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International audience; We investigate the role of inhibition in structuring new neural assemblies following a learning phase in the brain. Although many studies have been devoted to this subject, few of them have considered the specific role of inhibitory neurons. Therefore, this work aims at studying the impact of inhibition in adaptive neural networks. In particular we focus on the role of synchronized dynamics for the creation and maintenance of structural modules in the brain circuits. Specifically, we considered an excitatory-inhibitory neural network made of θ-neurons with plastic Hebbian synapses. The learning process is mimicked by stimulating different areas of the network with temporal alternating external stimuli. This stimulation process leads to the emergence of modular structures. Then, the network dynamics is analysed after the learning phase revealing the maintenance of co-active neural assemblies over the long term. We show that the presence of inhibitory neurons controls the emergence of the modular structures and their maintenance in the resting phase following the learning period. Furthermore, we demonstrate that the number of inhibitory neurons in the network is directly related to the maximal number of neural assemblies that can be maintained. These results support the idea that inhibition has a direct impact on the memory capacity of a neural network.

We investigate the role of inhibition in structuring new neural assemblies following a learning phase in the brain. Although many studies have been devoted to this subject, few of them have considered the specific role of inhibitory neurons. Therefore, this work aims at studying the impact of inhibition in adaptive neural networks. In particular we focus on the role of synchronized dynamics for the creation and maintenance of structural modules in the brain circuits.Specifically, we considered an excitatory-inhibitory neural network made of θ-neurons with plastic Hebbian synapses. The learning process is mimicked by stimulating different areas of the network with temporal alternating external stimuli. This stimulation process leads to the emergence of modular structures. Then, the network dynamics is analysed after the learning phase revealing the maintenance of co-active neural assemblies over the long term.We show that the presence of inhibitory neurons controls the emergence of the modular structures and their maintenance in the resting phase following the learning period. Furthermore, we demonstrate that the number of inhibitory neurons in the network is directly related to the maximal number of neural assemblies that can be maintained.These results support the idea that inhibition has a direct impact on the memory capacity of a neural network.

Brain circuits display modular architecture at different scales of organization. Such neural assemblies are typically associated to functional specialization but the mechanisms leading to their emergence and consolidation still remain elusive. In this paper we investigate the role of inhibition in structuring new neural assemblies driven by the entrainment to various inputs. In particular, we focus on the role of partially synchronized dynamics for the creation and maintenance of structural modules in neural circuits by considering a network of excitatory and inhibitory θ -neurons with plastic Hebbian synapses. The learning process consists of an entrainment to temporally alternating stimuli that are applied to separate regions of the network. This entrainment leads to the emergence of modular structures. Contrary to common practice in artificial neural networks—where the acquired weights are typically frozen after the learning session—we allow for synaptic adaptation even after the learning phase. We find that the presence of inhibitory neurons in the network is crucial for the emergence and the post-learning consolidation of the modular structures. Indeed networks made of purely excitatory neurons or of neurons not respecting Dale’s principle are unable to form or to maintain the modular architecture induced by the stimuli. We also demonstrate that the number of inhibitory neurons in the network is directly related to the maximal number of neural assemblies that can be consolidated, supporting the idea that inhibition has a direct impact on the memory capacity of the neural network. This work was supported (R.B., G.D. and G.Z.L.) by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific [Grant Agreement No. 945539 (Human Brain Project SGA3)] and by an EUTOPIA funding [EUTOPIA-PhD-2020-0000000066 - NEUROAI]. A.T. received financial support by the Labex MME-DII [Grant No. ANR-11-LBX-0023-01] (together with M.Q.), and by the ANR Project ERMUNDY [Grant No. ANR-18-CE37-0014] all part of the French program “Investissements d’Avenir”. G.D. is supported by the Spanish national research project [ref. PID2019-105772GB-I00/AEI/10.13039/501100011033] funded by the Spanish Ministry of Science, Innovation, and Universities (MCIU). M.Q. is also partially supported by CNRS through the IPAL lab in Singapore.