Timing in the absence of clocks: Encoding time in neural network states
Timing in the absence of clocks: Encoding time in neural network states
复制标题
DOI：
10.1016/j.neuron.2007.01.006
复制
发表时间：
2007-02-01
期刊：
影响因子：
16.2
通讯作者：
Buonomano, Dean V.
中科院分区：
文献类型：
--
作者：
Karmarkar, Urna R.;Buonomano, Dean V.
基于感觉事件时序的决定是感觉处理的基础。然而，大脑测量时间的机制在毫秒到几秒的范围内仍然不清楚。时间处理的主要模型认为振荡器发出的事件被积分以提供时间的线性度量。我们研究了一个替代模型，在该模型中，由于网络状态的时间依赖变化，大脑皮层网络固有地能够辨别时间。使用计算机模拟，我们表明，在这个框架内，没有时间的线性度量，并且给定的间隔是在先前事件的背景下编码的。人类心理物理学研究被用来检验该模型的预测。我们的结果提供了理论和实验证据，证明在短时间间隔内，不存在时间的线性度量，并且可以在局部神经网络的高维状态下对时间进行编码。
Decisions based on the timing of sensory events are fundamental to sensory processing. However, the mechanisms by which the brain measures time over ranges of milliseconds to seconds remain unclear. The dominant model of temporal processing proposes that an oscillator emits events that are integrated to provide a linear metric of time. We examine an alternate model in which cortical networks are inherently able to tell time as a result of time-dependent changes in network state. Using computer simulations we show that within this framework, there is no linear metric of time, and that a given interval is encoded in the context of preceding events. Human psychophysical studies were used to examine the predictions of the model. Our results provide theoretical and experimental evidence that, for short intervals, there is no linear metric of time, and that time may be encoded in the high-dimensional state of local neural networks.