Courses: BioE332
Large-scale neural modeling

Catalog Description: Emphasis is on modeling neural systems at the circuit level, ranging from feature maps in neocortex to episodic memory in hippocampus. Simulation exercises to explore the roles of cellular properties, synaptic plasticity, spike synchrony, rhythmic activity, recurrent connectivity, and noise and heterogeneity; quantitative techniques to analyze and predict network behavior; modeling projects to study neural systems of interest (second half of two-quarter sequence). Work in teams of two; run models in real-time on neuromorphic hardware developed for this purpose.

Modeling Project BioE332 students, Sridhar Devarajan (Neurosci) and Brian Percival (EE), demoing their modeling project on dynamic routing through neuronal coherence. [Winter 2007]

Course sequence: BioE332A, the first quarter of this course-sequence, is based on weekly three-hour labs (simulation exercises) performed in groups of two. Accompanying lectures provide the background needed to understand and perform these labs. BioE332B, the second quarter of this course-sequence, builds on these lessons through a quarter-long modeling project. Accompanying guest lectures introduce relevant background, ranging from data analysis to experimental techniques.

Prerequisites: Biology students should have a differential equations course (e.g., Math 42); no background in engineering is required. Engineering students should have a neurobiology course (e.g., Bio 20); otherwise the instructor's permission is required. Undergraduates need the instructor's permission.

Goals: Link structure to function by developing multilevel computational models of the nervous system. These models are studied in weekly lab exercises.

Target Audience: This course is intended to draw students from multiple disciplines with an interest in interdisciplinary approaches. Students are encouraged to pool their expertise in different areas by working in groups.

BioE332A—Winter 2008

Outline
Calendar

Class notes

Lecture 1 Overview
Lecture 2 Synapse
Lecture 3 Integrate-&-Fire Neuron
Lecture 4 Positive Feedback
Lecture 5 Adaptive Neuron
Lecture 6 Bursting Neuron
Lecture 7 Phase Response
Lecture 8 Phase Locking
Lecture 9 Synchrony Intro
Lecture 10 Delay Model of Synchrony
Lecture 11 Synchrony and Entrainment
Lecture 12 Binding
Lecture 13 Spike Timing-Dependent Plasticity
Lecture 14 Feedforward Synapses
Lecture 15 Enhancing Synchrony
Lecture 16 Sources of Variability
Lecture 17 Storing Patterns
Lecture 18 Recall Performance
Lecture 19 Hardware
Lecture 20 To be determined

Labs

Lab 1 Synapse Lab
Lab 2 Neuron Lab
Lab 3 Adapting–Bursting Lab
Lab 4 Phase Response Lab
Lab 5 Synchrony Lab
Lab 6 Binding Lab
Lab 7 STDP Lab
Lab 8 Plasticity Enhanced Synchrony Lab
Lab 9 Associative Recall

Readings

Lab 1
A. Destexhe, Z. Mainen, and T. Sejnowski. An efficient method for computing synaptic conductances based on a kinetic model of receptor binding. Neural Computation , 6(1):14-8, 1994.

Lab 2
E. M. Izhikevich. Dynamical systems in neuroscience: The geometry of excitability and bursting. MIT Press, 2007, Chapter 3, pp. 53-82 (preprint).

Lab 3
E. M. Izhikevich. Dynamical systems in neuroscience: The geometry of excitability and bursting. MIT Press, 2007, Section 7.3, pp. 252-63 (preprint).

Lab 3
E. M. Izhikevich. Dynamical systems in neuroscience: The geometry of excitability and bursting. MIT Press, 2007, Section 9.2, pp. 335-47 (preprint).

Lab 4
E. M. Izhikevich. Dynamical systems in neuroscience: The geometry of excitability and bursting. MIT Press, 2007, Section 10.1, pp. 444-57.

Lab 4
E. M. Izhikevich. Dynamical systems in neuroscience: The geometry of excitability and bursting. MIT Press, 2007, Section 10.4.2, pp. 477-9 .

Previous Years: 2007