Oprisan Sorinel A, Buhusi Catalin V
Department of Physics and Astronomy, College of Charleston, 66 George Street, Charleston, South Caroline 29424, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 May;87(5):052717. doi: 10.1103/PhysRevE.87.052717. Epub 2013 May 29.
Time perception in the suprasecond range is crucial for fundamental cognitive processes such as decision making, rate calculation, and planning. In the vast majority of species, behavioral manipulations, and neurophysiological manipulations, interval timing is scale invariant: the time-estimation errors are proportional to the estimated duration. The origin and mechanisms of this fundamental property are unknown. We discuss the computational properties of a circuit consisting of a large number of (input) neural oscillators projecting on a small number of (output) coincidence detector neurons, which allows time to be coded by the pattern of coincidental activation of its inputs. We show that time-scale invariance emerges from the neural noise, such as small fluctuations in the firing patterns of its input neurons and in the errors with which information is encoded and retrieved by its output neurons. In this architecture, time-scale invariance is resistant to manipulations as it depends neither on the details of the input population nor on the distribution probability of noise.
超秒范围内的时间感知对于诸如决策、速率计算和规划等基本认知过程至关重要。在绝大多数物种中，行为操纵和神经生理操纵表明，间隔计时是尺度不变的：时间估计误差与估计持续时间成正比。这一基本特性的起源和机制尚不清楚。我们讨论了一个由大量（输入）神经振荡器投射到少量（输出）重合检测神经元组成的电路的计算特性，该电路允许时间通过其输入的重合激活模式进行编码。我们表明，时间尺度不变性源于神经噪声，例如其输入神经元放电模式的小波动以及其输出神经元编码和检索信息时的误差。在这种架构中，时间尺度不变性对操纵具有抗性，因为它既不依赖于输入群体的细节，也不依赖于噪声的分布概率。
