Thursday, May 2, 2013

Suraj Keshri: May 8th

Title: Inferring neural connectivity

Abstract: Advances in large-scale multineuronal recordings have made it possible to study the simultaneous activity of complete ensembles of neurons. These techniques in principle provide the opportunity to discern the architecture of neuronal networks. However, current technologies can sample only small fraction of the underlying circuitry, therefore unmeasured neurons probably have a large collective impact on network dynamics and coding properties For example, it is well understood that common input plays an essential role in the interpretation of pairwise cross-correlograms. To infer the correct connectivity and computations in the circuit requires modelling tools that account for unrecorded neurons. We develop a model for fast inference of neural connectivity under the constraint that we only observe a subset of neurons in the population at a time.

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