Bures Zbynek
College of Polytechnics, Tolsteho 16, 58601, Jihlava, Czech Republic.
Biol Cybern. 2012 Feb;106(2):111-22. doi: 10.1007/s00422-012-0483-9. Epub 2012 Mar 30.
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.
在神经系统中,信号的表征主要基于神经元放电的速率和时间。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。最近的研究结果表明,即使是单个神经元也能够对脉冲序列执行基本的算术运算。然而,两个脉冲序列的相互作用以及由此产生的算术运算可能会受到相互作用的脉冲序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,那么算术运算是由两个点过程的相互作用给出的。通过基于随机事件重合检测的概率模型和补充的计算机模拟,我们表明点过程统计控制着正在执行的算术运算,特别是仅通过改变过程的事件间隔分布就可以从减法切换到除法。证明了该模型对评估听觉脑干中双耳信息的影响。结果强调了神经元放电模式的随机特性对大脑信息处理的重要性;因此,与神经元算术相关的进一步研究应考虑相互作用的脉冲序列的统计特性。