**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 explore the roles of cellular properties, synaptic plasticity, spike synchrony,
rhythmic activity, recurrent connectivity, and noise and heterogeneity; quantitative
techniques are introduced to analyze and predict network behavior. Students work in
teams of two and run models in real-time on neuromorphic hardware developed for this
purpose.

**Course Details:** 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. Modeling projects that build on these lessons can be performed in the
Spring Quarter through arrangement with the instructor.

**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 circuit-level 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 of two.

### BioE332—Winter 2010

#### Announcements

Class Time:
**Weds & Fri 12:50-2:05pm
**Location (as of 1/4/2010):

**Gates 100**

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

Lab Location: Alway 206

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 (Addendum)

Lecture 13 Spike Timing-Dependent Plasticity

Lecture 14 Feedforward Synapses

Lecture 15 Recurrent Synapses

Lecture 16 Limits of STDP

Lecture 17 Storing Patterns

Lecture 18 Recalling Patterns

Lecture 19 System Hardware

Lecture 20 Neurogrid

#### Labs

Lab 1 Synapse Lab (Block diagram - SVG, PNG)

Lab 2 Neuron Lab (Block diagram - SVG, PNG)

Lab 3 Adapting–Bursting Lab (Block diagram - SVG, PNG)

Lab 4 Phase Response Lab (Block diagram - SVG, PNG)

Lab 5 Synchrony Lab (Block diagram - SVG, PNG)

Lab 6 Attention Lab (Block diagram - SVG, PNG)

Lab 7 STDP Lab (Block diagram - SVG, PNG)

Lab 8 Plasticity Enhanced Synchrony Lab (Block diagram - SVG, PNG)

Lab 9 Associative Recall (Block diagram - SVG, PNG)

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.

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