Energy‐efficient neural information processing in individual neurons and neuronal networks. Issue 11 (22nd August 2017)

• Record Type: Journal Article
• Authors: Yu, Lianchun; Yu, Yuguo
• Other Names: Schousboe Arne guestEditor.; Yu Albert C. H. guestEditor.; McKenna Mary C. guestEditor.
• Is Part Of: Journal of neuroscience research. Volume 95:Issue 11(2017)
• Page Start: 2253
• Page End: 2266
• Publication Date: 2017-08-22
• Subjects: energy efficiency -- information processing -- metabolic energy cost -- evolution -- excitation/inhibition balance -- sparse coding; Neurobiology -- Periodicals; 612
• DOI: 10.1002/jnr.24131
• Languages: English
• ISSNs: 0360-4012

Abstract

Abstract : Brains are composed of networks of an enormous number of neurons interconnected with synapses. Neural information is carried by the electrical signals within neurons and the chemical signals among neurons. Generating these electrical and chemical signals is metabolically expensive. The fundamental issue raised here is whether brains have evolved efficient ways of developing an energy‐efficient neural code from the molecular level to the circuit level. Here, we summarize the factors and biophysical mechanisms that could contribute to the energy‐efficient neural code for processing input signals. The factors range from ion channel kinetics, body temperature, axonal propagation of action potentials, low‐probability release of synaptic neurotransmitters, optimal input and noise, the size of neurons and neuronal clusters, excitation/inhibition balance, coding strategy, cortical wiring, and the organization of functional connectivity. Both experimental and computational evidence suggests that neural systems may use these factors to maximize the efficiency of energy consumption in processing neural signals. Studies indicate that efficient energy utilization may be universal in neuronal systems as an evolutionary consequence of the pressure of limited energy. As a result, neuronal connections may be wired in a highly economical manner to lower energy costs and space. Individual neurons within a network may encode independent stimulus components to allow a minimal number of neurons to represent whole stimulus characteristics efficiently. This basic principle may fundamentally change our view of how billions of neurons organize themselves into complex circuits to operate and generate the most powerful intelligent cognition in nature. © 2017 Wiley Periodicals, Inc. Abstract : Costing the amount of energy that barely can power a light bulb, our brains could storage and process massive information, much more energy efficient than the super computers. This review covers knowledge we have learned on design principles, from the gating of ion channels to whole‐brain functional connectivity, of how an energy‐efficient, nature‐made computer was created.