Compressed and distributed sensing of neuronal activity for real time spike train decoding.
Compressed and distributed sensing of neuronal activity for real time spike train decoding.
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DOI：
10.1109/tnsre.2009.2012711
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发表时间：
2009-04
期刊：
影响因子：
--
通讯作者：
Oweiss K
中科院分区：
文献类型：
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作者：
Aghagolzadeh M;Oweiss K
多变量的点过程越来越多地被用来模拟大脑皮层的神经元反应特性。估计这些过程背后的条件强度函数对于描述和解码皮质神经元的放电模式是重要的。本文提出了一种直接从神经元细胞外记录的压缩表示来估计这些强度函数的新方法。这种方法是基于利用细胞外尖峰波形的稀疏表示，以前证明可以产生近乎最佳的去噪和压缩特性。我们表明，通过将这种稀疏表示限制到同时保留尖峰波形特征和潜在强度函数的时间特征的投影的子集，我们可以合理地近似记录的神经元的瞬时放电速率，并且在许多时间尺度上具有可变的调谐特征。这种特征非常适合从单次试验数据中检测神经元放电特征的细微时间差异。这种方法的另一个优点是，它从通常为瞬时神经解码而执行的神经信号的典型处理路径中消除了多个步骤。我们使用运动皮质活动的随机余弦调谐模型来演示该方法的解码性能，该模型在自然的、非目标定向的2-D手臂运动中进行。
Multivariate point processes are increasingly being used to model neuronal response properties in the cortex. Estimating the conditional intensity functions underlying these processes is important to characterize and decode the firing patterns of cortical neurons. This paper proposes a new approach for estimating these intensity functions directly from a compressed representation of the neurons’ extracellular recordings. The approach is based on exploiting a sparse representation of the extracellular spike waveforms, previously demonstrated to yield near-optimal denoising and compression properties. We show that by restricting this sparse representation to a subset of projections that simultaneously preserve features of the spike waveforms in addition to the temporal characteristics of the underlying intensity functions, we can reasonably approximate the instantaneous firing rates of the recorded neurons with variable tuning characteristics across a multitude of time scales. Such feature is highly desirable to detect subtle temporal differences in neuronal firing characteristics from single-trial data. An added advantage of this approach is that it eliminates multiple steps from the typical processing path of neural signals that are customarily performed for instantaneous neural decoding. We demonstrate the decoding performance of the approach using a stochastic cosine tuning model of motor cortical activity during a natural, nongoal-directed 2-D arm movement.