Effects of burst-timing-dependent plasticity on synchronous behaviour in neuronal network

Author(s)

Paludo Silveira, Joao Antonio ^[1] ; Protachevicz, Paulo Ricardo ^[2] ; Viana, Ricardo Luiz ^[1] ; Batista, Antonio Marcos ^{[2, 3, 4]}

Affiliation

^[1] Univ Fed Parana, Dept Phys, BR-82590300 Curitiba, Parana - Brazil

^[2] Univ Sao Paulo, Phys Inst, BR-05508090 Sao Paulo, SP - Brazil

^[3] Univ Estadual Ponta Grossa, Dept Math & Stat, BR-84030900 Ponta Grossa, Parana - Brazil

^[4] Univ Estadual Ponta Grossa, Grad Program Sci Phys, BR-84030900 Ponta Grossa, Parana - Brazil

• Document type: Journal article
• Source: Neurocomputing; v. 436, p. 126-135, MAY 14 2021.
• Web of Science Citations: 0

Abstract

Brain plasticity or neuroplasticity refers to the ability of the nervous system to reorganise itself in response to stimuli. For instance, sensory and motor stimulation, memory formation, and learning depend on brain plasticity. Neuronal synchronisation can be enhanced or suppressed by the plasticity. Synchronisation is related to many functions in the brain, as well as to some brain disorders. One possible plasticity rule is the burst-timing-dependent plasticity (BTDP), that induces synaptic alteration according to the timing of neuronal bursts. In this work, we build a network of coupled Rulkov maps where the excitatory connections are randomly distributed. We consider the BTDP to study its effects on the synchronous neuronal activities. In our simulations, we observe that depending on the initial synaptic weights, the whole network or part of it can have its neuronal synchronisation improved. This increase can be reached by two different mechanisms, the initial burst synchronisation and random statistical coincidence. A mix of these two mechanism is also found in the network. BTDP can induce the formation of desynchronised and synchronised clusters that operate in different frequencies, but only if the noise level is low. Our results show possible mechanisms of cluster formation in burst neuronal networks. We also consider the BTDP rule on a small-world network and show that, depending on the initial connection strength, the network can exhibit local or non-local properties. (c) 2021 Elsevier B.V. All rights reserved. (AU)

• FAPESP's process: 20/04624-2 - Synaptic plasticity in neuronal networks
• Grantee: Paulo Ricardo Protachevicz
• Support Opportunities: Scholarships in Brazil - Post-Doctoral