Sparse model-based estimation of functional dependence in high-dimensional field and spike multiscale networks
Sparse model-based estimation of functional dependence in high-dimensional field and spike multiscale networks
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DOI：
10.1088/1741-2552/ab225b
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发表时间：
2019-10-01
影响因子：
4
通讯作者：
Shanechi, Maryam M.
中科院分区：
文献类型：
--
作者：
Bighamian, Ramin;Wong, Yan T.;Shanechi, Maryam M.
目标。行为编码跨越多个脑活动尺度，从二值神经元尖峰到包括局部场电位（LFP）在内的连续场。多尺度模型需要描述同时记录的尖峰和场信号的行为编码和条件依赖关系，从而形成一个高维的多尺度网络。然而，在高维记录中学习峰场依赖性是具有挑战性的，因为有大量的峰场信号对，这使得标准的学习技术容易过度拟合。的方法。我们提出了一种基于稀疏模型的估计算法来学习这些多尺度网络依赖关系。我们开发了一个多尺度编码模型，包括每个神经元的二进制尖峰点过程模型，其放电率是LFP网络特征和行为状态的函数。这样，spike-field依赖关系就构成了要学习的模型参数。我们通过形成一个约束优化问题来解决参数学习挑战，以最大化具有L1惩罚项的似然性，从而简化了对显著spike-LFP依赖性的检测。然后，我们应用Akaike信息准则（AIC）来强制模型中非零依赖参数的稀疏数量。主要的结果。我们使用模拟和两种非人类灵长类动物（NHP）在3D运动任务（运动皮层记录）和前扫视任务（前额叶记录）中的spike-field数据来验证该算法。我们发现，通过识别具有稀疏依赖参数集的模型，与没有依赖的模型相比，该算法提高了尖峰预测。此外，与标准方法相比，该算法识别的依赖参数明显减少，同时由于检测到更少的虚假依赖，可能提高了它们的峰值预测。此外，与仅使用同一电极上的LFP特征相比，通过包含来自所有电极的LFP特征，任何电极上的尖峰预测都得到了改善。最后，与标准方法不同，该算法揭示了作为距离、大脑区域和频带的函数的spike-field网络依赖模式。的意义。该算法有助于研究高维峰场网络中的函数依赖关系，从而得到更精确的多尺度编码模型。
Objective. Behavior is encoded across multiple scales of brain activity, from binary neuronal spikes to continuous fields including local field potentials (LFP). Multiscale models need to describe both the encoding of behavior and the conditional dependencies in simultaneously recorded spike and field signals, which form a high-dimensional multiscale network. However, learning spike-field dependencies in high-dimensional recordings is challenging due to the prohibitively large number of spike-field signal pairs, which makes standard learning techniques subject to overfitting. Approach. We present a sparse model-based estimation algorithm to learn these multiscale network dependencies. We develop a multiscale encoding model consisting of a point process model of binary spikes for each neuron whose firing rate is a function of the LFP network features and behavioral states. Doing so, spike-field dependencies constitute the model parameters to be learned. We resolve the parameter learning challenge by forming a constrained optimization problem to maximize the likelihood with an L1 penalty term that eases the detection of significant spike-LFP dependencies. We then apply the Akaike information criterion (AIC) to force a sparse number of nonzero dependency parameters in the model. Main results. We validate the algorithm using simulations and spike-field data from two non-human primates (NHP) in a 3D motor task with motor cortical recordings and a pro-saccade visual task with prefrontal recordings. We find that by identifying a model with a sparse set of dependency parameters, the algorithm improves spike prediction compared with models without dependencies. Further, the algorithm identifies significantly fewer dependency parameters compared with standard methods while improving their spike prediction likely due to detecting fewer spurious dependencies. Also, spike prediction on any electrode improves by including LFP features from all electrodes compared with using only those on the same electrode. Finally, unlike standard methods, the algorithm uncovers patterns of spike-field network dependencies as a function of distance, brain region, and frequency band. Significance. This algorithm can help study functional dependencies in high-dimensional spike-field networks and leads to more accurate multiscale encoding models.