PT  - JOURNAL ARTICLE
AU  - Owen Mackwood
AU  - Laura B. Naumann
AU  - Henning Sprekeler
TI  - Learning excitatory-inhibitory neuronal assemblies in recurrent networks
AID  - 10.1101/2020.03.30.016352
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.03.30.016352
4099  - http://biorxiv.org/content/early/2020/06/04/2020.03.30.016352.short
4100  - http://biorxiv.org/content/early/2020/06/04/2020.03.30.016352.full
AB  - In sensory circuits with poor feature topography, stimulus-specific feedback inhibition necessitates carefully tuned synaptic circuitry. Recent experimental data from mouse primary visual cortex (V1) show that synapses between pyramidal neurons and parvalbumin-expressing (PV) inhibitory interneurons tend to be stronger for neurons that respond to similar stimulus features. The mechanism that underlies the formation of such excitatory-inhibitory (E/I) assemblies is unresolved. Here, we show that activity-dependent synaptic plasticity on input and output synapses of PV interneurons generates a circuit structure that is consistent with mouse V1. Using a computational model, we show that both forms of plasticity must act synergistically to form the observed E/I assemblies. Once established, these assemblies produce a stimulus-specific competition between pyramidal neurons. Our model suggests that activity-dependent plasticity can enable inhibitory circuits to actively shape cortical computations.Competing Interest StatementThe authors have declared no competing interest.