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.
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
- Professor Boahen: Tuesday 12 - 1 pm (Clark W125)
- TA: Friday 2:10 - 3:30 pm (Clark W1.3, next to W125)
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
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
- 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.
- E. M. Izhikevich. Dynamical systems in neuroscience: The geometry of excitability and bursting. MIT Press, 2007, Chapter 3, pp. 53-82 (preprint).
- 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).
- 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.
- B. Daniels. Synchronization of Globally Coupled Nonlinear Oscillators: the Rich Behavior of the Kuramoto Model. Ohio Wesleyan Physics Dept., Essay, pp. 7-20, 2005.