The influence of attractor topology on seizure initiation in the hippocampal region

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The goal of this research project is to elucidate how the computational purpose of different hippocampal circuits is related to their susceptibility for seizures. In particular, this grant is intended to partially fund a researcher who would focus on using machine learning approaches to connect computational purpose with seizure susceptibility in various neural networks. Indeed, I have been able to hire a machine learning expert with the help of this grant, but instead of a graduate student, a postdoc named Dr. Ismaeel Ramzan has joined my group. He has made considerable progress towards the aims of this grant.Dr. Ramzan has devised two machine learning tasks that mimic the computational role of two different hippocampal regions called CA3 and entorhinal cortex (EC). He has then applied a seizure-inducing perturbation upon the trained recurrent neural networks and has assessed their susceptibility to seizures. Preliminary results indicate that EC-like networks are at greater risk for developing seizures. Moreover, Dr. Ramzan has begun to probe the underlying mechanisms that connect task structure with seizure susceptibility. He does so by inferring the energy landscape of trained networks and comparing the topologies of the low-energy manifolds between the two cases.