ArticlePLoS computational biology2021
End-to-end neural system identification with neural information flow.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- Higher visual areas act like domain-general filters with strong selectivity and functional specialization.Nature communications · 2026Article
- Universal differential equations as a unifying modeling language for neuroscience.Frontiers in computational neuroscience · 2025Review
- Alignment of auditory artificial networks with massive individual fMRI brain data leads to generalisable improvements in brain encoding and downstream tasks.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Article
- A benchmark of individual auto-regressive models in a massive fMRI dataset.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Neural encoding with unsupervised spiking convolutional neural network.Communications biology · 2023Article
- The neuroconnectionist research programme.Nature reviews. Neuroscience · 2023Review
- Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations.Nature communications · 2023Article
- Article
- High-Level Visual Encoding Model Framework with Hierarchical Ventral Stream-Optimized Neural Networks.Brain sciences · 2022Article
- Brain2Pix: Fully convolutional naturalistic video frame reconstruction from brain activity.Frontiers in neuroscience · 2022Article
- A deeper look at vision and memory.Nature neuroscience · 2022Article
- A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence.Nature neuroscience · 2022Article
- Computational models of category-selective brain regions enable high-throughput tests of selectivity.Nature communications · 2021Article
- Unveiling functions of the visual cortex using task-specific deep neural networks.PLoS computational biology · 2021Article
- Discovering the Computational Relevance of Brain Network Organization.Trends in cognitive sciences · 2020Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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