摘要翻译：
结合神经元模型受Hodgkin-Huxley型点神经元数值模拟的启发，也受泄漏积分-点火模型的启发。在结合神经元中，输入的轨迹被记住一段固定的时间，之后它就完全消失了。这与上述两个模型形成对比，后者突触后电位呈指数衰减，只有在触发后才能被遗忘。结合神经元记忆的有限性使得人们可以构造快速的递归网络来进行计算机建模。最近，当结合神经元以泊松输入流驱动时，有限性被用来精确地描述输出随机过程。本文考虑了最简单的网络连接神经元。也就是说，期望单个神经元的每一个输出尖峰都立即输入到其输入。对于这种结构，在外部输入泊松流的情况下，如果结合神经元的阈值为2时，输出流用串行间隔概率密度分布来表征。对于较高的阈值，用数值计算分布。并与无反馈约束神经元的分布和漏积分器的分布进行了比较。数值计算了带反馈的漏积分器的样本分布。有人认为，即使是最简单的网络也能从根本上改变spikng统计数据。讨论了单个神经元水平上的信息凝聚。
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英文标题：
《Output Stream of Binding Neuron with Feedback》
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作者：
Alexander K. Vidybida
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最新提交年份：
2007
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Neurons and Cognition        神经元与认知
分类描述：Synapse, cortex, neuronal dynamics, neural network, sensorimotor control, behavior, attention
突触，皮层，神经元动力学，神经网络，感觉运动控制，行为，注意
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一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  The binding neuron model is inspired by numerical simulation of Hodgkin-Huxley-type point neuron, as well as by the leaky integrate-and-fire model. In the binding neuron, the trace of an input is remembered for a fixed period of time after which it disappears completely. This is in the contrast with the above two models, where the postsynaptic potentials decay exponentially and can be forgotten only after triggering. The finiteness of memory in the binding neuron allows one to construct fast recurrent networks for computer modeling. Recently, the finiteness is utilized for exact mathematical description of the output stochastic process if the binding neuron is driven with the Poissonian input stream. In this paper, the simplest networking is considered for binding neuron. Namely, it is expected that every output spike of single neuron is immediately fed into its input. For this construction, externally fed with Poissonian stream, the output stream is characterized in terms of interspike interval probability density distribution if the binding neuron has threshold 2. For higher thresholds, the distribution is calculated numerically. The distributions are compared with those found for binding neuron without feedback, and for leaky integrator. Sample distributions for leaky integrator with feedback are calculated numerically as well. It is oncluded that even the simplest networking can radically alter spikng statistics. Information condensation at the level of single neuron is discussed.
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PDF链接：
https://arxiv.org/pdf/0706.0163