CSSS 2006 Neuroscience - experiment and physiology

Lecturer: Rob de Ruyter



Outline:

  • 1. Physics and physiology of neurons

    You must understand something about neural hardware to understand neural processing, and we can't avoid introducing some terminology. So I propose to have one lecture on the physics and physiology of neurons, with a bit of jargon, along the following lines:

    a. Introduction to basic mechanisms and molecular underpinnings: Physical limits and constraints, basic physiology
    b. Neural signals: Graded responses, action potentials, propagation of signals
    c. Processes at longer time scales: Neuromodulators, plasticity

  • 2 . Sensitivity and statistical efficiency in early vision

    The analysis of sensory systems at the front end has taught us a lot about principles underlying optimization of information transmission. At this level it is relatively easy to understand physical and physiological constraints on the system. That makes it possible to quantify the efficiency of information processing. A little bit of linear systems analysis goes a long way toward understanding these issues, and will be useful background if Bruno and/or Tony want to discuss optimal filtering etc.

    a. Linear systems
    b. Design principles in sensory systems, in particular vision: - design of compound eyes - photon counting - signal and noise in phototransduction
    b. Optimal preprocessing in the retina - adaptation and absolute efficiency at higher light levels
    c. Simple nonlinearities: - optimizing photon counting in a convergent system - motion detection as an example of nonlinear neural computation -

  • 3 - Information processing in a real and uncertain world

    We probably all agree that we won't understand sensory information processing until we've seen what happens in natural conditions. Signals are much more complicated, and tasks much less well defined compared to the laboratory environment. What are the consequences of that, and how can we make progress along these lines, both experimentally and theoretically.

    a. Can we take a principled approach to information processing in biological systems? - Predictive coding - Gain matching
    b. Adaptation, illusions, and possible function
    c. Example of neural computation: Motion estimation as an optimal estimation strategy in real-world conditions

    Literature:

    (Most of these I have electronically, those with * not yet)
  • Aho, AC, Donner, K, Helenius, S, Olesen Larsen L, Reuter T: Visual performance of the toad (Bufo bufo) at low light levels: retinal ganglion cell responses and prey catching accuracy. J Comp Physiol A 172: 671-682 (1993)
  • * Aidley, D.J.: The Physiology of Excitable Cells, 3d ed, Cambridge University Press (1990)
  • Barlow, HB: Critical factors in the design of the eye and the visual cortex. The Ferrier Lecture 1980. Proc R Soc Lond B 212, 1-34 (1981)
  • Barlow, HB: Redundancy reduction revisited. Network: Comput Neural Syst 12 241Ð253 (2001)
  • Baylor, D. How Photons Start Vision. PNAS 93-2, 560-565 (1996)
  • Brenner N, Bialek W, de Ruyter van Steveninck RR: Adaptive Rescaling Maximizes Information Transmission. Neuron 26:695-702, (2000)
  • * Bullock TH: The reliability of neurons. J Gen Physiol 55:565-584 (1970)
  • Fairhall AL, Lewen GD, Bialek W, de Ruyter van Steveninck RR. Efficiency and ambiguity in an adaptive neural code. Nature 412: 787-792, (2001)
  • * Feynman, Leighton and Sands: Lectures on Physics, Vol I, Sect 36-4, Addison Wesley (1963)
  • Field GD, Rieke F: Nonlinear Signal Transfer from Mouse Rods to Bipolar Cells and Implications for Visual Sensitivity. Neuron 34, 773Ð785 (2002)
  • van Hateren JH: Spatiotemporal contrast sensitivity of early vision. Vision Res 33, 257-267 (1993)
  • Hecht S, Shlaer S, Pirenne MH: Energy, Quanta, and Vision. J Gen Physiol 25, 819-840 (1942)
  • Hodgkin AL, Huxley AF: A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117, 500-544 (1952)
  • * Laughlin, SB: A simple coding procedure enhances a neuron;s information capacity. Z Naturforsch 36c, 910-912 (1981)
  • Lewen GD, Bialek W, de Ruyter van Steveninck RR: Neural coding of naturalistic motion stimuli. Network: Computation in Neural Systems 12:317-329 (2001)
  • * Reichardt W: Autocorrelation, a principle for the evaluation of sensory information by the central nervous system. In: Rosenblith WA (ed) Principles of Sensory Communication. John Wiley, New York, NY, 303-317 (1961)
  • * Rieke F, Warland D, de Ruyter van Steveninck R, Bialek W: Spikes: Exploring the Neural Code. MIT Press, Cambridge (1997)
  • de Ruyter van Steveninck R, Bialek W: Real-time Performance of a Movement-Sensitive Neuron in the Blowfly Visual System: Coding and Information Transfer in Short Spike Sequences. Proc R Soc Lond, B 234, 379-414 (1988)
  • de Ruyter van Steveninck R, Bialek W: Timing and counting precision in the blowfly visual system. In: Methods in Neural Networks IV (J. van Hemmen, J.D. Cowan, E. Domany, eds.). Springer Verlag, Heidelberg, New York, pp 313-371 (2001)
  • Shannon CE: A mathematical theory of communication. Bell Syst Techn J 27:379-423 and 623-656 (1948)
  • Sigworth FJ: Life's transistors. Nature 423, 21-22 (2003)
  • Srinivasan MV, Laughlin SB, Dubs A: Predictive coding: A fresh view of inhibition in the retina. Proc. R. Soc. London B 216: 427Ð59 (1982)