Mathematical regularities of irregular neural codes for space

Abstract: Much of the thinking about neural population codes was motivated in the past decades by reports on neurons with highly stereotyped tuning functions. Indeed, neurons are often observed to exhibit smooth, unimodal tuning to an encoded variable, centered around preferred stimuli that vary across the neural population. Recent experiments, however, have uncovered neural response functions that are much less stereotyped and regular than observed previously. Some of the most striking examples have been observed in the hippocampus and its associated brain areas. The classical view has been that hippocampal place cells are active only in a compact region of space and exhibit a stereotyped tuning to position. In contrast to this expectation, however, place cells in large environments typically fire in multiple locations, and the multiple firing fields of individual cells, as well as those of the whole population, greatly vary in size and shape. We recently discovered that a remarkably simple mathematical model, in which firing fields are generated by thresholding a realization of a random Gaussian process, accounts for the statistical properties of place fields in precise quantitative detail. The model captures the statistics of field sizes and positions, and generates new quantitative predictions on the statistics of field shapes and topologies. These predictions are quantitatively verified in multiple recent data sets from bats and rodents, in one, two, and three dimensions, in both small and large environments. Together, these results imply that common mechanisms underlie the diverse statistics observed in the different experiments. The model further suggests that synaptic projections to CA1 are predominantly random, and that such random projections can produce a highly efficient neural code for space. If time permits I will present another recent work, in which we uncover simple principles underlying the spatial selectivity in a new class of neurons in the medial entorhinal cortex.

Bio: Yoram Burak is a Professor of Brain Sciences and Physics at the Hebrew University of Jerusalem, where he is the incumbent of the William N. Skirball Chair in Neurophysics, with appointments at the Racah Institute of Physics and the Edmond and Lily Safra Center for Brain Sciences (ELSC). He received his Ph.D. in theoretical physics with distinction from Tel-Aviv University in 2005, supervised by Prof. David Andelman. Before joining the Hebrew University in 2012, he was a Swartz postdoctoral fellow in theoretical neuroscience at the Center for Brain Science at Harvard University, where he worked with Haim Sompolinsky, Markus Meister, and others and, before that, a postdoctoral research associate at the Kavli Institute for Theoretical Physics at UCSB where he worked primarily with Boris Shraiman. His research seeks to identify how neural circuits in the brain implement computational functions.

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