title: Transmission efficiency in ring, brain inspired neuronal networks. Information and energetic aspects.
abstract: Organisms often evolve as compromises, and many of these compromises can be expressed in terms of energy efficiency. Thus, many authors analyze energetic costs processes during information transmission in the brain. In this paper we study information transmission rate per energy used in a class of ring, brain inspired neural networks, which we assume to involve components like excitatory and inhibitory neurons or long-range connections. Choosing model of neuron we followed a probabilistic approach proposed by Levy and Baxter (2002), which contains all essential qualitative mechanisms participating in the transmission process and provides results consistent with physiologically observed values. Our research shows that all network components, in broad range of conditions, significantly improve the information-energetic efficiency. It turned out that inhibitory neurons can improve the information-energetic transmission efficiency by 50%, while long-range connections can improve the efficiency even by 70%. We also found that the most effective is the network with the smallest size: we observed that two times increase of the size can cause even three times decrease of the information-energetic efficiency. This article is part of a Special Issue entitled Neural Coding 2012.
authors.affiliation: Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, Bydgoszcz, Kopernika 1, Poland. Electronic address: bartekp@ukw.edu.pl.
authors.forename: Bartosz
authors.initials: B
authors.lastname: Paprocki
authors.forename: Janusz
authors.initials: J
authors.lastname: Szczepanski
mesh_terms: D004734:Energy Metabolism; D007257:Information Theory; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*
keywords: Brain inspired network; Information transmission efficiency; Inhibitory neuron; Long-range connection; Mutual information; Neuronal computation