ArticleNature communications2017
Sparse synaptic connectivity is required for decorrelation and pattern separation in feedforward networks.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers.
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Who cites it
80 citing papers in PubMed, 138 citations in OpenAlex.
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- Cerebellar microcircuits enable robust evidence-based decisions through cortico-cerebellar coupling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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- A Synchronization-Driven Learning Rule for Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks.Research square · 2026Article
- Cerebellum-inspired memtransistors enable emergent differentiation for hardware-efficient novelty detection.Nature communications · 2026Article
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- Random innervation of cerebellar Purkinje cells as a substrate for diverse representational learning.bioRxiv : the preprint server for biology · 2026Article
- Sparsity and memory constraints interact with training sequence to bias learning of associative maps.Neuropsychologia · 2026Article
- Sleep facilitates pattern separation through SK channel-mediated sparse coding.Current biology : CB · 2026Article
- Granule cells reorient cortical manifolds to separate contexts but preserve their geometry.bioRxiv : the preprint server for biology · 2026Article
- Voltage imaging as a window into neural computation.Neurophotonics · 2026Article
- Sparse input representations explain odor discrimination in complex, concentration-varying mixtures.bioRxiv : the preprint server for biology · 2026Article
- Structured sampling of molecularly classified mossy fiber inputs by cerebellar granule cells.Frontiers in computational neuroscience · 2026Article
- Optogenetic interrogation of the zebrafish lateral line reveals brain-wide neural circuits involved in pattern separation.iScience · 2025Article
- A computational model of the cerebellar granular layer calibrated to experimental data for studying inhibition and sensory encoding.Scientific reports · 2025Article
- Degeneracy Explains Diversity in Interneuronal Regulation of Pattern Separation in Heterogeneous Dentate Gyrus Networks.Function (Oxford, England) · 2025Article
- Cerebellar circuit computations for predictive motor control.Nature reviews. Neuroscience · 2025Review
- Spatial constraints and cell surface molecule depletion structure a randomly connected learning circuit.Current biology : CB · 2025Article
- Self-supervised predictive learning accounts for cortical layer-specificity.Nature communications · 2025Article
20 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Pattern separation is a fundamental function of the brain. The divergent feedforward networks thought to underlie this computation are widespread, yet exhibit remarkably similar sparse synaptic connectivity. Marr-Albus theory postulates that such networks separate overlapping activity patterns by mapping them onto larger numbers of sparsely active neurons. But spatial correlations in synaptic input and those introduced by network connectivity are likely to compromise performance. To investigate the structural and functional determinants of pattern separation we built models of the cerebellar input layer with spatially correlated input patterns, and systematically varied their synaptic connectivity. Performance was quantified by the learning speed of a classifier trained on either the input or output patterns. Our results show that sparse synaptic connectivity is essential for separating spatially correlated input patterns over a wide range of network activity, and that expansion and correlations, rather than sparse activity, are the major determinants of pattern separation.
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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.