PUBLICATION

Computational functions of precisely balanced neuronal microcircuits in an olfactory memory network

Authors
Meissner-Bernard, C., Jenkins, B., Rupprecht, P., Bouldoires, E.A., Zenke, F., Friedrich, R.W., Frank, T.
ID
ZDB-PUB-250223-1
Date
2025
Source
Cell Reports   44: 115330115330 (Journal)
Registered Authors
Friedrich, Rainer
Keywords
CP: Neuroscience, EI balance, computational neuroscience, inhibitory microcircuits, olfactory cortex, olfactory system, optogenetics, zebrafish
MeSH Terms
  • Interneurons/physiology
  • Smell/physiology
  • Memory*/physiology
  • Nerve Net/physiology
  • Zebrafish*/physiology
  • Synapses/metabolism
  • Synapses/physiology
  • Animals
  • Optogenetics
  • Action Potentials/physiology
  • Models, Neurological
  • Neurons/physiology
PubMed
39985769 Full text @ Cell Rep.
Abstract
Models of balanced autoassociative memory networks predict that specific inhibition is critical to store information in connectivity. To explore these predictions, we characterized and manipulated different subtypes of fast-spiking interneurons in the posterior telencephalic area Dp (pDp) of adult zebrafish, the homolog of the piriform cortex. Modeling of recurrent networks with assemblies showed that a precise balance of excitation and inhibition is important to prevent not only excessive firing rates ("runaway activity") but also the stochastic occurrence of high pattern correlations ("runaway correlations"). Consistent with model predictions, runaway correlations emerged in pDp when synaptic balance was perturbed by optogenetic manipulations of feedback inhibition but not feedforward inhibition. Runaway correlations were driven by sparse subsets of strongly active neurons rather than by a general broadening of tuning curves. These results are consistent with balanced neuronal assemblies in pDp and reveal novel computational functions of inhibitory microcircuits in an autoassociative network.
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