ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2019
Robust Associative Learning Is Sufficient to Explain the Structural and Dynamical Properties of Local Cortical Circuits.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Strong and localized recurrence controls the dimensionality of neural activity across brain areas.Nature neuroscience · 2026Article
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- Synaptic pruning, myelination and the emergence of psychiatric disorders in late adolescence.bioRxiv : the preprint server for biology · 2026Article
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- Two-factor synaptic consolidation reconciles robustness with pruning and homeostatic scaling.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Sparse Learning Enabled by Constraints on Connectivity and Function.Physical review letters · 2025Article
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- Optimal learning with excitatory and inhibitory synapses.PLoS computational biology · 2020Article
- Computing and optimizing over all fixed-points of discrete systems on large networks.Journal of the Royal Society, Interface · 2020Article
- Flexible neural connectivity under constraints on total connection strength.PLoS computational biology · 2020Article
- Local Cortical Circuit Features Arise in Networks Optimized for Associative Memory Storage.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- Impact of higher order network structure on emergent cortical activity.Network neuroscience (Cambridge, Mass.) · 2020Article
- Robust Associative Learning Is Sufficient to Explain the Structural and Dynamical Properties of Local Cortical Circuits.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
- Article
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Abstract
The ability of neural networks to associate successive states of network activity lies at the basis of many cognitive functions. Hence, we hypothesized that many ubiquitous structural and dynamical properties of local cortical networks result from associative learning. To test this hypothesis, we trained recurrent networks of excitatory and inhibitory neurons on memories composed of varying numbers of associations and compared the resulting network properties with those observed experimentally. We show that, when the network is robustly loaded with near-maximum amount of associations it can support, it develops properties that are consistent with the observed probabilities of excitatory and inhibitory connections, shapes of connection weight distributions, overexpression of specific 2- and 3-neuron motifs, distributions of connection numbers in clusters of 3-8 neurons, sustained, irregular, and asynchronous firing activity, and balance of excitation and inhibition. In addition, memories loaded into the network can be retrieved, even in the presence of noise that is comparable with the baseline variations in the postsynaptic potential. The confluence of these results suggests that many structural and dynamical properties of local cortical networks are simply a byproduct of associative learning. We predict that overexpression of excitatory-excitatory bidirectional connections observed in many cortical systems must be accompanied with underexpression of bidirectionally connected inhibitory-excitatory neuron pairs.
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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.