Energy distribution property and energy coding of a structural neural network

Studying neural coding through neural energy is a novel view. In this paper, based on previously proposed single neuron model, the correlation between the energy consumption and the parameters of the cortex networks (amount of neurons, coupling strength, and transform delay) under an oscillational condition were researched. We found that energy distribution varies orderly as these parameters change, and it is closely related to the synchronous oscillation of the neural network. Besides, we compared this method with traditional method of relative coefficient, which shows energy method works equal to or better than the traditional one. It is novel that the synchronous activity and neural network parameters could be researched by assessing energy distribution and consumption. Therefore, the conclusion of this paper will refine the framework of neural coding theory and contribute to our understanding of the coding mechanism of the cerebral cortex. It provides a strong theoretical foundation of a novel neural coding theory—energy coding.

[1]  R. Kötter,et al.  Cortical network dynamics with time delays reveals functional connectivity in the resting brain , 2008, Cognitive Neurodynamics.

[2]  D. Bradley,et al.  Neural population code for fine perceptual decisions in area MT , 2005, Nature Neuroscience.

[3]  Guanrong Chen,et al.  Energy Function and Energy Evolution on Neuronal Populations , 2008, IEEE Transactions on Neural Networks.

[4]  M. Raichle,et al.  Appraising the brain's energy budget , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Peter Dayan,et al.  Encoding and Decoding Spikes for Dynamic Stimuli , 2008, Neural Computation.

[6]  Rubin Wang,et al.  Energy coding in biological neural networks , 2007, Cognitive Neurodynamics.

[7]  William B. Levy,et al.  Energy Efficient Neural Codes , 1996, Neural Computation.

[8]  Hermann Haken,et al.  Towards a unifying model of neural net activity in the visual cortex , 2007, Cognitive Neurodynamics.

[9]  Wolf Singer,et al.  Distributed processing and temporal codes in neuronal networks , 2009, Cognitive Neurodynamics.

[10]  Peter T Fox,et al.  Nonlinear coupling between cerebral blood flow, oxygen consumption, and ATP production in human visual cortex , 2010, Proceedings of the National Academy of Sciences.

[11]  Olaf Sporns,et al.  Neurobiologically Realistic Determinants of Self-Organized Criticality in Networks of Spiking Neurons , 2011, PLoS Comput. Biol..

[12]  Jun Igarashi,et al.  Theta phase coding in a network model of the entorhinal cortex layer II with entorhinal-hippocampal loop connections , 2007, Cognitive Neurodynamics.

[13]  Shun-ichi Amari,et al.  Difficulty of Singularity in Population Coding , 2005, Neural Computation.

[14]  P. Latham,et al.  Ruling out and ruling in neural codes , 2009, Proceedings of the National Academy of Sciences.

[15]  Z. Zhang,et al.  Phase Synchronization Motion and Neural Coding in Dynamic Transmission of Neural Information , 2011, IEEE Transactions on Neural Networks.

[16]  Mu-ming Poo,et al.  Self-Control in Decision-Making Involves Modulation of the vmPFC Valuation System , 2012 .

[17]  Guanrong Chen,et al.  Energy coding and energy functions for local activities of the brain , 2009, Neurocomputing.

[18]  L. Abbott,et al.  Theoretical Neuroscience Rising , 2008, Neuron.

[19]  P. Tibbetts.  :Cognitive Neuroscience: The Biology of the Mind , 2009 .

[20]  Rosa Cao,et al.  The hemo-neural hypothesis: on the role of blood flow in information processing. , 2008, Journal of neurophysiology.

[21]  Mingzhou Ding,et al.  Enhancement of neural synchrony by time delay. , 2004, Physical review letters.

[22]  Terrence J Sejnowski,et al.  Communication in Neuronal Networks , 2003, Science.

[23]  Rubin Wang,et al.  Mechanism on brain information processing: Energy coding , 2006 .