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Optimal quantization for energy-efficient information transfer in a population of neuron-like devices

机译:最佳量化用于神经元样器群中的节能信息转移

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Suprathreshold Stochastic Resonance (SSR) is a recently discovered form of stochastic resonance that occurs in populations of neuron-like devices. A key feature of SSR is that all devices in the population possess identical threshold nonlinearities. It has previously been shown that information transmission through such a system is optimized by nonzero internal noise. It is also clear that it is desirable for the brain to transfer information in an energy efficient manner. In this paper we discuss the energy efficient maximization of information transmission for the case of variable thresholds and constraints imposed on the energy available to the system, as well as minimization of energy for the case of a fixed information rate. We aim to demonstrate that under certain conditions, the SSR configuration of all devices having identical thresholds is optimal. The novel feature of this work is that optimization is performed by finding the optimal threshold settings for the population of devices, which is equivalent to solving a noisy optimal quantization problem.
机译:Suprathreshold随机共振(SSR)是最近被发现的随机共振形式,发生在神经元样器件的群体中。 SSR的一个关键特征是,人口中的所有设备都具有相同的阈值非线性。先前已经表明,通过非零内噪声优化了这种系统的信息传输。还显然,大脑希望以节能的方式转移信息。在本文中,我们讨论了对系统可用的能量的可变阈值和限制的情况的信息传输的能量有效最大化,以及用于固定信息速率的情况的能量最小化。我们的目标是证明在某些条件下,具有相同阈值的所有设备的SSR配置是最佳的。这项工作的新颖特征是通过查找设备群的最佳阈值设置来执行优化,这相当于解决嘈杂的最佳量化问题。

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