Latent state-space models for neural decoding.
Latent state-space models for neural decoding.
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DOI：
10.1109/embc.2014.6944262
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Truccolo W
中科院分区：
文献类型：
--
作者：
Aghagolzadeh M;Truccolo W
运动皮质中单个神经元的集合可以显示出强大的低维集体动力学。在这项研究中，我们探索了一种方法，其中神经解码应用于估计的低维动力学，而不是应用于完整记录的神经元群体。一种潜在状态空间模型(SSM)方法被用来从测量的神经元群体中的峰活动来估计低维神经动力学。然后使用第二状态空间模型表示通过卡尔曼滤波从估计的低维动态中解码。基于潜伏期SSM的解码方法在一只猴子进行自然的3-D伸展和抓握动作时从初级运动皮质记录的神经元活动上得到说明。我们的分析表明，基于估计的低维动力学的3-D REACH译码性能与基于完整记录的神经元种群的译码性能相当。
Ensembles of single-neurons in motor cortex can show strong low-dimensional collective dynamics. In this study, we explore an approach where neural decoding is applied to estimated low-dimensional dynamics instead of to the full recorded neuronal population. A latent state-space model (SSM) approach is used to estimate the low-dimensional neural dynamics from the measured spiking activity in population of neurons. A second state-space model representation is then used to decode, via a Kalman filter, from the estimated low-dimensional dynamics. The latent SSM-based decoding approach is illustrated on neuronal activity recorded from primary motor cortex in a monkey performing naturalistic 3-D reach and grasp movements. Our analysis show that 3-D reach decoding performance based on estimated low-dimensional dynamics is comparable to the decoding performance based on the full recorded neuronal population.