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Journal Article

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


Seeliger,  Katja
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands;
Max Planck Research Group Vision and Computational Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Seeliger, K., Ambrogioni, L., Güçlütürk, Y., van den Bulk, L. M., Güçlü, U., & van Gerven, M. A. J. (2021). End-to-end neural system identification with neural information flow. PLoS Computational Biology, 17(2): e1008558. doi:10.1371/journal.pcbi.1008558.

Cite as: https://hdl.handle.net/21.11116/0000-000B-A3D2-F
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.