A Mean Field to Capture Asynchronous Irregular Dynamics of Conductance-Based Networks of Adaptive Quadratic Integrate-and-Fire Neuron Models.
Neural Comput
; 36(7): 1433-1448, 2024 Jun 07.
Article
em En
| MEDLINE
| ID: mdl-38776953
ABSTRACT
Mean-field models are a class of models used in computational neuroscience to study the behavior of large populations of neurons. These models are based on the idea of representing the activity of a large number of neurons as the average behavior of mean-field variables. This abstraction allows the study of large-scale neural dynamics in a computationally efficient and mathematically tractable manner. One of these methods, based on a semianalytical approach, has previously been applied to different types of single-neuron models, but never to models based on a quadratic form. In this work, we adapted this method to quadratic integrate-and-fire neuron models with adaptation and conductance-based synaptic interactions. We validated the mean-field model by comparing it to the spiking network model. This mean-field model should be useful to model large-scale activity based on quadratic neurons interacting with conductance-based synapses.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Potenciais de Ação
/
Redes Neurais de Computação
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Modelos Neurológicos
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Neurônios
Limite:
Animals
/
Humans
Idioma:
En
Revista:
Neural Comput
Ano de publicação:
2024
Tipo de documento:
Article