Modeling dynamic neural activity by combining naturalistic video stimuli and stimulus-independent latent factors

Finn Schmidt, Suhas Shrinivasan, Polina Turishcheva, Fabian H. Sinz

Keywords: Dynamics of neural representations, probabilistic neural predictive models, primary visual cortex of mice, latent variable models, stimulus-independent variability

TL;DR: This paper presents a probabilistic model combining video inputs with stimulus-independent latent factors to model dynamic neural activity in mouse V1.

Abstract:

Understanding how the brain processes dynamic natural stimuli remains a fundamental
challenge in neuroscience. Current dynamic neural encoding models either take stimuli
as input but ignore shared variability in neural responses, or they model this variability
by deriving latent embeddings from neural responses or behavior while ignoring the visual
input. To address this gap, we propose a probabilistic model that incorporates video inputs
along with stimulus-independent latent factors to capture variability in neuronal responses,
predicting a joint distribution for the entire population. After training and testing our
model on mouse V1 neuronal responses, we found that it outperforms video-only models in
terms of log-likelihood and achieves further improvements when conditioned on responses
from other neurons. Furthermore, we find that the learned latent factors strongly correlate
with mouse behavior, although the model was trained without behavior data.

Submission Number: 54