A simple neural network exhibiting selective activation of neuronal ensembles: From winner-take-all to winners-share-all
A simple neural network exhibiting selective activation of neuronal ensembles: From winner-take-all to winners-share-all
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DOI：
10.1162/neco.1997.9.1.77
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发表时间：
1997-01-01
影响因子：
2.9
通讯作者：
Tanaka, S
中科院分区：
文献类型：
--
作者：
Fukai, T;Tanaka, S
从具有侧抑制和自抑制的神经网络的传统膜动力学出发，导出了平均放电速率的Lotka-Volterra型神经生态方程。通过解析和数值计算，研究了接受外部输入的竞争神经网络所采用的神经选择机制。一个值得注意的发现是，侧抑制相对于自我抑制的强度对于确定三种性质不同的行为类型之间的网络的稳定状态至关重要。这两种类型的抑制性连接的同等强度会导致网络出现众所周知的赢家通吃行为。然而，如果侧抑制弱于自抑制，则一定数量的神经元在稳定状态下被激活，或者赢家的数量通常多于一(赢家共享所有行为)。另一方面，如果自我抑制弱于侧向抑制，则只有一个神经元被激活，但获胜者不一定是接受最大输入的神经元。这表明，我们的简单网络模型为理解神经选择机制提供了数学基础。
A neuroecological equation of the Lotka-Volterra type for mean firing rate is derived from the conventional membrane dynamics of a neural network with lateral inhibition and self-inhibition. Neural selection mechanisms employed by the competitive neural network receiving external inputs are studied with analytic and numerical calculations. A remarkable finding is that the strength of lateral inhibition relative to that of self-inhibition is crucial for determining the steady states of the network among three qualitatively different types of behavior. Equal strength of both types of inhibitory connections leads the network to the well-known winner-take-all behavior. If, however, the lateral inhibition is weaker than the self-inhibition, a certain number of neurons are activated in the steady states or the number of winners is in general more than one (the winners-share-all behavior). On the other hand, if the self-inhibition is weaker than the lateral one, only one neuron is activated, but the winner is not necessarily the neuron receiving the largest input. It is suggested that our simple network model provides a mathematical basis for understanding neural selection mechanisms.