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Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains

机译:峰值时间依赖于可塑性找到了连续峰值列车中重复模式的开始

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摘要

Experimental studies have observed Long Term synaptic Potentiation (LTP) when a presynaptic neuron fires shortly before a postsynaptic neuron, and Long Term Depression (LTD) when the presynaptic neuron fires shortly after, a phenomenon known as Spike Timing Dependant Plasticity (STDP). When a neuron is presented successively with discrete volleys of input spikes STDP has been shown to learn ‘early spike patterns’, that is to concentrate synaptic weights on afferents that consistently fire early, with the result that the postsynaptic spike latency decreases, until it reaches a minimal and stable value. Here, we show that these results still stand in a continuous regime where afferents fire continuously with a constant population rate. As such, STDP is able to solve a very difficult computational problem: to localize a repeating spatio-temporal spike pattern embedded in equally dense ‘distractor’ spike trains. STDP thus enables some form of temporal coding, even in the absence of an explicit time reference. Given that the mechanism exposed here is simple and cheap it is hard to believe that the brain did not evolve to use it.
机译:实验研究已经观察到突触前神经元在突触后神经元之前不久触发时的长期突触增强(LTP),以及突触前神经元在突触后神经元之后不久触发时的长期压抑(LTD),这种现象称为尖峰时序依赖可塑性(STDP)。当神经元连续出现离散的输入尖峰时,STDP已显示出学习“早期尖峰模式”,也就是说,将突触权重集中在持续早期发射的传入神经上,结果是突触后尖峰潜伏期减小,直到达到最小且稳定的价值。在这里,我们显示出这些结果仍然处于连续状态,其中传入者以恒定的人口比率连续射击。这样,STDP能够解决一个非常困难的计算问题:将重复的时空峰值模式定位在同样密集的“干扰物”峰值序列中。因此,即使没有明确的时间参考,STDP仍可以实现某种形式的时间编码。考虑到这里公开的机制既简单又便宜,因此很难相信大脑并没有进化为使用它。

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