CNI Speaker Series: Pankaj Mehta

Pankaj Mehta
Department of Physics
Boston University

Understanding collective behavior in cellular populations: from neurons to social amoeba

It is now possible to simultaneously measure the activity of large cellular populations. An important question is how to make sense of this "high dimensional data". In the case of neurons, one approach proposed for analyzing this data is the use of Maximum Entropy Models. Using such an analysis, it has been suggested that neural populations in the retina may be poised near criticality and exhibit a feature known as Zipf’s law. Here we present analytical arguments and numerical simulations showing that such “critical behavior” naturally arises in systems with unobserved random variables -- such as a common input stimulus to a neural population -- that affect the observed degrees of freedom. We then analyze retinal population activity in response to a natural movie and demonstrate that the signatures of criticality indeed arise from our mechanism, suggesting the limitations of using static Maximum Entropy models for modeling data with time-dependent inputs. In the second part of the talk (time-permitting), I will show how a canonical model from computational neuroscience, the FitzHugh-Nagumo model, can be used to understand the emergence of noise-induced collective oscillations in populations of the social amoeba Dictyostelium discoideum.


A pizza lunch will be served.