Lecture 34 - 08/11/05 Guest Lecture by Prof. Rohit Manchanda Biological Neurons - II

2. 08.11.052 The human brain Seat of consciousness and cognition Perhaps the most complex information processing machine in nature Historically, considered as a monolithic information processing machine

3. 08.11.053 Beginner’s Brain Map Forebrain (Cerebral Cortex): Language, maths, sensation, movement, cognition, emotion Cerebellum: Motor Control Midbrain: Information Routing; involuntary controls Hindbrain: Control of breathing, heartbeat, blood circulation Spinal cord: Reflexes, information highways between body & brain

4. 08.11.054 Brain : a computational machine? Information processing: brains vs computers - brains better at perception / cognition - slower at numerical calculations Evolutionarily, brain has developed algorithms most suitable for survival Algorithms unknown: the search is on Brain astonishing in the amount of information it processes –Typical computers: 10 9 operations/sec –Housefly brain: 10 11 operations/sec

5. 08.11.055 Brain facts & figures Basic building block of nervous system: nerve cell (neuron) ~ 10 12 neurons in brain ~ 10 15 connections between them Connections made at “synapses” The speed: events on millisecond scale in neurons, nanosecond scale in silicon chips

6. 08.11.056 Neuron - “classical” Dendrites –Receiving stations of neurons –Don't generate action potentials Cell body –Site at which information received is integrated Axon –Generate and relay action potential –Terminal Relays information to next neuron in the pathway http://www.educarer.com/images/brain-nerve-axon.jpg

7. 08.11.057 Membrane Biophysics: Overview Part 1: Resting membrane potential

8. 08.11.058 Resting Membrane Potential Measurement of potential between ICF and ECF –V m = V i - V o –ICF and ECF at isopotential separately. –ECF and ICF are different from each other. + _ ICF ECF -40 to -90 mV

9. 08.11.059 Resting Membrane Potential - recording Electrode wires can not be inserted in the cells without damaging them (cell membrane thickness: 7nm) –Solution: Glass microelectrodes (Tip diameter: 10 nm) Glass  Non conductor Therefore, while pulling a capillary after heating, it is filled with KCl and tip of electrode is open and KCl is interfaced with a wire. ICF ECF -40 to -90 mV + _ KCl

10. 08.11.0510 R.m.p. - towards a theory Ionic concentration gradients across biological cell membrane Mammalian muscle (rmp = -75 mV) ECFICF Cations Na + 145 mM12 mM K+K+ 4 mM155 mM Anions Cl - 120 mM4 mM Frog muscle (rmp = -80 mV) ECFICF Cations Na + 109 mM4 mM K+K+ 2.2 mM124 mM Anions Cl - 77 mM1.5 mM

11. 08.11.0511 R.m.p. - towards a theory Ionic concentration gradients: squid axon (rmp = -60 mV) 40 mM550 mMCl - Anions 400 mM10 mMK+K+ 50 mM440 mMNa + Cations ICFECF 3325Cl o /Cl i Anions 5652K i /K o 2812Na o /Na i Cations FrogMammal Ionic concentration ratios

12. 08.11.0512 Ionic concentration ratios across biological cell membranes 1000.021,065Paramecium 1.040.90.5Nitella Plant cells 1830Eel electroplaque 2532Cat cardiac 1252Rat cardiac 332856Frog sartorius muscle 4360Frog nerve 38Crab axon 1136Cuttlefish axon 14940Squid axon Cl o /Cl i Na o /Na i_ K i / K o Species, tissue

13. 08.11.0513 Trans-membrane Ionic Distributions

14. 08.11.0514 Resting potential as a K + equilibrium (Nernst) potential

15. 08.11.0515 Resting Membrane Potential: Nernst Eqn Consider values for typical concentration ratios E K = -90 mV E Na = +60 mV r.m.p. = {-60 to –80} mV

16. 08.11.0516 Goldman-Hodgkin-Katz (GHK) eqn Taking values of R,T &F and dividing throughout by P K : Consider  = V. large, v. small, and intermediate

17. 08.11.0517 Equivalent Circuit Model: Resting Membrane C m g K g Na E E K V m Out In

18. 08.11.0518 Equivalent Circuit Model including Na pump EKEK E Na I K(p) g Na gKgK VmVm Out In I Na(p) CmCm

19. 08.11.0519 Membrane Biophysics: Overview Part 2: Action potential

20. 08.11.0520 ACTION POTENTIAL

21. 08.11.0521 ACTION POTENTIAL: Ionic mechanisms

22. 08.11.0522 Action Potential: Na + and K + Conductance

23. 08.11.0523 Action Potential Propagation Non decremental, constant velocity S RR R RR X = 0 1 2 3 4 V Th Time Convex Concave

24. 08.11.0524 Hippocampus: location

25. 08.11.0525 Hippocampal Network & Connections

26. 08.11.0526 Membrane Biophysics: Overview Part 3: Synaptic transmission & potentials

27. 08.11.0527 “Canonical” neurons: Neuroscience

28. 08.11.0528 Synapses : Chemical & Electrical

29. 08.11.0529 Transmission : Chemical & Electrical

30. 08.11.0530 Receptors Neurotransmitter Chemical Transmission

31. 08.11.0531 Postsynaptic Electrical Effects

32. 08.11.0532 Synaptic Integration: The Canonical Picture Action potential Action potential: Output signal Axon: Output line

33. 08.11.0533 The Perceptron Model A perceptron is a computing element with input lines having associated weights and the cell having a threshold value. The perceptron model is motivated by the biological neuron. Output = y wnwn W n-1 w1w1 X n-1 x1x1 Threshold = θ

34. 08.11.0534 Step function / Threshold function y = 1 for Σ > θ, y >1 =0 otherwise Σw i x i > θ θ 1 y ΣwixiΣwixi Features of Perceptron Input output behavior is discontinuous and the derivative does not exist at Σw i x i = θ Σw i x i - θ is the net input denoted as net Referred to as a linear threshold element - linearity because of x appearing with power 1 y= f(net): Relation between y and net is non-linear

35. 08.11.0535 Perceptron / ANN “neuron” Neurophysiological basis of: a)Input Signs b)Input Weights

36. 08.11.0536 Dendrites & Synapses in Real Life !

37. 08.11.0537

38. 08.11.0538 Neuron morphologies

39. 08.11.0539 In the Retina …