**Courses: ** BioE332

Large-scale neural modeling

** ****Catalog Description:** Emphasis is on cortical computation, from feature maps in the neocortex to episodic memory in the hippocampus, looking at the role of recurrent connectivity, rhythmic activity, spike synchrony and synaptic plasticity, as well as noise and heterogeneity. Techniques to predict and quantify network behavior introduced in lectures are applied to data recorded from models simulated in labs. Models are run in real-time on neuromorphic hardware developed for this purpose, facilitating learning and discovery by swiftly exploring the model's parameter space. Students develop and

simulate their own large-scale models in the second quarter of this course-sequence.

**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:** Psych 120, Math 51, Stats 110. Biology students with no background in engineering are welcome. Engineering students should have an introductory neuroscience course. 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 2007

#### 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 Plasticity with Poisson Spike Trains

Lecture 15 Enhancing Synchrony

Lecture 16 Sources of Variability

Lecture 17 Storing Patterns

Lecture 18 Recall Performance

#### Labs

Lab 1 Synapse LabLab 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 1A. 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.

### BioE332B—Spring 2007

#### Class notes

Lecture 1 Neuron Parameters

Lecture 2 Network Parameters

Lecture 3 Example Set-ups

Lecture 4 Neuronal Experimental Techniques—*Shaul Hestrin*

Lecture 5 Analyzing Multineuron Data—*Krishna Shenoy*

Lecture 6 System-Level Experimental Techniques—*Tirin Moore*

Lecture 7 Retina Model—*Kwabena Boahen *

Lecture 8 V1 Model—*Paul Merolla*

Lecture 9 Multicompartment Models—*Paul Rhodes*