Multimodal Deep Learning Model Unveils Behavioral Dynamics of V1 Activity in Freely Moving Mice.

ABSTRACT: Despite their immense success as a model of macaque visual cortex, deep convolutional neural networks (CNNs) have struggled to predict activity in visual cortex of the mouse, which is thought to be strongly dependent on the animal's behavioral state. Furthermore, most computational models focus on predicting neural responses to static images presented under head fixation, which are dramatically different from the dynamic, continuous visual stimuli that arise during movement in the real world. Consequently, it is still unknown how natural visual input and different behavioral variables may integrate over time to generate responses in primary visual cortex (V1). To address this, we introduce a multimodal recurrent neural network that integrates gaze-contingent visual input with behavioral an

SUBMITTER: Xu A 

PROVIDER: S-EPMC11242920 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature