A network of coincidence detector neurons with periodic and chaotic dynamics
A network of coincidence detector neurons with periodic and chaotic dynamics
复制标题
具有周期性和混沌动力学的重合检测器神经元网络
DOI：
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发表时间：
2004
影响因子：
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通讯作者：
K. Aihara
中科院分区：
文献类型：
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作者：
Masataka Watanabe;K. Aihara
我们提出了一个简单的神经网络模型来理解时间脉冲编码的动态。该模型由具有均匀突触效率和随机脉冲传播延迟的符合检测器神经元组成。我们还假设了一个全局负反馈机制来控制网络的活动，导致在一定的时间窗口内发射固定数量的神经元。由于这种约束，网络状态变得定义良好，动态等效于分段非线性映射。该模型的数值模拟表明，神经元放电的潜伏期是至关重要的全球网络动力学，当突触后放电的时间是不太敏感的扰动时间的突触前尖峰，网络动力学变得稳定和周期性的，而增加的敏感性导致不稳定和混沌动力学。此外，我们引入了一个学习规则，降低吸引子的李雅普诺夫指数，扩大吸引盆。
We propose a simple neural network model to understand the dynamics of temporal pulse coding. The model is composed of coincidence detector neurons with uniform synaptic efficacies and random pulse propagation delays. We also assume a global negative feedback mechanism which controls the network activity, leading to a fixed number of neurons firing within a certain time window. Due to this constraint, the network state becomes well defined and the dynamics equivalent to a piecewise nonlinear map. Numerical simulations of the model indicate that the latency of neuronal firing is crucial to the global network dynamics; when the timing of postsynaptic firing is less sensitive to perturbations in timing of presynaptic spikes, the network dynamics become stable and periodic, whereas increased sensitivity leads to instability and chaotic dynamics. Furthermore, we introduce a learning rule which decreases the Lyapunov exponent of an attractor and enlarges the basin of attraction.
影响因子：
5.2
作者：
Greenough,WT;Anderson,BJ
通讯作者：
Anderson,BJ