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

Neural information flow (NIF) provides a novel approach for system identification in neuroscience. It models the neural computations in multiple brain regions and can be trained end-to-end via stochastic gradient descent from noninvasive data. NIF models represent neural information processing via 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 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 recorded in a single participant. We show that we can recover plausible visual representations and population receptive fields that are consistent with empirical findings.Author summary: We propose a method for data-driven estimation of computational models, representing neural information processing between different cortical areas. We demonstrate this method on the largest single-participant naturalistic fMRI dataset recorded to date. By training a simplified model of the visual system we show that biologically plausible computations emerge in the training process, yielding a new approach to understanding information processing in neural systems. The approach is applicable to other sensory or imaging modalities, thus providing a general way to computational modeling in cognitive neuroscience.