Optimal Change-Detection and Spiking Neurons
Optimal Change-Detection and Spiking Neurons
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最佳变化检测和尖峰神经元
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发表时间：
2006
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通讯作者：
Angela J. Yu
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作者：
Angela J. Yu
在一个不稳定的，潜在的敌对环境中生存需要动物迅速而准确地检测感官变化，这是两个经常相互竞争的需求。服从这种检测的神经元面临着相应的挑战，即尽可能快地辨别输入中的“真实的”变化，同时忽略噪声波动。从数学上讲，这是一个变化检测问题的例子，在受控随机过程社区中正在积极研究。在本文中，我们利用该社区开发的复杂工具，形式化的神经系统所面临的问题的实例化，并在一定的假设下，贝叶斯最优决策策略的特征。我们将从这个最佳策略中推导出一个信息积累和决策过程，它非常类似于一个泄漏的积分和激发神经元的动力学。这种对应关系表明，神经元被优化用于跟踪输入变化，并揭示了细胞内特性的计算输入，如静息膜电位，电压依赖性电导和尖峰后复位电压。我们还探讨了时机、不确定性、神经调节和奖励等因素对神经元动力学和敏感性的影响，因为最佳决策策略关键取决于这些因素。
Survival in a non-stationary, potentially adversarial environment requires animals to detect sensory changes rapidly yet accurately, two oft competing desiderata. Neurons subserving such detections are faced with the corresponding challenge to discern "real" changes in inputs as quickly as possible, while ignoring noisy fluctuations. Mathematically, this is an example of a change-detection problem that is actively researched in the controlled stochastic processes community. In this paper, we utilize sophisticated tools developed in that community to formalize an instantiation of the problem faced by the nervous system, and characterize the Bayes-optimal decision policy under certain assumptions. We will derive from this optimal strategy an information accumulation and decision process that remarkably resembles the dynamics of a leaky integrate-and-fire neuron. This correspondence suggests that neurons are optimized for tracking input changes, and sheds new light on the computational import of intracellular properties such as resting membrane potential, voltage-dependent conductance, and post-spike reset voltage. We also explore the influence that factors such as timing, uncertainty, neuromodulation, and reward should and do have on neuronal dynamics and sensitivity, as the optimal decision strategy depends critically on these factors.