Department Economics of Climate Change, Technische Universität Berlin, 10623 Berlin, Germany.
Neural Comput. 2010 Jun;22(6):1493-510. doi: 10.1162/neco.2010.05-09-1016.
The timescale-invariant recognition of temporal stimulus sequences is vital for many species and poses a challenge for their sensory systems. Here we present a simple mechanistic model to address this computational task, based on recent observations in insects that use rhythmic acoustic communication signals for mate finding. In the model framework, feedforward inhibition leads to burst-like response patterns in one neuron of the circuit. Integrating these responses over a fixed time window by a readout neuron creates a timescale-invariant stimulus representation. Only two additional processing channels, each with a feature detector and a readout neuron, plus one final coincidence detector for all three parallel signal streams, are needed to account for the behavioral data. In contrast to previous solutions to the general time-warp problem, no time delay lines or sophisticated neural architectures are required. Our results suggest a new computational role for feedforward inhibition and underscore the power of parallel signal processing.
时间刺激序列的时不变识别对许多物种至关重要，这对它们的感觉系统构成了挑战。在这里，我们提出了一个简单的机械模型来解决这个计算任务，该模型基于昆虫中最近的观察结果，昆虫使用节奏性的声音通讯信号来寻找配偶。在模型框架中，前馈抑制导致电路中的一个神经元产生爆发式的反应模式。通过读取神经元对固定时间窗口内的这些反应进行积分，创建了一个时不变的刺激表示。仅需两个额外的处理通道，每个通道都有一个特征检测器和一个读取神经元，再加上三个并行信号流的一个最终符合检测器，就可以解释行为数据。与解决一般时间扭曲问题的以前的解决方案相比，不需要时间延迟线或复杂的神经结构。我们的结果为前馈抑制提供了一个新的计算作用，并强调了并行信号处理的强大功能。
