An analysis of hippocampal spatio-temporal representations using a Bayesian algorithm for neural spike train decoding
An analysis of hippocampal spatio-temporal representations using a Bayesian algorithm for neural spike train decoding
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DOI：
10.1109/tnsre.2005.847368
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发表时间：
2005-06-01
影响因子：
4.9
通讯作者：
Brown, EN
中科院分区：
文献类型：
--
作者：
Barbieri, R;Wilson, MA;Brown, EN
神经锋电位序列解码算法是表征神经元集合如何表示生物信号的重要工具。我们提出了一个贝叶斯神经尖峰序列解码算法的基础上的点过程模型的单个神经元，一个线性随机状态空间模型的生物信号，和一个时间延迟参数。延迟参数表示生物信号与总体尖峰活动之间的时间超前或滞后。我们使用该算法来研究大鼠海马CA1区锥体神经元的整体尖峰活动对位置的表征是否更符合前瞻编码，即，未来位置，或追溯编码，过去位置。使用44个同时记录的神经元和400 ms的整体延迟潜伏期，在10分钟的觅食在一个开放的圆形环境中的中位数解码错误为5.1厘米。该算法的0.95置信区域的真实覆盖概率为0.71。这些结果说明了贝叶斯神经锋电位序列解码范式可以用来调查时空表示的位置由海马神经元的合奏。
Neural spike train decoding algorithms are important tools for characterizing how ensembles of neurons represent biological signals. We present a Bayesian neural spike train decoding algorithm based on a point process model of individual neurons, a linear stochastic state-space model of the biological signal, and a temporal latency parameter. The latency parameter represents the temporal lead or lag between the biological signal and the ensemble spiking activity. We use the algorithm to study Whether the representation of position by the ensemble spiking activity of pyramidal neurons in the CA1 region of the rat hippocampus is more consistent with prospective coding, i.e., future position, or retrospective coding, past position. Using 44 simultaneously recorded neurons and an ensemble delay latency of 400 ms, the median decoding error was 5.1 cm during 10 min of foraging in an open circular environment. The true coverage probability for the algorithm's 0.95 confidence regions was 0.71. These results illustrate how the Bayesian neural spike train decoding paradigm may be used to investigate spatio-temporal representations of position by an ensemble of hippocampal neurons.