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

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 z...

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Vydáno v:Cell reports (Cambridge) Ročník 44; číslo 3; s. 115330
Hlavní autoři: Meissner-Bernard, Claire, Jenkins, Bethan, Rupprecht, Peter, Bouldoires, Estelle Arn, Zenke, Friedemann, Friedrich, Rainer W., Frank, Thomas
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Inc 25.03.2025
Elsevier
Témata:
Action Potentials - physiology
Animals
computational neuroscience
CP: Neuroscience
EI balance
inhibitory microcircuits
Interneurons - physiology
Memory - physiology
Models, Neurological
Nerve Net - physiology
Neurons - physiology
olfactory cortex
olfactory system
Optogenetics
zebrafish
Zebrafish - physiology
inhibitory microcircuits
olfactory cortex
CP: Neuroscience
optogenetics
olfactory system
computational neuroscience
zebrafish
EI balance
ISSN:2211-1247, 2211-1247
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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. [Display omitted] •Characterized fast-spiking interneuron subtypes in the zebrafish homolog of piriform cortex•Optogenetic attenuation of feedback inhibition generated runaway correlations•Runaway correlations occurred in network models when synaptic balance was disrupted•The results support models of autoassociative memory by balanced EI assemblies Meissner-Bernard et al. combined experiments and modeling to analyze specific functions of inhibition in a memory network. Inhibition was found to be important not only to stabilize overall activity levels but also to maintain activity patterns in an informative regime, consistent with models assuming balanced, non-random connectivity between assemblies of neurons.
AbstractList 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.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.
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.
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. [Display omitted] •Characterized fast-spiking interneuron subtypes in the zebrafish homolog of piriform cortex•Optogenetic attenuation of feedback inhibition generated runaway correlations•Runaway correlations occurred in network models when synaptic balance was disrupted•The results support models of autoassociative memory by balanced EI assemblies Meissner-Bernard et al. combined experiments and modeling to analyze specific functions of inhibition in a memory network. Inhibition was found to be important not only to stabilize overall activity levels but also to maintain activity patterns in an informative regime, consistent with models assuming balanced, non-random connectivity between assemblies of neurons.
ArticleNumber 115330
Author Friedrich, Rainer W.
Meissner-Bernard, Claire
Jenkins, Bethan
Frank, Thomas
Zenke, Friedemann
Rupprecht, Peter
Bouldoires, Estelle Arn
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  givenname: Thomas
  orcidid: 0000-0002-5113-176X
  surname: Frank
  fullname: Frank, Thomas
  email: thomas.frank@uni-goettingen.de
  organization: University of Göttingen, Faculty of Biology and Psychology, 37073 Göttingen, Germany
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Issue 3
Keywords inhibitory microcircuits
olfactory cortex
CP: Neuroscience
optogenetics
olfactory system
computational neuroscience
zebrafish
EI balance
Language English
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Copyright © 2025 The Author(s). Published by Elsevier Inc. All rights reserved.
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Snippet Models of balanced autoassociative memory networks predict that specific inhibition is critical to store information in connectivity. To explore these...
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SubjectTerms Action Potentials - physiology
Animals
computational neuroscience
CP: Neuroscience
EI balance
inhibitory microcircuits
Interneurons - physiology
Memory - physiology
Models, Neurological
Nerve Net - physiology
Neurons - physiology
olfactory cortex
olfactory system
Optogenetics
zebrafish
Zebrafish - physiology
Title Computational functions of precisely balanced neuronal microcircuits in an olfactory memory network
URI https://dx.doi.org/10.1016/j.celrep.2025.115330
https://www.ncbi.nlm.nih.gov/pubmed/39985769
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