**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.

**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

#### 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
.