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Prof.
Department of Applied Mathematics, University of Washington
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Schedule
Thursday, November 18, 2021
5:00 PM Europe/Berlin
Domain
NeuroscienceHost
Tubingen Neuro Campus
Duration
70 minutes
There is an avalanche of new data on activity in neural networks and the biological brain, revealing the collective dynamics of vast numbers of neurons. In principle, these collective dynamics can be of almost arbitrarily high dimension, with many independent degrees of freedom — and this may reflect powerful capacities for general computing or information. In practice, neural datasets reveal a range of outcomes, including collective dynamics of much lower dimension — and this may reflect other desiderata for neural codes. For what networks does each case occur? We begin by exploring bottom-up mechanistic ideas that link tractable statistical properties of network connectivity with the dimension of the activity that they produce. We then cover “top-down” ideas that describe how features of connectivity and dynamics that impact dimension arise as networks learn to perform fundamental computational tasks.
Eric Shea-Brown
Prof.
Department of Applied Mathematics, University of Washington
Contact & Resources
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