2013/06/21 by David Leen, Eric Shea‐Brown, Eric Shea-Brown +2
Biochemistry, Genetics and Molecular Biology · Engineering · Neuroscience · Physics and Astronomy · #Advanced Memory and Neural Computing #FOS: Biological sciences #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #q-bio.NC #stochastic dynamics and bifurcation
paper · pdf · doi:10.48550/arxiv.1306.5275
arxiv created 2013/06/21 · openalex publication_date 2013/06/21 · arxiv updated 2013/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The collective dynamics of neural populations are often characterized in terms of correlations in the spike activity of different neurons. Open questions surround the basic nature of these correlations. In particular, what leads to higher-order correlations -- correlations in the population activity that extend beyond those expected from cell pairs? Here, we examine this question for a simple, but ubiquitous, circuit feature: common fluctuating input arriving to spiking neurons of integrate-and-fire type. We show that leads to strong higher-order correlations, as for earlier work with discrete threshold crossing models. Moreover, we find that the same is true for another widely used, doubly-stochastic model of neural spiking, the linear-nonlinear cascade. We explain the surprisingly strong connection between the collective dynamics produced by these models, and conclude that higher-order correlations are both broadly expected and possible to capture with surprising accuracy by simplified (and tractable) descriptions of neural spiking.