Mechanism on brain information processing: Energy coding

According to the experimental result of signal transmission and neuronal energetic demands being tightly coupled to information coding in the cerebral cortex, the authors present a brand new scientific theory that offers a unique mechanism for brain information processing. They demonstrate that the neural coding produced by the activity of the brain is well described by the theory of energy coding. Due to the energy coding model’s ability to reveal mechanisms of brain information processing based upon known biophysical properties, they cannot only reproduce various experimental results of neuroelectrophysiology but also quantitatively explain the recent experimental results from neuroscientists at Yale University by means of the principle of energy coding. Due to the theory of energy coding to bridge the gap between functional connections within a biological neural network and energetic consumption, they estimate that the theory has very important consequences for quantitative research of cognitive function.

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

[2]  Idan Segev,et al.  Methods in Neuronal Modeling , 1988 .

[3]  Xianfa Jiao,et al.  Synchronization in neuronal population with the variable coupling strength in the presence of external stimulus , 2006 .

[4]  Rubin Wang,et al.  Nonlinear stochastic models of neurons activities , 2001, Neurocomputing.

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

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

[7]  William B. Levy,et al.  Energy-efficient interspike interval codes , 2005, Neurocomputing.

[8]  W J Schwartz,et al.  Metabolic mapping of functional activity in the hypothalamo-neurohypophysial system of the rat. , 1979, Science.

[9]  Xianfa Jiao,et al.  Nonlinear dynamic model and neural coding of neuronal network with the variable coupling strength in the presence of external stimuli , 2005 .

[10]  Louis Sokoloff,et al.  Activity‐dependent Energy Metabolism in Rat Posterior Pituitary Primarily Reflects Sodium Pump Activity , 1980, Journal of neurochemistry.

[11]  C. Koch,et al.  Invariant visual representation by single neurons in the human brain , 2005, Nature.

[12]  Rubin Wang,et al.  Stochastic model and neural coding of large-scale neuronal population with variable coupling strength , 2006, Neurocomputing.

[13]  J. Nicholls From neuron to brain , 1976 .

[14]  F. Hyder,et al.  Cerebral energetics and spiking frequency: The neurophysiological basis of fMRI , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[16]  Hatsuo Hayashi,et al.  An Exploration of Dynamics of the Moving Mechanism of the Growth Cone , 2003, Molecules : A Journal of Synthetic Chemistry and Natural Product Chemistry.

[17]  H. Haken Principles of brain functioning , 1995 .

[18]  Robert A. Wilson,et al.  Book Reviews: The MIT Encyclopedia of the Cognitive Sciences , 2000, CL.

[19]  William B Levy,et al.  Energy-Efficient Neuronal Computation via Quantal Synaptic Failures , 2002, The Journal of Neuroscience.

[20]  Kelvin E. Jones,et al.  Neuronal variability: noise or part of the signal? , 2005, Nature Reviews Neuroscience.

[21]  F. Hyder,et al.  Total neuroenergetics support localized brain activity: Implications for the interpretation of fMRI , 2002, Proceedings of the National Academy of Sciences of the United States of America.