Dynamic analysis of neural encoding by point process adaptive filtering
Dynamic analysis of neural encoding by point process adaptive filtering
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DOI：
10.1162/089976604773135069
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发表时间：
2004-05-01
影响因子：
2.9
通讯作者：
Brown, EN
中科院分区：
文献类型：
--
作者：
Eden, UT;Frank, LM;Brown, EN
神经感受野是动态的，因为随着经验，神经元改变其对相关刺激的尖峰响应。为了理解神经系统如何适应生物信息的表征，从实验测量中分析感受野可塑性至关重要。自适应信号处理是一门成熟的工程学科，用于表征系统参数的时间演变，它提出了一个研究感受野可塑性的框架。我们使用贝叶斯规则Chapman-Kolmogorov范式与线性状态方程和点过程观测模型，以获得自适应滤波器适合从神经尖峰序列估计。我们推导出点过程滤波器类似的卡尔曼滤波器，递归最小二乘法，和最速下降算法，并描述这些新的过滤器的属性。我们说明我们的算法在两个模拟数据的例子。第一个是对海马神经元空间感受野的缓慢和快速演变的研究。第二种是自适应解码研究，其中信号从合奏神经尖峰活动解码为合奏中的神经元的感受野演变。我们的研究结果提供了一个范例，自适应估计点过程的观察，并建议一个实用的方法来构建过滤算法，以跟踪神经感受野动态的毫秒时间尺度。
Neural receptive fields are dynamic in that with experience, neurons change their spiking responses to relevant stimuli. To understand how neural systems adapt their representations of biological information, analyses of receptive field plasticity from experimental measurements are crucial. Adaptive signal processing, the well-established engineering discipline for characterizing the temporal evolution of system parameters, suggests a framework for studying the plasticity of receptive fields. We use the Bayes' rule Chapman-Kolmogorov paradigm with a linear state equation and point process observation models to derive adaptive filters appropriate for estimation from neural spike trains. We derive point process filter analogues of the Kalman filter, recursive least squares, and steepest-descent algorithms and describe the properties of these new filters. We illustrate our algorithms in two simulated data examples. The first is a study of slow and rapid evolution of spatial receptive fields in hippocampal neurons. The second is an adaptive decoding study in which a signal is decoded from ensemble neural spiking activity as the receptive fields of the neurons in the ensemble evolve. Our results provide a paradigm for adaptive estimation for point process observations and suggest a practical approach for constructing filtering algorithms to track neural receptive field dynamics on a millisecond timescale.