神经峰电位序列的潜变量动力学Ising模型
Latent kinetic Ising models of neural spike trains
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中文总结 AI 辅助
针对神经峰电位序列推断，提出潜变量动力学Ising模型SpiKIsing，分离集体网络动力学与单神经元历史效应，通过变分EM算法联合推断，在合成及漏积分发放网络数据上验证了连接恢复与神经元分类能力。
中文摘要 AI 辅助
从神经元峰电位序列中推断有向有效相互作用是统计物理和计算神经科学中的一个核心逆问题。动力学Ising模型为此任务提供了一个易处理的框架，但其在神经数据上的应用通常需要将峰电位序列分箱为二元活动变量，这丢弃了箱内时间信息，并将集体网络动力学与单神经元历史效应混为一谈。我们引入了SpiKIsing，一个潜变量模型，将这两个描述层次分开。一个离散时间的不对称动力学Ising模型描述集体网络活动，而连续时间、依赖于历史的点过程在给定潜状态的条件下生成观测到的峰电位，明确考虑了不应期和峰电位后恢复。我们推导了一个变分平均场期望最大化方案，其中点过程似然作为有效观测场进入，从而能够联合推断潜活动、网络耦合和发射参数。该框架自然扩展到具有结构化先验的最大后验推断，包括稀疏性和偏好Dale一致外向相互作用的分层扩展。我们在匹配的合成数据上验证了参数恢复，并在由基于电导的漏积分发放网络生成的峰电位序列上测试了该方法。在此设置中，尽管数据生成动力学与SpiKIsing模型之间存在显著不匹配，SpiKIsing仍能恢复稀疏连接结构，并正确分类所有兴奋性和抑制性神经元。
英文摘要
Inferring directed effective interactions from neuronal spike trains is a central inverse problem in statistical physics and computational neuroscience. Kinetic Ising models provide a tractable framework for this task, but their application to neural data typically requires binning spike trains into binary activity variables, discarding within-bin timing and conflating collective network dynamics with single-neuron history effects. We introduce SpiKIsing, a latent-variable model that separates these two levels of description. A discrete-time asymmetric kinetic Ising model describes collective network activity, while continuous-time, history-dependent point processes generate the observed spikes conditional on the latent states, accounting explicitly for refractoriness and post-spike recovery. We derive a variational mean-field expectation-maximization scheme in which the point-process likelihood enters as an effective observation field, enabling joint inference of latent activity, network couplings, and emission parameters. The framework extends naturally to maximum-a-posteriori inference with structured priors, including sparsity and a hierarchical extension favouring Dale-consistent outgoing interactions. We validate parameter recovery on matched synthetic data and test the method on spike trains generated by a recurrent conductance-based leaky integrate-and-fire network. In this setting, SpiKIsing recovers sparse connection structure and correctly classifies all excitatory and inhibitory neurons despite the substantial mismatch between the data-generating dynamics and the SpiKIsing model.
发表机构
- College of Engineering and Physical Sciences, Aston University（阿斯顿大学工程与物理科学学院）
机构由 AI 辅助整理，请以论文原文为准。
