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首页> 外文期刊>Journal of Computational Neuroscience >Neurons as ideal change-point detectors
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Neurons as ideal change-point detectors

机译：神经元是理想的变化点检测器

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摘要

Every computational unit in the brain monitors incoming signals, instant by instant, for meaningful changes in the face of stochastic fluctuation. Recent studies have suggested that even a single neuron can detect changes in noisy signals. In this paper, we demonstrate that a single leaky integrate-and-fire neuron can achieve change-point detection close to that of theoretical optimal, for uniform-rate process, functions even better than a Bayes-optimal algorithm when the underlying rate deviates from a presumed uniform rate process. Given a reasonable number of synaptic connections (order 10~4) and the rate of the input spike train, the values of the membrane time constant and the threshold found for optimizing change-point detection are close to those seen in biological neurons. These findings imply that biological neurons could act as sophisticated change-point detectors.
机译：大脑中的每个计算单元都会即时监视输入信号，以应对随机波动情况下的有意义变化。最近的研究表明，即使是单个神经元也可以检测到噪声信号的变化。在本文中，我们证明了单个泄漏的积分并发射神经元可以实现接近理论最优值的变化点检测，对于均匀速率过程，当基础速率偏离时，其性能甚至优于贝叶斯最优算法。假定的统一费率过程。给定合理数量的突触连接（10〜4阶）和输入尖峰序列的速率，发现膜时间常数的值和优化变化点检测的阈值与生物神经元中的值接近。这些发现暗示生物神经元可以充当复杂的变化点检测器。

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