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首页> 外文期刊>IEEE Transactions on Information Theory >Directed Information Between Connected Leaky Integrate-and-Fire Neurons
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Directed Information Between Connected Leaky Integrate-and-Fire Neurons

机译：连接的泄漏整合和发射神经元之间的定向信息

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摘要

The connectivity structure between neurons is useful for determining how groups of neurons perform tasks. Directed information is a measure that can be used to infer connectivity between neurons using their recorded time series. In this paper, we develop a method of calculating the directed information rate from one neuron to another neuron it is connected to, given a particular neuronal topology. We assume a leaky integrate-and-fire (LIF) neuron model with independent and identically distributed random spike train inputs, which governs how the membrane potential of the output neuron evolves. We use this neuron model to find the dynamics of the resulting output spike train from its membrane potential dynamics, both for when the past of the input neuron is observed and when it is not. We show that an action potential in the LIF model causes a conditional independence of the activity before and after it, and we capture this conditional independence via a Markov model. We use these spike train dynamics to then calculate the directed information between the spike train of the input neuron to the spike train generated by the LIF model. In addition, we show how changing the refractory period of the LIF model affects the directed information, and also how the spike train dynamics are affected by memory constraints, which are commonly imposed in estimators of directed information.
机译：神经元之间的连接结构对于确定神经元组如何执行任务很有用。定向信息是一种可用于使用记录的时间序列来推断神经元之间的连接性的度量。在本文中，我们开发了一种在给定特定神经元拓扑的情况下计算从一个神经元到与其连接的另一个神经元的定向信息率的方法。我们假设具有独立且相同分布的随机峰值训练输入的泄漏集成点火（LIF）神经元模型，该模型控制输出神经元的膜电位如何演变。我们使用该神经元模型从其膜电位动力学中找到生成的输出尖峰序列的动力学，包括何时观察到输入神经元的过去以及何时观察不到输入神经元的过去。我们表明，LIF模型中的动作电位会导致活动前后的条件独立性，并且我们通过马尔可夫模型捕获了这种条件独立性。我们使用这些峰值序列动力学来计算输入神经元的峰值序列与LIF模型生成的峰值序列之间的有向信息。此外，我们展示了如何更改LIF模型的不应期如何影响定向信息，以及尖峰序列动态如何受存储约束（通常在定向信息的估计器中施加）的影响。

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