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.
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
LabsLab 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
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.
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