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

ABSTRACT: Repetitive activation of subpopulations of neurons leads to the formation of neuronal assemblies, which can guide learning and behavior. Recent technological advances have made the artificial induction of these assemblies feasible, yet how various parameters of induction can be optimized is not clear. Here, we studied this question in large-scale cortical network models with excitatory-inhibitory balance. We found that the background network in which assemblies are embedded can strongly modulate their dynamics and formation. Networks with dominant excitatory interactions enabled a fast formation of assemblies, but this was accompanied by recruitment of other non-perturbed neurons, leading to some degree of nonspecific induction. On the other hand, networks with strong excitatory-inhibitory interactions ensured that the formation of assemblies remained constrained to the perturbed neurons, but slowed down the induction. Our results suggest that these two regimes can be suitable for computational and cognitive tasks with different trade-offs between speed and specificity.

SUBMITTER: Sadeh S

PROVIDER: S-EPMC8565910 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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

Sadeh Sadra S Clopath Claudia C

Science advances 20211103 45

Repetitive activation of subpopulations of neurons leads to the formation of neuronal assemblies, which can guide learning and behavior. Recent technological advances have made the artificial induction of these assemblies feasible, yet how various parameters of induction can be optimized is not clear. Here, we studied this question in large-scale cortical network models with excitatory-inhibitory balance. We found that the background network in which assemblies are embedded can strongly modulate ...[more]

PMID: 34731002

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Project description:How the connectivity of cortical networks determines the neural dynamics and the resulting computations is one of the key questions in neuroscience. Previous works have pursued two complementary approaches to quantify the structure in connectivity. One approach starts from the perspective of biological experiments where only the local statistics of connectivity motifs between small groups of neurons are accessible. Another approach is based instead on the perspective of artificial neural networks where the global connectivity matrix is known, and in particular its low-rank structure can be used to determine the resulting low-dimensional dynamics. A direct relationship between these two approaches is however currently missing. Specifically, it remains to be clarified how local connectivity statistics and the global low-rank connectivity structure are inter-related and shape the low-dimensional activity. To bridge this gap, here we develop a method for mapping local connectivity statistics onto an approximate global low-rank structure. Our method rests on approximating the global connectivity matrix using dominant eigenvectors, which we compute using perturbation theory for random matrices. We demonstrate that multi-population networks defined from local connectivity statistics for which the central limit theorem holds can be approximated by low-rank connectivity with Gaussian-mixture statistics. We specifically apply this method to excitatory-inhibitory networks with reciprocal motifs, and show that it yields reliable predictions for both the low-dimensional dynamics, and statistics of population activity. Importantly, it analytically accounts for the activity heterogeneity of individual neurons in specific realizations of local connectivity. Altogether, our approach allows us to disentangle the effects of mean connectivity and reciprocal motifs on the global recurrent feedback, and provides an intuitive picture of how local connectivity shapes global network dynamics.

Project description:The neuro-physiological properties of individuals with genetic pre-disposition to neurological disorders are largely unknown. Here we aimed to explore these properties using cerebral organoids (COs) derived from fibroblasts of individuals with confirmed genetic mutations including PRNPE200K, trisomy 21 (T21), and LRRK2G2019S, which are associated with Creutzfeldt Jakob disease, Down Syndrome, and Parkinson's disease. We utilized no known disease/healthy COs (HC) as normal function controls. At 3-4 and 6-10 months post-differentiation, COs with mutations showed no evidence of disease-related pathology. Electrophysiology assessment showed that all COs exhibited mature neuronal firing at 6-10 months old. At this age, we observed significant changes in the electrophysiology of the COs with disease-associated mutations (dCOs) as compared with the HC, including reduced neuronal network communication, slowing neuronal oscillations, and increased coupling of delta and theta phases to the amplitudes of gamma oscillations. Such changes were linked with the detection of hypersynchronous events like spike-and-wave discharges. These dysfunctions were associated with altered production and release of neurotransmitters, compromised activity of excitatory ionotropic receptors including receptors of kainate, AMPA, and NMDA, and changed levels and function of excitatory glutamatergic synapses and inhibitory GABAergic synapses. Neuronal properties that modulate GABAergic inhibition including the activity of Na-K-Cl cotransport 1 (NKCC1) in Cl- homeostasis and the levels of synaptic and extra-synaptic localization of GABA receptors (GABARs) were altered in the T21 COs only. The neurosteroid allopregnanolone, a positive modulator of GABARs, was downregulated in all the dCOs. Treatment with this neurosteroid significantly improved the neuronal communication in the dCOs, possibly through improving the GABAergic inhibition. Overall, without the manifestation of any disease-related pathology, the genetic mutations PRNPE200K, T21, and LRRK2G2019S significantly altered the neuronal network communication in dCOs by disrupting the excitatory-to-inhibitory balance.

Dataset Information

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

Publications

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

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