Courses: BioE332
Large-scale neural modeling

Catalog Description: Large-scale models link cellular properties, columnar microcircuits, recurrent connectivity, and feedback projections to experimentally studied behaviors such as selective attention and working memory. Emphasis is on exploring spike-based communication and biophysics-based computation through modeling to make experimentally testable predictions. In the first half of the course, students review and implement large-scale models in the literature. In the second half of the course, students extend these models or develop large-scale models of their own. Students work in teams of two and run models with up to a million neurons in real-time on Neurogrid—a neuromorphic simulation platform developed at Stanford that delivers supercomputer-level performance.

Modeling Project BioE332 students, Sridhar Devarajan (Neurosci) and Brian Percival (EE), demoing their modeling project on dynamic routing through neuronal coherence. [Winter 2007]

Course Details: Based on modeling projects. Accompanying lectures provide background on systems neuroscience and on modeling techniques.

Prerequisites: A course in neuronal circuits (e.g., BIO 217, NBIO 258), or systems neurobiology (e.g., PSYCH 209A, NBIO 220), or computational neuroscience (e.g., NENS 220).

Goals: Link structure to function by developing circuit-level computational models of the nervous system.

Target Audience: This course is targeted to students already exposed to systems neuroscience and computational methods wishing to learn how to build multiscale models that link neuronal biophysics to neural circuits to cognitive behavior.

BioE332—Spring 2011



Class Time: Weds & Fri 12:50-2:05pm
Location: CCSR 4205
Office Hours:
- Professor Boahen: Tue 12-1
- TA (Peiran Gao): Mon 1-4

Class notes:

Week 1:
Lecture: Synchrony, Synchrony II
Discussion: Modeling background
Reading: Jonas et al.
Assignment: Synchrony
Extension Presentation: Dethier & Ye, Iyer & Macklin

Week 2:
Lecture: Working memory
Discussion: Synchrony modeling results
Reading: Chow et al.
Assignment: Bumps and Working Memory
Extension Presentation: Kitch & Jordanova

Week 3:
Lecture: Balanced networks
Discussion: Working memory results
Reading: Aertsen et al., Brunel et al.
Assignment: IF Neuron and Balanced Network

Week 4:
Lecture: Neurogrid tutorial I
Discussion: Neurogrid tutorial II

Week 5:
Lecture: Neurogrid tutorial III
Discussion: Balanced network results

Week 6:
Lecture: Project proposal presentations

Week 7: No lecture/discussion

Week 8:
Discussion: Project progress presentations
Discussion: Project progress presentations

Week 9: No lecture/ discussion

Week 10 (Finals week):
Discussion: Final project presentations
Discussion: Final project presentations

Previous Years: