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Crouching tiger, hidden neuron

Banff International Research Station for Mathematical Innovation and Discovery

A major obstacle to understanding population coding in the brain is that neural activity can only be monitored at limited spatial and temporal scales. Inferences about network properties important for coding, such as connectivity between neurons, are sensitive to hidden units: unobserved neurons or other inputs that drive network activity. This problem is important not just for understanding inference from data, but also for which network properties shape spike train statistics as subsampled or pooled signals are transmitted through the brain. Recent computational efforts have been made to fit models to hidden units, but a fundamental theory of the effects of unobserved influences on the statistics of subsampled or pooled network activity remains elusive. Using methods from statistical physics, we have developed an analytical framework to begin answering questions about how ground truth properties of neuronal networks are distorted when an experimenter (or downstream neuron) can only observe coarsely resolved activity data. As a specific example, we study how the coupling filters of a generalized linear model fit to pooled spike train data change as a function of the fraction of spike trains pooled together.

Mathematics; Biology and other natural sciences; Probability theory and stochastic processes; Mathematical biology

video/mp4

Author affiliation: University of Washington

2016-06-09

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58277

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/