**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 2009

#### Announcements

Class Time:
**Weds & Fri 12:50-2:05pm
**Location (as of 1/6/09):

**McCullough 122**

Lab Time: Mon 2-5pm or Tues 8:30-11:30am

Lab Location: Alway 202C

Office Hours:

- Professor Boahen: Tuesday 12 - 1 pm (Clark W125)

- TA: Friday 2:10 - 3:30 pm (Clark W1.3, next to W125)

#### 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 Two-Neuron Interaction

Lecture 9 Synchrony and Inhibition

Lecture 10 Delay Model of Synchrony

Lecture 11 Attention Intro

Lecture 12 Attention and Neuromodulation

Lecture 13 Spike Timing-Dependent Plasticity

Lecture 14 Limits of STDP

Lecture 15 Recurrent Synapses

Lecture 16 Feedforward Synapses

Lecture 17 Storing Patterns

Lecture 18 Recalling Patterns

Lecture 19 System Hardware

Lecture 20 Neurogrid

#### 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 Attention Lab

Lab 7 STDP Lab

Lab 8 Plasticity Enhanced Synchrony Lab

Lab 9 Associative Recall

Lab 10 In-Depth Investigations

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

Lab 6

- B. Daniels.
*Synchronization of Globally Coupled Nonlinear Oscillators: the Rich Behavior of the Kuramoto Model*. Ohio Wesleyan Physics Dept., Essay, pp. 7-20, 2005.