ArticleProceedings of the National Academy of Sciences of the United States of America2024
Synapse-type-specific competitive Hebbian learning forms functional recurrent networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Homeostatic Plasticity Enables Stable yet Tunable Neuronal Assemblies.bioRxiv : the preprint server for biology · 2026Article
- Cellular and subcellular specialization enables biology-constrained deep learning.Cell reports · 2026Article
- Learning neural dynamics through instructive signals.bioRxiv : the preprint server for biology · 2025Article
- Continual familiarity decoding from recurrent connections in spiking networks.PLoS computational biology · 2025Article
- Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex.eLife · 2025Article
- Resource-dependent heterosynaptic spike-timing-dependent plasticity in recurrent networks with and without synaptic degeneration.Frontiers in computational neuroscience · 2025Article
- Structural influences on synaptic plasticity: The role of presynaptic connectivity in the emergence of E/I co-tuning.PLoS computational biology · 2024Article
- Synapse-type-specific competitive Hebbian learning forms functional recurrent networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Origins and Evolution of Imagination, From Australopithecus to Modern-Day Deep Learning.Wiley interdisciplinary reviews. Cognitive scienceReview
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Authors and funding
3 authors.
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Abstract
Cortical networks exhibit complex stimulus-response patterns that are based on specific recurrent interactions between neurons. For example, the balance between excitatory and inhibitory currents has been identified as a central component of cortical computations. However, it remains unclear how the required synaptic connectivity can emerge in developing circuits where synapses between excitatory and inhibitory neurons are simultaneously plastic. Using theory and modeling, we propose that a wide range of cortical response properties can arise from a single plasticity paradigm that acts simultaneously at all excitatory and inhibitory connections-Hebbian learning that is stabilized by the synapse-type-specific competition for a limited supply of synaptic resources. In plastic recurrent circuits, this competition enables the formation and decorrelation of inhibition-balanced receptive fields. Networks develop an assembly structure with stronger synaptic connections between similarly tuned excitatory and inhibitory neurons and exhibit response normalization and orientation-specific center-surround suppression, reflecting the stimulus statistics during training. These results demonstrate how neurons can self-organize into functional networks and suggest an essential role for synapse-type-specific competitive learning in the development of cortical circuits.
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