ArticleScience advances2024
Emergence of co-tuning in inhibitory neurons as a network phenomenon mediated by randomness, correlations, and homeostatic plasticity.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Task success in trained spiking neural network models coincides with emergence of cross-stimulus-modulated inhibition.Biological cybernetics · 2026Article
- Higher dopamine D1 receptor expression in prefrontal parvalbumin neurons underlies higher distractibility in marmosets versus macaques.Communications biology · 2025Article
- Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex.eLife · 2025Article
- Synaptic plasticity facilitates oscillations in a V1 cortical column model with multiple interneuron types.Frontiers in computational neuroscience · 2025Article
- Discovering plasticity rules that organize and maintain neural circuits.bioRxiv : the preprint server for biology · 2024Article
- Structural influences on synaptic plasticity: The role of presynaptic connectivity in the emergence of E/I co-tuning.PLoS computational biology · 2024Article
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Authors and funding
2 authors.
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Abstract
Cortical excitatory neurons show clear tuning to stimulus features, but the tuning properties of inhibitory interneurons are ambiguous. While inhibitory neurons have been considered to be largely untuned, some studies show that some parvalbumin-expressing (PV) neurons do show feature selectivity and participate in co-tuned subnetworks with pyramidal neurons. In this study, we first use mean-field theory to demonstrate that a combination of homeostatic plasticity governing the synaptic dynamics of the connections from PV to excitatory neurons, heterogeneity in the excitatory postsynaptic potentials that impinge on PV neurons, and shared correlated input from layer 4 results in the functional and structural self-organization of PV subnetworks. Second, we show that structural and functional feature tuning of PV neurons emerges more clearly at the network level, i.e., that population-level measures identify functional and structural co-tuning of PV neurons that are not evident in pairwise individual-level measures. Finally, we show that such co-tuning can enhance network stability at the cost of reduced feature selectivity.
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