bioRxivNeurosciencePreprint
Assembly formation is stabilized by Parvalbumin neurons and accelerated by Somatostatin neurons
Fereshteh LagziiD, Martha Canto-BustosiD, Anne-Marie M. OswaldiD, Brent DoironiD
Abstract
Learning entails preserving the features of the external world in the neuronal representations of the brain, and manifests itself in the form of strengthened interactions between neurons within assemblies. Hebbian synaptic plasticity is thought to be one mechanism by which correlations in spiking promote assembly formation during learning. While spike timing dependent plasticity (STDP) rules for excitatory synapses have been well characterized, inhibitory STDP rules remain incomplete, particularly with respect to sub-classes of inhibitory interneurons. Here, we report that in layer 2/3 of the orbitofrontal cortex of mice, inhibition from parvalbumin (PV) interneurons onto excitatory (E) neurons follows a symmetric STDP function and mediates homeostasis in E-neuron firing rates. However, inhibition from somatostatin (SOM) interneurons follows an asymmetric, Hebbian STDP rule. We incorporate these findings in both large scale simulations and mean-field models to investigate how these differences in plasticity impact network dynamics and assembly formation. We find that plasticity of SOM inhibition builds lateral inhibitory connections and increases competition between assemblies. This is reflected in amplified correlations between neurons within assembly and anti-correlations between assemblies. An additional finding is that the emergence of tuned PV inhibition depends on the interaction between SOM and PV STDP rules. Altogether, we show that incorporation of differential inhibitory STDP rules promotes assembly formation through competition, while enhanced inhibition both within and between assemblies protects new representations from degradation after the training input is removed.
- organism: mouse
- assay: electronic health records
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 36
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Purely STDP-based assembly dynamics: stability, learning, overlaps, drift and aging
Paul Manz, Raoul-Martin Memmesheimer·PLoS Computational BiologyPeer-reviewed·Apr 12, 2023
- Learning excitatory-inhibitory neuronal assemblies in recurrent networks
Owen Mackwood, Laura Naumann, Henning Sprekeler·eLifePeer-reviewed·Apr 26, 2021
- Homeostatic Plasticity Enables Stable, Flexible, and Tunable Assemblies
Michelle Miller, Christoph Miehl, Brent Doiron·bioRxiv·Jun 22, 2026
- Distinct roles of parvalbumin and somatostatin interneurons in the synchronization of spike-times in the neocortex
Hyun Jae Jang, Hyowon Chung, … Jeehyun Kwag·Science AdvancesPeer-reviewed·Apr 22, 2020
- Training and spontaneous reinforcement of neuronal assemblies by spike timing plasticity
Gabriel Koch Ocker, Brent Doiron·Cerebral CortexPeer-reviewed·Jan 5, 2018
- Excitatory-inhibitory balance modulates the formation and dynamics of neuronal assemblies in cortical networks
Sadra Sadeh, Claudia Clopath A·Science AdvancesPeer-reviewed·Nov 3, 2021