9.29 Introduction to Computational Neuroscience (2004)

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Lecture schedule

• Lecture 1 (T 2/3) (pdf, realvideo)
  • Introduction
  • Examples of neural coding. Simple linear regression.
  • Dayan and Abbott, Section 1.1
  • R. Wessel, C. Koch, F. Gabbiani, Coding of time-varying electric field amplitude modulations in a wave-type electric fish, J. of Neurophysiology, 75(6), 2280-93 (1996).
  • Fitting Data to a Straight Line, Numerical Recipes in C
• Lecture 2 (Th 2/5) (pdf, realvideo)
  • Convolution and correlation I.
  • Firing rate.
  • Dayan and Abbott, Sections 1.2-1.3
  • Convolution and Deconvolution Using the FFT, Numerical Recipes in C
• Optional Lecture 1 (M 2/9, 7pm, E25-401) (pdf, realvideo))
  • Initializing and using vectors and matrices in MATLAB. Matrix shortcuts. Plots in MATLAB. Useful commands.
  • Simple statistics and linear regression.
• Lecture 3 (T 2/10) (pdf, realvideo)
  • Convolution and correlation II.
  • Spike-triggered average.
  • Wiener-Hopf equations and white noise analysis
  • Dayan and Abbott, Sections 2.1-2.2
  • Correlation and Autocorrelation Using the FFT, Numerical Recipes in C
• Lecture 4 (Th 2/12) (realvideo) Assignment #1 due!
  • Visual receptive fields I
    
  • Basics of the visual system. Center-surround receptive fields. Simple and complex cortical cells.
    
  • S. E. Palmer, Vision Science - photons to phenomenology, 146-154, The MIT Press, Cambridge, Massachusetts (1999).
  • Dayan and Abbott, Sections 2.3-2.6
  • Website: Space-Time Receptive Fields of Visual Neurons
• no class (T 2/17, M schedule)
• Optional Lecture 2 (Tu 2/17) (realvideo) 
  • Probability theory.
• Lecture 5 (Th 2/19) (realvideo) Assignment #2 due!
• Visual receptive fields II
  
• Optional Lecture 3 (M 2/23) (realvideo)
  • Markov processes.
• Lecture 6 (T 2/24) (pdf, realvideo)
• Operant matching I
  
• C. Gallistel, T. Mark, A. King, and P. Latham, The Rat Approximates an Ideal Detector of Changes in Rates of Reward, Journal of Experimental Psychology: Animal Behavior Processes, 2001, 27: 354-372.
• R. Herrnstein, On the Law of Effect, Journal of the Experimental Analysis of Behavior, 1970, 13: 243-266, Number 2 (March).
• Lecture 7 (Th 2/26) (realvideo) Assignment #3 due!
• Operant matching II
  
• H. S. Seung, Matching and maximizing are two ends of a spectrum of policy search algorithms
• R. Herrnstein and D. Prelec, Melioration: A Theory of Distributed Choice, The Journal of Economic Perspectives, Volume 5, Issue 3 (Summer, 1991), 137-156.
• Code for implementation of parallel lines task
• Lecture 8 (T 3/2) (realvideo)
  • Games I
    
  • C .Camerer,Behavorial Game Theory
• Optional Lecture 4 (M 3/01, ) (realvideo)
  • Linear stability analysis
    
• Lecture 9 (Th 3/4) (realvideo)
  • Games II
    
  • A. Sanfey, J. Rilling, J. Aronson, L. Nystrom, and J. Cohen, The Neural Basis of Economic Decision-Making in the Ultimatum Game, 2003 Jun 13, Science, 300(5626):1755-8.
  • C. Camerer, Strategizing in the Brain, 2003 Jun 13, Science, 300(5626):1673-5.
• Project meeting 1 (T 3/9)
  Discussion of topics; choice of projects; work begins.
• Project meeting 2 (Th 3/11) Assignment #4 due!
• Project meeting 3 (T 3/16)
• Project meeting 4 (Th 3/18)
• Spring break, 3/22-26
• Project presentations I (T 3/30)(realvideo)
• Project presentations II (Th 4/1) (realvideo)
• Lecture 10 (T 4/6) (realvideo)
  • Ion channels. Nernst equation. Passive electrical properties of neurons.
  • Dayan and Abbott, Section 5.2.
  • D. Johnston and S. Miao-Sin Wu, Foundations of Cellular Neurophysiology, Chapter 2, The MIT Press, Cambridge, Massachusetts, London, England (1995)
• Lecture 11 (Th 4/8) (realvideo)
  • The action potential. Hodgkin-Huxley model I.
    
  • Dayan and Abbott, Sections 5.3, 5.5, and 5.6
  • C. Koch, Biophysics of Computation, Information Processing in Single Neurons, Chapter 6, Oxford University Press, New York, Oxford (1999)
• Lecture 12 (T 4/13) (realvideo) Assignment #5 due!
• Hodgkin-Huxley model II
  
• Lecture 13 (Th 4/15) (realvideo)
  • A-type potassium channels, calcium-dependent potassium channels. 
  • Dayan and Abbott, Section 6.2.
• Patriot's Day (T 4/20)
• Lecture 14 (Th 4/22) (realvideo) Assignment #6 due!
  
• Synapses
  
• Dayan and Abbott, Section 5.8
• Optional Lecture 5 () (realvideo)
• Numerical methods for differential equations.
• Dayan and Abbott, Section 5.11 (Appendices A and B).
• A. Sherman, Lecture Notes and Lab Problems on Numerical Methods.
• Arthur Sherman's web page on Numerical Methods in Neuronal Modeling
• Lecture 15 (T 4/27) (realvideo)
• Associative memory I.
  
• Sebastian's 9.641 notes on the Hopfield Model
• J.J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proc. Natl. Acad. Sci. USA, 79, 2554-58
• Lecture 16 (Th 4/29) (realvideo) Assignment #7 due
  • Associative memory II.
    
  • More of Sebastian's notes on associative memory
  • Y. Miyashita, Neuronal correlate of visual associative long-term memory in the primate temporal cortex, Nature, 335, 817-20 (1988).
  • M. Griniasty, M. V. Tsodyks, and D. J. Amit, Conversion of temporal correlations between stimuli to spatial correlations between attractors, Neural Comput., 5, 1-17 (1993).
  • D. J. Amit, The Hebbian paradigm reintegrated: local reverberations as internal representations, Behav. Brain Sci., 18, 617-26 (1995).
  • K. Nakazawa, M. C. Quirk, R. A. Chitwood, et. al. Requirement for Hippocampal CA3 NMDA Receptors in Associative Memory Recall, Science, 297, 211-218 (2002).
• Lecture 17 (T 5/4) (realvideo)
  • Decisionmaking
    
• Projects (Th 5/6)
• Projects (T 5/11)
• Review (Th 5/13) (realvideo)
• Final exam (M 5/17, 1:30-4:30)