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The stochastic properties of input spike trains control neuronal arithmetic

机译:输入穗序列的随机特性控制神经元算法

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In the nervous system, the representation of signals is based predominantly on the rate and timing of neuronal discharges. In most everyday tasks, the brain has to carry out a variety of mathematical operations on the discharge patterns. Recent findings show that even single neurons are capable of performing basic arithmetic on the sequences of spikes. However, the interaction of the two spike trains, and thus the resulting arithmetic operation may be influenced by the stochastic properties of the interacting spike trains. If we represent the individual discharges as events of a random point process, then an arithmetical operation is given by the interaction of two point processes. Employing a probabilistic model based on detection of coincidence of random events and complementary computer simulations, we show that the point process statistics control the arithmetical operation being performed and, particularly, that it is possible to switch from subtraction to division solely by changing the distribution of the inter-event intervals of the processes. Consequences of the model for evaluation of binaural information in the auditory brainstem are demonstrated. The results accentuate the importance of the stochastic properties of neuronal discharge patterns for information processing in the brain; further studies related to neuronal arithmetic should therefore consider the statistics of the interacting spike trains.
机译:在神经系统中,信号的表示主要基于神经元放电的速度和时间。在大多数日常任务中,大脑必须对放电模式进行各种数学运算。最近的发现表明,即使单个神经元也能够对尖峰序列执行基本的算术运算。然而,两个尖峰列的相互作用,以及因此产生的算术运算可能受到相互作用的尖峰列的随机特性的影响。如果我们将单个放电表示为随机点过程的事件,则通过两个点过程的相互作用来进行算术运算。利用基于随机事件的一致性检测和互补计算机模拟的概率模型,我们显示了点过程统计信息控制着正在执行的算术运算,特别是,仅通过改变点的分布就可以从减法转换为除法。进程之间的事件间隔。证明了模型对听觉脑干中双耳信息评估的后果。结果强调了神经元放电模式的随机特性对于大脑中信息处理的重要性;因此,与神经元算术有关的进一步研究应考虑相互作用的尖峰序列的统计信息。

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