G63.2855.001 / G80.3400.001 -- Fall, 2005

Special Topics in Mathematical Physiology:
Computational Modeling of Neuronal Systems

Instructors: John Rinzel & Eero Simoncelli
Time: Wed 9:30-11:20 am
Location: Warren Weaver Hall, rm 1314 (subject to change)
Prerequisites: linear algebra, linear systems theory, basic probability/statistics, differential equations. Some matlab programming experience.

Brief Description:

We will survey various approaches to modeling of neuronal systems, from components of individual cells to populations of interacting neurons, and from models of physiological mechanisms to more abstract models of information encoding and decoding. A central theme will be the role of theory in neuroscience, in generating testable hypotheses, providing conceptual and methodological frameworks for the analysis and interpretation of experimental data and developing of new experimental methods. Questions to be addressed include: how do we characterize the observed responses or identify the computations of particular neurons or neuronal circuits; how do the responses/ computations evolve dynamically; how are they implemented in neural ware; and how are they manifested in human/animal behaviors?

Schedule (updated incrementally):

Date Lecturer Topic Handouts Homework
7 Sep E. Simoncelli Intro to theory in neuroscience: descriptive vs. mechanistic vs. interpretive.
Descriptive models of neural encoding: linear/nonlinear cascade 		Course description
Student info sheet
Lecture slides 		 
14 Sep J. Rinzel Nonlinear neuronal dynamics:
excitability and oscillations, HH and networks 		Reference chapter
Lecture slides 		HW 1
21 Sep J. Rinzel Spontaneous activity in developing spinal cord;
firing rate and LIF network models 		Lecture slides  
28 Sep E. Simoncelli Encoding models II: Poisson spiking, Fitting and validating LNP models. Integrate-and-fire Book chapter
Lecture slides 		 
5 Oct E. Simoncelli / J. Pillow Generalized Integrate-and-fire encoding models: fitting and validation. Lecture slides
Journal article 		 
12 Oct D. Cai Large-scale model of cortical area V1. Lecture slides HW 2, due 19 Oct
19 Oct D. Tranchina Synaptic depression: from stochastic to rate model;
application to a model of cortical suppression. 		Carandini etal, 2002
Lecture slides 		HW 3, due 02 Nov
26 Oct J. Rinzel Precise temporal integration in the auditory brainstem. Lecture slides  
2 Nov J. Mancilla / R. Moreno Networks: Feedforward propagation, correlations from shared inputs Lecture slides: pdf, ppt  
9 Nov A. Reyes / B. Doiron Feedforward propagation in layered networks Reyes slides: pdf, ppt
 Doiron slides (pdf) 		 
16 Nov No Class
[due to annual Society for Neuroscience meeting] 		     
23 Nov No Class
[due to Thanskgiving] 		     
30 Nov E. Simoncelli Decoding: concepts and methods. Lecture slides  
7 Dec P. Glimcher Neurobiology of decision making. Lecture slides: pdf, ppt  
14 Dec E. Shea-Brown A network model for decision-making and time estimation. Lecture slides  
21 Dec Registered students Oral presentation of class projects    

Relevant Books:

  • Theoretical Neuroscience , by Dayan and Abbott. MIT Press, 2001.
  • Spikes: Exploring the Neural Code, by Rieke, Warland, de Ruyter, & Bialek. MIT Press, 1997.
  • Spiking Neuron Models: Single Neurons, Populations, Plasticity, by Gerstner and Kistler. Cambridge University Press, 2002.
  • Biophysics of Computation, by Koch. Oxford University Press, 1999.

    Vision (biological):

  • Eye, Brain, and Vision, by David Hubel. Scientific American Library, 1995.
  • Foundations of Vision, by Brian Wandell. Sinauer Associates, 1995.