Sadras Nitin, Shanechi Maryam M
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2012-2015. doi: 10.1109/EMBC.2018.8512598.
The point-process filter (PPF) is a real-time recursive algorithm that computes the minimum mean-squared error estimate of a behavioral state, given neural spiking observations. When used with stimulus-sensitive neurons that represent behavioral states transiently, the PPF needs to know the times at which stimuli will occur. However, these times will not be known a-priori. In this work, we develop a matched-filter point process filter (MF-PPF) that can decode behavioral states that are encoded transiently in neural activity when stimulus times are unknown. A linear filter matched to each neuron's temporal receptive field is used to estimate stimulus onset times, which are then fed into the PPF to decode the behavioral state. As an example, we use the MF-PPF to decode visual saliency from simulated superior colliculus spiking activity. This new decoder has the potential to decode behavioral states from brain regions with transient representations and temporal receptive fields.
点过程滤波器（PPF）是一种实时递归算法，它在给定神经放电观测值的情况下，计算行为状态的最小均方误差估计。当与瞬时表示行为状态的刺激敏感神经元一起使用时，PPF需要知道刺激发生的时间。然而，这些时间是无法先验得知的。在这项工作中，我们开发了一种匹配滤波器点过程滤波器（MF-PPF），当刺激时间未知时，它可以解码在神经活动中瞬时编码的行为状态。与每个神经元的时间感受野相匹配的线性滤波器用于估计刺激开始时间，然后将其输入到PPF中以解码行为状态。例如，我们使用MF-PPF从模拟的上丘放电活动中解码视觉显著性。这种新的解码器有潜力从具有瞬时表示和时间感受野的脑区解码行为状态。
