Neural correlates of flexible cognition

Abstract: New probes now allow high density recordings of activity in primates. We report on findings from two collaborations with the Buffalo lab in which primates were trained to perform complex tasks, in which we explore the emergence of schemas. In the first study, macaque monkeys navigated in a visually rich virtual reality environment. We find that while many neurons in hippocampus show place-like responses, the population as a whole tends to primarily encode relevant task epochs.

ANNULÉ - How do natural neuronal networks deal with noise?

The retina is a dense layered network of neurons that transforms incoming light from visual scenes into noisy spiking activity. This transformation is highly non-linear and has not been fully characterized yet. In particular, the activity of the different output neurons is noisy and correlated. There is at the moment no consensus on the extent and purpose of the correlations observed in the population response, some studies stating that they can be beneficial whilst others showing otherwise. In this talk I will discuss some recent results to answer this question.

Rethinking behavior in the light of evolution

Abstract: In psychology and neuroscience, the human brain is usually described as an information processing system that encodes and manipulates representations of knowledge to produce plans of action. This view leads to a decomposition of brain functions into putative processes such as object recognition, memory, decision-making, action planning, etc., inspiring the search for the neural correlates of these processes. However, neurophysiological data does not support many of the predictions of these classic subdivisions.

Sequences and modularity of dynamic attractors in inhibition-dominated neural networks

Résumé : Threshold-linear networks (TLNs) display a wide variety of nonlinear dynamics including multistability, limit cycles, quasiperiodic attractors, and chaos. Over the past few years, we have developed a detailed mathematical theory relating stable and unstable fixed points of TLNs to graph-theoretic properties of the underlying network. These results enable us to design networks that count stimulus pulses, track position, and encode multiple locomotive gaits in a single central pattern generator circuit.

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