End-to-end neural system identification with neural information flow

Abstract Neural information flow (NIF) is a new framework for system identification in neuroscience. It integrates population receptive field estimation, neural encoding, connectivity analysis and hemodynamic response estimation in a single differentiable model that can be trained end-to-end via stochastic gradient descent. NIF models represent neural information processing systems as a network of coupled tensors, each encoding the representation of the sensory input contained in a brain region. The elements of these tensors can be interpreted as cortical columns whose activity encodes the presence of a specific feature in a spatiotemporal location. Each tensor is coupled to the measured data specific to a brain region via low-rank observation models that can be decomposed into the spatial, temporal and feature receptive fields of a localized neuronal population. Both these observation models and the convolutional weights defining the information processing within regions and effective connectivity between regions are learned end-to-end by predicting the neural signal during sensory stimulation. We trained a NIF model on the activity of early visual areas using a large-scale fMRI dataset. We show that we can recover plausible visual representations and population receptive fields that are consistent with empirical findings.