Learning with Precise Spike Times: A New Decoding Algorithm for Liquid State Machines.
Learning with Precise Spike Times: A New Decoding Algorithm for Liquid State Machines.
复制标题
通过精确的尖峰时间学习：一种新的液态状态机解码算法。
DOI：
10.1162/neco_a_01218
复制
发表时间：
2019
影响因子：
2.9
通讯作者：
Florescu D
中科院分区：
文献类型：
--
作者：
Florescu D
大量证据表明，生物神经网络按照神经元产生和传输的尖峰的精确时间对信息进行编码，这与基于速率的代码相比具有多个优势。在这里，我们采用了一个向量空间公式的尖峰序列，并引入了一个新的液态机（LSM）网络架构和一个新的前向正交回归算法学习的输入输出信号映射或解码的大脑活动。该算法使用精确的尖峰时间来选择与每个学习任务相关的突触前神经元。我们表明，使用精确的尖峰定时来训练LSM和选择读出突触前神经元导致二进制分类任务的性能显着增加，从多电极阵列记录中解码神经活动，以及在语音识别任务中，与使用标准架构和训练方法相比。
There is extensive evidence that biological neural networks encode information in the precise timing of the spikes generated and transmitted by neurons, which offers several advantages over rate-based codes. Here we adopt a vector space formulation of spike train sequences and introduce a new liquid state machine (LSM) network architecture and a new forward orthogonal regression algorithm to learn an input-output signal mapping or to decode the brain activity. The proposed algorithm uses precise spike timing to select the presynaptic neurons relevant to each learning task. We show that using precise spike timing to train the LSM and selecting the readout presynaptic neurons leads to a significant increase in performance on binary classification tasks, in decoding neural activity from multielectrode array recordings, as well as in a speech recognition task, compared with what is achieved using the standard architecture and training methods.