Artificial Neural Network Model with Astrocyte-Driven Short-Term Memory

被引：3

作者：

Zimin, Ilya A. ^{[1
]}

Kazantsev, Victor B. ^{[1
,2
]}

Stasenko, Sergey V. ^{[1
]}

机构：

[1] Lobachevsky State Univ Nizhny Novgorod, Lab Adv Methods High Dimens Data Anal, Nizhnii Novgorod 603022, Russia

[2] Moscow Inst Phys & Technol, Lab Neurobiomorph Technol, Moscow 117303, Russia

来源：

BIOMIMETICS | 2023年 / 8卷 / 05期

基金：

俄罗斯科学基金会;

关键词：

short-term memory; convolutional neural network; machine learning; neuron-glial interaction; WORKING-MEMORY; PERSISTENT ACTIVITY; CAPACITY;

D O I：

10.3390/biomimetics8050422

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this study, we introduce an innovative hybrid artificial neural network model incorporating astrocyte-driven short-term memory. The model combines a convolutional neural network with dynamic models of short-term synaptic plasticity and astrocytic modulation of synaptic transmission. The model's performance was evaluated using simulated data from visual change detection experiments conducted on mice. Comparisons were made between the proposed model, a recurrent neural network simulating short-term memory based on sustained neural activity, and a feedforward neural network with short-term synaptic depression (STPNet) trained to achieve the same performance level as the mice. The results revealed that incorporating astrocytic modulation of synaptic transmission enhanced the model's performance.

引用

页数：16

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