## Frontmatter | | | | --- | --- | | Authors | [[Katja Seeliger]], [[Luca Ambrogioni]], [[Umut Güçlü]], [[Marcel van Gerven]] | | Date | 2021/02 | | Source | [[PLOS Computational Biology]] | | URL | https://doi.org/10.1371/journal.pcbi.1008558 | | Citation | Seeliger, K., Ambrogioni, L., Güçlü, U., & van Gerven, M. (2021). [[End-to-end neural system identification with neural information flow]]. _PLOS Computational Biology_. [[URL](https://doi.org/10.1371/journal.pcbi.1008558)]. #Journal | ## Abstract 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. ## PDF ![[End-to-end neural system identification with neural information flow.pdf]]