Statistical mechanics of attractor neural network models with synaptic depression
Statistical mechanics of attractor neural network models with synaptic depression
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DOI：
10.1088/1742-6596/197/1/012018
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发表时间：
2009-12
期刊：
影响因子：
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通讯作者：
Y. Igarashi;Masafumi Oizumi;Yosuke Otsubo;K. Nagata;M. Okada
中科院分区：
文献类型：
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作者：
Y. Igarashi;Masafumi Oizumi;Yosuke Otsubo;K. Nagata;M. Okada
已知突触抑制控制突触前输入的增益。由于皮层神经元接收数千个突触前输入，并且它们的输出被馈送到数千个其他神经元，因此突触抑制应该影响神经网络的宏观性质。我们采用简单的神经网络模型来探索突触抑制的宏观效应。由于传统的平衡力学方法中连接的不对称性，无法分析具有突触抑制的系统。因此，我们首先提出了微观动力学平均场理论。接下来，我们推导出宏观稳态方程，并讨论了各种类型的神经网络模型的稳态的稳定性。
Synaptic depression is known to control gain for presynaptic inputs. Since cortical neurons receive thousands of presynaptic inputs, and their outputs are fed into thousands of other neurons, the synaptic depression should influence macroscopic properties of neural networks. We employ simple neural network models to explore the macroscopic effects of synaptic depression. Systems with the synaptic depression cannot be analyzed due to asymmetry of connections with the conventional equilibrium statistical-mechanical approach. Thus, we first propose a microscopic dynamical mean field theory. Next, we derive macroscopic steady state equations and discuss the stabilities of steady states for various types of neural network models.