Realistic Spiking Neural Network: Non-synaptic Mechanisms Improve Convergence in Cell Assembly

Autor: Carla A. Scorza, Fulvio A. Scorza, Luiz Eduardo Canton Santos, Antônio Márcio Rodrigues, Damien Depannemaecker, Antonio-Carlos G. de Almeida
Přispěvatelé: Institut des Neurosciences de Paris-Saclay (Neuro-PSI), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Universidade Federal de São João del-Rei (UFSJ), Instituto Superior Técnico, Universidade Técnica de Lisboa (IST), Federal University of Sao Paulo (Unifesp), Institut des Neurosciences Paris-Saclay (NeuroPSI), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
0209 industrial biotechnology
Computer science
Ephaptic coupling
Cognitive Neuroscience
Models
Neurological
Action Potentials
02 engineering and technology
Machine Learning
Synaptic weight
Bursting
020901 industrial engineering & automation
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
Artificial Intelligence
Convergence (routing)
Spiking neural network
0202 electrical engineering
electronic engineering
information engineering
Premovement neuronal activity
Synchronism
Biophysical model
Neurons
Artificial neural network
Burst activity
[SCCO.NEUR]Cognitive science/Neuroscience
Brain
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Coupling (electronics)
Electrophysiology
020201 artificial intelligence & image processing
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Neural Networks
Computer
Convergence
Neuroscience
Zdroj: Neural Networks
Neural Networks, Elsevier, 2020, 122, pp.420-433. ⟨10.1016/j.neunet.2019.09.038⟩
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2019.09.038⟩
Popis: International audience; Learning in neural networks inspired by brain tissue has been studied for machine learning applications. However, existing works primarily focused on the concept of synaptic weight modulation, and other aspects of neuronal interactions, such as non-synaptic mechanisms, have been neglected. Non-synaptic interaction mechanisms have been shown to play significant roles in the brain, and four classes of these mechanisms can be highlighted: (i) electrotonic coupling; (ii) ephaptic interactions; (iii) electric field effects; and iv) extracellular ionic fluctuations. In this work, we proposed simple rules for learning inspired by recent findings in machine learning adapted to a realistic spiking neural network. We show that the inclusion of non-synaptic interaction mechanisms improves cell assembly convergence. By including extracellular ionic fluctuation represented by the extracellular electrodiffusion in the network, we showed the importance of these mechanisms to improve cell assembly convergence. Additionally, we observed a variety of electrophysiological patterns of neuronal activity, particularly bursting and synchronism when the convergence is improved.
Databáze: OpenAIRE
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