A computational model to investigate astrocyte glutamate modulation effect on tripartite synapse and neuronal network state

2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME) ◽

10.1109/icbme.2016.7890956 ◽

2016 ◽

Author(s):

Mohammad Mohammadi ◽

Fariba Bahrami ◽

Mahyar Janahmadi

Keyword(s):

Computational Model ◽

Neuronal Network ◽

Modulation Effect ◽

Tripartite Synapse ◽

Network State

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Building a Large-Scale Computational Model of a Cortical Neuronal Network

Understanding Complex Systems - Lectures in Supercomputational Neurosciences ◽

10.1007/978-3-540-73159-7_9 ◽

2007 ◽

pp. 251-266

Author(s):

Lucia Zemanová ◽

Changsong Zhou ◽

Jürgen Kurths

Keyword(s):

Computational Model ◽

Neuronal Network ◽

Large Scale

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A New Computational Model for Astrocytes and Their Role in Biologically Realistic Neural Networks

Computational Intelligence and Neuroscience ◽

10.1155/2018/3689487 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Zahra Sajedinia ◽

Sébastien Hélie

Keyword(s):

Neural Network ◽

Neural Networks ◽

Synaptic Plasticity ◽

Computational Model ◽

Bipartite Network ◽

Presynaptic Neuron ◽

Tripartite Synapse ◽

And Performance ◽

Robust To Noise

Recent studies in neuroscience show that astrocytes alongside neurons participate in modulating synapses. It led to the new concept of “tripartite synapse”, which means that a synapse consists of three parts: presynaptic neuron, postsynaptic neuron, and neighboring astrocytes. However, it is still unclear what role is played by the astrocytes in the tripartite synapse. Detailed biocomputational modeling may help generate testable hypotheses. In this article, we aim to study the role of astrocytes in synaptic plasticity by exploring whether tripartite synapses are capable of improving the performance of a neural network. To achieve this goal, we developed a computational model of astrocytes based on the Izhikevich simple model of neurons. Next, two neural networks were implemented. The first network was only composed of neurons and had standard bipartite synapses. The second network included both neurons and astrocytes and had tripartite synapses. We used reinforcement learning and tested the networks on categorizing random stimuli. The results show that tripartite synapses are able to improve the performance of a neural network and lead to higher accuracy in a classification task. However, the bipartite network was more robust to noise. This research provides computational evidence to begin elucidating the possible beneficial role of astrocytes in synaptic plasticity and performance of a neural network.

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