ArticleNature communications2021
Predictive learning as a network mechanism for extracting low-dimensional latent space representations.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Strong and localized recurrence controls the dimensionality of neural activity across brain areas.Nature neuroscience · 2026Article
- Manifold-constrained plasticity enables stable learning in recurrent neural circuits.PLoS computational biology · 2026Article
- Categorization is 'baked' into the brain.Nature reviews. Neuroscience · 2026Review
- Prototype-based sleep micro-structure learning for explainable and robust multimodal recognition of sleep-related conditions.Research square · 2026Article
- Review
- Patterns of neural activity in prelimbic cortex neurons correlate with attentional behavior in the rodent continuous performance test.Translational psychiatry · 2025Article
- Three types of remapping with linear decoders: A population-geometric perspective.PLoS computational biology · 2025Article
- Patterns of neural activity in prelimbic cortex neurons correlate with attentional behavior in the rodent continuous performance test.bioRxiv : the preprint server for biology · 2025Article
- Three types of remapping with linear decoders: a population-geometric perspective.bioRxiv : the preprint server for biology · 2025Article
- Exploring the Architectural Biases of the Cortical Microcircuit.Neural computation · 2025Article
- A mechanism for the emergence of low-dimensional structures in brain dynamics.NPJ systems biology and applications · 2025Article
- TiDHy: Timescale Demixing via Hypernetworks to learn simultaneous dynamics from mixed observations.bioRxiv : the preprint server for biology · 2025Article
- Optimal sparsity in autoencoder memory models of the hippocampus.bioRxiv : the preprint server for biology · 2025Article
- Image biomarkers and explainable AI: handcrafted features versus deep learned features.European radiology experimental · 2024Review
- A hierarchical active inference model of spatial alternation tasks and the hippocampal-prefrontal circuit.Nature communications · 2024Article
- Reach-dependent reorientation of rotational dynamics in motor cortex.Nature communications · 2024Article
- Computational role of structure in neural activity and connectivity.Trends in cognitive sciences · 2024Review
- Exploring the Architectural Biases of the Canonical Cortical Microcircuit.bioRxiv : the preprint server for biology · 2024Article
- Article
- Tuned geometries of hippocampal representations meet the computational demands of social memory.Neuron · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial neural networks have recently achieved many successes in solving sequential processing and planning tasks. Their success is often ascribed to the emergence of the task's low-dimensional latent structure in the network activity - i.e., in the learned neural representations. Here, we investigate the hypothesis that a means for generating representations with easily accessed low-dimensional latent structure, possibly reflecting an underlying semantic organization, is through learning to predict observations about the world. Specifically, we ask whether and when network mechanisms for sensory prediction coincide with those for extracting the underlying latent variables. Using a recurrent neural network model trained to predict a sequence of observations we show that network dynamics exhibit low-dimensional but nonlinearly transformed representations of sensory inputs that map the latent structure of the sensory environment. We quantify these results using nonlinear measures of intrinsic dimensionality and linear decodability of latent variables, and provide mathematical arguments for why such useful predictive representations emerge. We focus throughout on how our results can aid the analysis and interpretation of experimental data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.