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SHOUVAL, HAREL ZEEV Learning spatio-temporal statistics from the environment in recurrent networks EB022891
My lab is interested in how circuits of neurons can learn and represent the spatio-temporal dynamics of external stimuli. We have developed models with spiking neurons and local biophysically plausible learning rules that can accomplish this. In our framework the ability of circuits to accomplish this task depends a pre-existing local structure of cortical microcircuits. What we can offer experimentalists is a theoretical framework that can make sense of specific microcircuits circuits in brain, and make sense of specific synaptic plasticity observed experimentally in the brain. We make specific predictions about different classes of temporal profiles of cortical cells. We are interested in both electrophysiological recording results and calcium imaging results both from in vivo experiments and from slice experiments. These should come from experiments in which animals or slices were exposed to patterns or paradigms with temporal regularity over expended time periods of at least hundreds of milliseconds. What we can offer experimental labs is to use unsupervised dimensionality reduction and clustering methods in order to classify single cells within the networks, and correlation methods to uncover effective connectivity. We can use these results to help verify or reject our current models, and to generate hypothesis as to the circuit wide implications of the results. We are also interested in results related to synaptic plasticity in similar types of experiments, and especially in neuromodulator dependent synaptic plasticity. Here too we can analyze the data and offer model-tested hypotheses as to the implications of the experimental results for learning in circuits.