Parvalbumin interneuron maturation reorganizes cortical population activity structure during development
Abstract
During brain development, spontaneous cortical activity undergoes a dramatic transformation from highly synchronous network events to less correlated and sparse neuronal firing that is thought to underlie more flexible neural coding. However, how the structure of population activity changes across this transition, what circuit mechanisms drive this reorganization, and the implications for information encoding have not been fully established. Using longitudinal in vivo calcium imaging in early postnatal mice, we demonstrate that cortical network desynchronization during the second postnatal week is accompanied by a redistribution of fractional variance from dominant, low-dimensional modes into many weaker dimensions, and a growing influence of GABAergic interneurons (INs) on the network. All-optical circuit interrogation revealed that parvalbumin (PV) INs, but not somatostatin (SST) INs, are sufficient to desynchronize the network and shift the distribution of population variance. Through targeted ablation of a fraction of PV INs, we show dimensionality is highly correlated with stimulus decoding accuracy. Furthermore, PV INs undergo a sharp increase in transcriptional maturation between postnatal day (P) 8 and 11, during which they preferentially enrich for synaptic genes. Lastly, a novel, biologically plausible, multi-cell-type computational model reproduced the developmental desynchronization and rise in dimensionality. Strikingly, without being constrained by perturbation data, the model accurately predicted the effects of each IN class on the network that we observed empirically.
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BibTeX
@misc{wu2026parvalbumin,
title = {{Parvalbumin interneuron maturation reorganizes cortical population activity structure during development}},
author = {Michelle W. Wu and Matteo Mariani and Anand Suresh and Nazim Kourdougli and Grace Jiang and Akash J. Kothari and Tanya S. Jain and Mario Dipoppa and Carlos Portera-Cailliau},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.22.761282},
url = {https://www.biorxiv.org/content/10.64898/2026.10.22.761282v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Parvalbumin interneuron maturation reorganizes cortical population activity structure during development
AU - Michelle W. Wu
AU - Matteo Mariani
AU - Anand Suresh
AU - Nazim Kourdougli
AU - Grace Jiang
AU - Akash J. Kothari
AU - Tanya S. Jain
AU - Mario Dipoppa
AU - Carlos Portera-Cailliau
PY - 2026
DA - 2026/10/22
PB - bioRxiv
DO - 10.64898/2026.10.22.761282
UR - https://www.biorxiv.org/content/10.64898/2026.10.22.761282v1
AB - During brain development, spontaneous cortical activity undergoes a dramatic transformation from highly synchronous network events to less correlated and sparse neuronal firing that is thought to underlie more flexible neural coding. However, how the structure of population activity changes across this transition, what circuit mechanisms drive this reorganization, and the implications for information encoding have not been fully established. Using longitudinal in vivo calcium imaging in early postnatal mice, we demonstrate that cortical network desynchronization during the second postnatal week is accompanied by a redistribution of fractional variance from dominant, low-dimensional modes into many weaker dimensions, and a growing influence of GABAergic interneurons (INs) on the network. All-optical circuit interrogation revealed that parvalbumin (PV) INs, but not somatostatin (SST) INs, are sufficient to desynchronize the network and shift the distribution of population variance. Through targeted ablation of a fraction of PV INs, we show dimensionality is highly correlated with stimulus decoding accuracy. Furthermore, PV INs undergo a sharp increase in transcriptional maturation between postnatal day (P) 8 and 11, during which they preferentially enrich for synaptic genes. Lastly, a novel, biologically plausible, multi-cell-type computational model reproduced the developmental desynchronization and rise in dimensionality. Strikingly, without being constrained by perturbation data, the model accurately predicted the effects of each IN class on the network that we observed empirically.
SN - 2692-8205
ER -