Encoding for computation: Recognizing brief dynamical patterns by exploiting effects of weak rhythms on action-potential timing
Encoding for computation: Recognizing brief dynamical patterns by exploiting effects of weak rhythms on action-potential timing
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DOI：
10.1073/pnas.0401125101
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发表时间：
2004-04-20
影响因子：
11.1
通讯作者：
Hopfield, JJ
中科院分区：
文献类型：
--
作者：
Hopfield, JJ
许多刺激只有作为随时间变化的模式才有意义。大多数听觉和许多视觉刺激都是这种性质，可以被描述为多维的，时间依赖的向量。一个简单的神经元可以将向量的单个分量编码为一个触发率。一个小的亚阈值振荡电流的添加扰动的动作电位的时序，编码的信号也在一个时序关系，与共存的放电率表示的影响不大。当亚阈值信号对于一组神经元是共同的时，基于时序的信息对于从该组接收输入的神经元是重要的。这种信息编码允许简单地实现利用速率编码不容易完成的计算。这些想法是通过使用语音提供一个现实的输入信号，以生物启发的尖峰神经元模型网络进行检查。两层系统的输出神经元被示出为专门编码语音的短语言元素。
Many stimuli have meaning only as patterns over time. Most auditory and many visual stimuli are of this nature and can be described as multidimensional, time-dependent vectors. A simple neuron can encode a single component of the vector in a firing rate. The addition of a small subthreshold oscillatory current perturbs the action-potential timing, encoding the signal also in a timing relationship, with little effect on the coexisting firing rate representation. When the subthreshold signal is common to a group of neurons, the timing-based information is significant to neurons receiving inputs from the group. This information encoding allows simple implementation of computations not readily done with rate coding. These ideas are examined by using speech to provide a realistic input signal to a biologically inspired model network of spiking neurons. The output neurons of the two-layer system are shown to specifically encode short linguistic elements of speech.