Complexity What are some efforts to build more biologically plausible neural networks

Information integration is a measure, developed by Tononi and co-researchers, of the capacity for dynamic neural networks to be in informational states ... ... informational differences among them, efficiently coordinating and integrating the sub-prediction results. Secondly, a two-stage predictive model selection ... in the upper network level. Moreover, in a slightly different direction, research on multi-task learning neural networks, an approach aiming at internal ... Jan 29, 2024 ... We will see how ANN-assisted mixture space exploration can adapt to dynamic mixtures and balance the local and global information. Section 3 ... ... information is transformed into valuable information with neural net. The individual element of the neural network, i.e., the living neuronal cell, is ... Keywords: Feedforward neural network, complexity, information complexity, neural complexity, radial basis functions, RBF networks, learning. 1. Introduction. Classically, external receptors of the body transmit information from the environment to the cerebral cortex via the thalamus. This review explains and ... Sep 10, 2019 ... Using a genetic algorithm approach to find optimal neural network structures for model-free policy learning in reinforcement learning; Using ... Apr 28, 2021 ... First, the Information Bottleneck Principle is not the same as bottleneck layers in networks like auto-encoders. These can serve as information ... Neural networks can realize conditional distributions of the form p(y. z I x. u) specified by network parameter u. The set of such realizable distributions is a ...