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Biologically plausible neural algorithms for learning structured sequences

Stanford Neurosciences Institute, Chris Stock

Humans naturally learn to generate and process complicated sequential patterns. For example, a concert pianist can learn an enormous repertoire of memorized music. In neuroscience, it is widely thought that synaptic plasticity – the process by which the connections between neurons change response to experience – underlies such remarkable behavior. An important question is how to quantify the rules of synaptic plasticity which enable such flexible learning and recall of complex, dynamical sequences. During my exchange at EPFL, hosted by the computational neuroscience lab of Wulfram Gerstner, I will study plasticity rules in nonlinear recurrent network models to better understand biologically plausible algorithms by which neural systems can learn to generate complicated sequences.



Lead Researcher(s): 

Advisor: Surya Ganguli (applied physics)

EPFL exchange host: Carmen Sandi, Director, Brain Mind Institute Laboratory of Behavioral Genetics Brain Mind Institute

Funding Type: 
EPFL-Stanford Exchange
Award Year: