Excitatory-inhibitory balance modulates the formation and dynamics of neuronal assemblies in cortical networks

Abstract

Repetitive activation of subpopulation of neurons in cortical networks leads to the formation of neuronal assemblies, which can guide learning and behavior. Recent technological advances have made the artificial induction of such assemblies feasible, yet how various patterns of activation can shape their emergence in different operating regimes is not clear. Here we studied this question in large-scale cortical networks composed of excitatory (E) and inhibitory (I) neurons. We found that the dynamics of the network in which neuronal assemblies are embedded is important for their induction. In networks with strong E-E coupling at the border of E-I balance, increasing the number of perturbed neurons enhanced the potentiation of ensembles. This was, however, accompanied by off-target potentiation of connections from unperturbed neurons. When strong E-E connectivity was combined with dominant E-I interactions, formation of ensembles became specific. Counter-intuitively, increasing the number of perturbed neurons in this regime decreased the average potentiation of individual synapses, leading to an optimal assembly formation at intermediate sizes. This was due to potent lateral inhibition in this regime, which also slowed down the formation of neuronal assemblies, resulting in a speed-accuracy trade-off in the performance of the networks in pattern completion and behavioral discrimination. Our results therefore suggest that the two regimes might be suited for different cognitive tasks, with fast regimes enabling crude detections and slow but specific regimes favoring finer discriminations. Functional connectivity inferred from recent experiments in mouse cortical networks seems to be consistent with the latter regime, but we show that recurrent and top-down mechanisms can dynamically modulate the networks to switch between different states. Our work provides a framework to study how neuronal perturbations can lead to network-wide plasticity under biologically realistic conditions, and sheds light on the design of future experiments to optimally induce behaviorally relevant neuronal assemblies.

Whole brain evaluation of cortical micro-connectomes

“…We used relatively young mice, 3weeks old. This age range could reflect their importance in ongoing pattern formation in the process of developing neural circuits [Sadeh, Clopath, 2021]. The experimental results in our study are consistent with the fact that, if we observe activities of living neurons as the whole of cortical regions, the contributions of excitatory and inhibitory neurons are highly balanced.…”

Neural learning rules for generating flexible predictions and computing the successor representation

“…A key component of our plasticity rule is a decorrelative term that depresses synapses based on coincident activity. Such anti-Hebbian or inhibitory effects are hypothesized to be broadly useful for learning, especially in unsupervised learning with overlapping input features [56, 57, 58]. Consistent with this hypothesis, anti-Hebbian learning has been implicated in circuits that perform a wide range of computations, from distinguishing patterns, [37], to familiarity detection [38], to learning birdsong syllables [59].…”

“…Consistent with this hypothesis, anti-Hebbian learning has been implicated in circuits that perform a wide range of computations, from distinguishing patterns, [37], to familiarity detection [38], to learning birdsong syllables [59]. This inhibitory learning may be useful because it decorrelates redundant information, allowing for greater specificity and capacity in a network [57, 37]. Our results provide further support of these hypotheses and predict that anti-Hebbian learning is fundamental to a predictive neural circuit.…”

Excitation-inhibition imbalance in Alzheimer’s disease using multiscale neural model inversion of resting-state fMRI

“…Excitation-inhibition (E-I) balance is a fundamental property of neuronal circuits that regulates multiple essential brain functions such as information coding, synaptic plasticity, memory stability and neurogenesis (Deneve and Machens, 2016;Rubin et al, 2017;Bhatia et al, 2019;Lopatina et al, 2019;Sadeh and Clopath, 2021). Disruption of E-I balance has been implicated in both AD animal models (Palop et al, 2007;Verret et al, 2012) and human AD (Dickerson et al, 2004(Dickerson et al, , 2005Celone et al, 2006;Bakker et al, 2012).…”