Year: 2015
Type: conference-paper
Abstract: Neural population activity often exhibits rich variability.This variability can arise from single-neuron stochasticity, neural dynamics on short time-scales, as well as from modulations of neural firing properties on long time-scales, often referred to as neural non-stationarity.To better understand the nature of co-variability in neural circuits and their impact on cortical information processing, we introduce a hierarchical dynamics model that is able to capture both slow inter-trial modulations in firing rates as well as neural population dynamics.We derive a Bayesian Laplace propagation algorithm for joint inference of parameters and population states.On neural population recordings from primary visual cortex, we demonstrate that our model provides a better account of the structure of neural firing than stationary dynamics models.
Source: Max Planck Digital Library
Authors: Mijung Park, Gergő Bohner, Jakob H. Macke
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Award: G8063013204
OpenAlex ID: W2186648317