Topic: population vectors

In neuroscience, we record neural activity over time. We can try to make sense of this data by studying the (low-dimensional) space the activity is constrained to. To identify these spaces, one typically either treats the rows (neurons / spike trains) or the columns (timepoints / population vectors) of the activity matrix as points. In general, these two perspectives will not give the same space, obscuring the underlying structure of the data. Here, we provide a unified framework for thinking about this challenge in neural data. We show that if rows and columns are convex on the space(s),

Information theory provides a powerful theoretical framework for a quantitative understanding of the structure and function of neural circuits. Despite this, the application of these ideas to neuroscience and machine learning has been hampered by the fact that the computation and maximization of mutual information is only analytically tractable for simple coding problems, under the assumption of small network sizes and low dimensional stimuli. Using tools from the theory of spin-glasses we present a novel approach to estimating the entropy of large neural populations in response to arbitrary s

Passive odor exposure can change olfactory perception beyond simple pattern separation. Recent studies reveal that principal neurons in the mouse olfactory bulb exhibit bi-directional changes in population- level representations that depend on input similarity. Our analysis reveals the presence of a systematic drift, manifested as rotation of population vectors away from their initial representations. We explain these phenomena using a recurrent inhibition feedback model with unsupervised Hebbian learning. Our model predicts a power- law decay in mean population activity over days of odor expo