The effects of theta precession on spatial learning and simplicial complex dynamics in a topological model of the hippocampal spatial map.

ABSTRACT: Learning arises through the activity of large ensembles of cells, yet most of the data neuroscientists accumulate is at the level of individual neurons; we need models that can bridge this gap. We have taken spatial learning as our starting point, computationally modeling the activity of place cells using methods derived from algebraic topology, especially persistent homology. We previously showed that ensembles of hundreds of place cells could accurately encode topological information about different environments ("learn" the space) within certain values of place cell firing rate, place field size, and cell population; we called this parameter space the learning region. Here we advance the model both technically and conceptually. To make the model more physiological, we explored the effec

SUBMITTER: Arai M 

PROVIDER: S-EPMC4063672 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature