Effects of parity, frustration, and stochastic fluctuations on integrated conceptual information for networks with two small-sized loops.

This paper presents an evaluation of the system-level integrated conceptual information of a major complex for a small-scale network containing two loops in accordance with the integrated information theory 3.0 framework. We focus on the following parameters characterizing the system model: (1) number of nodes in the loop, (2) frustration of the loop, and (3) temperature controlling the stochastic fluctuation of the state transition.

Statistical mechanics of lossy data compression using a nonmonotonic perceptron.

The performance of a lossy data compression scheme for uniformly biased Boolean messages is investigated via methods of statistical mechanics. Inspired by a formal similarity to the storage capacity problem in neural network research, we utilize a perceptron of which the transfer function is appropriately designed in order to compress and decode the messages. Employing the replica method, we analytically show that our scheme can achieve the optimal performance known in the framework of lossy compression in most cases when the code length becomes infinite.