ASSOCIATIVE MEMORY IN A NETWORK OF SPIKING NEURONS
ASSOCIATIVE MEMORY IN A NETWORK OF SPIKING NEURONS
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DOI：
10.1088/0954-898x/3/2/004
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发表时间：
1992-05-01
影响因子：
7.8
通讯作者：
VANHEMMEN, JL
中科院分区：
文献类型：
--
作者：
GERSTNER, W;VANHEMMEN, JL
Hopfield网络提供了神经元结构中的联想记忆的简单模型。然而，它是基于高度人为的假设，特别是使用正式的两状态神经元或分级反应神经元。在这篇论文中，我们讨论了如果形式神经元被一个“尖峰”神经元模型取代会发生什么的问题。我们分两步完成这项工作。首先，我们展示了如何在一个简单的神经元放电阈值模型中包含不应性和噪声。由这种模型产生的棘波序列重现了真实神经元中的棘波间期和增益函数的分布。在第二步中，我们连接模型神经元，以形成一个大型联想记忆系统。棘波的传递由一个突触核来描述，它包括轴突延迟、‘Hebbian’突触效率和真实的突触后反应。系统的集体行为是通过一组动力学方程来预测的，这些方程在必须存储有限数量的模式的大型且完全连接的网络的极限内是精确的。结果表明，在平稳恢复状态下，脉冲动力学的统计特性被完全消除，系统退化为一个由分级反应神经元组成的网络。然而，在振荡提取状态的情况下，神经元的尖峰噪声和内部时间常数变得重要，并决定系统的行为。
The Hopfield network provides a simple model of an associative memory in a neuronal structure. It is, however, based on highly artificial assumptions, especially the use of formal two-state neurons or graded-response neurons. In this paper we address the question of what happens if formal neurons are replaced by a model of 'spiking' neurons. We do so in two steps. First, we show how to include refractoriness and noise into a simple threshold model of neuronal spiking. The spike trains resulting from such a model reproduce the distribution of interspike intervals and gain functions found in real neurons. In a second step we connect the model neurons so as to form a large associative memory system. The spike transmission is described by a synaptic kernel which includes axonal delays, 'Hebbian' synaptic efficacies, and a realistic postsynaptic response. The collective behaviour of the system is predicted by a set of dynamical equations which are exact in the limit of a large and fully connected network that has to store a finite number of patterns. We show that in a stationary retrieval state the statistics of the spiking dynamics is completely wiped out and the system reduces to a network of graded-response neurons. In the case of an oscillatory retrieval state, however, the spiking noise and the internal time constants of the neurons become important and determine the behaviour of the system.