ArticleCommunications biology2023
Role of interneuron subtypes in controlling trial-by-trial output variability in the neocortex.
Article in Communications biology, 2023. 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.
- Can we infer excitation-inhibition balance from the spectrum of population activity?Communications biology · 2025Article
- Recurrent circuits encode de novo visual center-surround computations in the mouse superior colliculus.PLoS biology · 2025Article
- Excitatory/inhibitory ratio disruption modulates neural synchrony and flow directions in a cortical microcircuit.PLoS computational biology · 2025Article
- Spike reliability is cell type specific and shapes excitation and inhibition in the cortex.Scientific reports · 2025Article
- A layered microcircuit model of somatosensory cortex with three interneuron types and cell-type-specific short-term plasticity.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Spike Reliability is Cell-Type Specific and Shapes Excitation and Inhibition in the Cortex.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
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Abstract
Trial-by-trial variability is a ubiquitous property of neuronal activity in vivo which shapes the stimulus response. Computational models have revealed how local network structure and feedforward inputs shape the trial-by-trial variability. However, the role of input statistics and different interneuron subtypes in this process is less understood. To address this, we investigate the dynamics of stimulus response in a cortical microcircuit model with one excitatory and three inhibitory interneuron populations (PV, SST, VIP). Our findings demonstrate that the balance of inputs to different neuron populations and input covariances are the primary determinants of output trial-by-trial variability. The effect of input covariances is contingent on the input balances. In general, the network exhibits smaller output trial-by-trial variability in a PV-dominated regime than in an SST-dominated regime. Importantly, our work reveals mechanisms by which output trial-by-trial variability can be controlled in a context, state, and task-dependent manner.
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