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End-to-end neural system identification with neural information flow
- Source :
- Plos Computational Biology; 1553-7358; 2; vol. 17; e1008558; ~Plos Computational Biology~~~~~1553-7358~2~17~~e1008558
- Publication Year :
- 2021
-
Abstract
- Contains fulltext : 230081.pdf (preprint version ) (Open Access)<br />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.
Details
- Database :
- OAIster
- Journal :
- Plos Computational Biology; 1553-7358; 2; vol. 17; e1008558; ~Plos Computational Biology~~~~~1553-7358~2~17~~e1008558
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1284056997
- Document Type :
- Electronic Resource