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Center for Nonlinear Studies |
High-fidelity information propagation can be crucial for information processing in neural systems. This is the case particularly in systems with no clocks, such as biological neuronal networks or analog neuromorphic systems. In this talk, I will discuss the synfire-gated synfire chain (SGSC), a pulse-gating mechanism that is capable of faithfully propagating graded (firing rate) information in networks of spiking neurons. After first presenting the SGSC and giving an understanding of why it is robust, I will show how SGSCs can be used to process information and make decisions, resulting in a framework that is sufficiently powerful for general computation. Then I will show how SGSCs can be deployed in concert with Hebbian synaptic plasticity in an algorithm capable of learning a stochastic process and making predictions of future process values. Trained as a theoretical physicist, Andrew did two postdocs in Cosmology (Univ of Cambridge and Fermilab) before switching fields to Neuroscience. After a postdoctoral fellowship and research assistant professorship at Mt Sinai School of Medicine, he moved to a joint position in Mathematics and Engineering at the University of Georgia, where he was tenured as an associate professor. He then followed his wife(a zoo and wildlife veterinarian) to UC Davis, where he is a Research Professor in the Math Department. His research has focused on computing with neural and neuromorphic systems and quantum simulations on quantum computers.
Host: Garrett Kenyon