Energy coding in neural network with inhibitory neurons

Abstract

This paper aimed at assessing and comparing the effects of the inhibitory neurons in the neural network on the neural energy distribution, and the network activities in the absence of the inhibitory neurons to understand the nature of neural energy distribution and neural energy coding. Stimulus, synchronous oscillation has significant difference between neural networks with and without inhibitory neurons, and this difference can be quantitatively evaluated by the characteristic energy distribution. In addition, the synchronous oscillation difference of the neural activity can be quantitatively described by change of the energy distribution if the network parameters are gradually adjusted. Compared with traditional method of correlation coefficient analysis, the quantitative indicators based on nervous energy distribution characteristics are more effective in reflecting the dynamic features of the neural network activities. Meanwhile, this neural coding method from a global perspective of neural activity effectively avoids the current defects of neural encoding and decoding theory and enormous difficulties encountered. Our studies have shown that neural energy coding is a new coding theory with high efficiency and great potential.

The essence of neuronal activity from the consistency of two different neuron models

“…As the membrane potential and the corresponding energy expenditure have a unique correspondence, the traditional membrane potential encoding can also be expressed by the neural energy coding [9][10][11][12][13][14]. This will provide a sufficient theoretical basis for future studies on perceptual cognition and the construction of global neural models for brain function, which is a new brain theory of neuro combined with holism and reductionism.…”

“…Since this new finding allows us to explain the many neurophysiological phenomena that have so far failed to give a reasonable explanation, it is of important scientific significance [11]. After that, we proposed the coding of neural energy in structural network as well as energy distribution under different parameters [9,10]. Recently, we applied this novel neural coding theory to the mental exploration, which took the spatial distribution of the power of the positional cells in hippocampus at the same time as a neural energy field to perform investigation.…”

“…For this purpose, a novel theory of neural coding has been proposed in neuroscience researches [4][5][6][7][8][9][10][11][12][13], which provides a new perspective on how to understand the nature of neuronal activity. The core of this coding theory is that there is a unique correspondence between various neuronal membrane potentials and their neural energy expenditure, which makes it possible to convert the firing patterns of a variety of complex, highly nonlinear membrane potentials into new neural information processing methods that are processed in an energy's firing pattern and have been named as neural energy coding [7][8][9][10][11][12][13].…”

“…The core of this coding theory is that there is a unique correspondence between various neuronal membrane potentials and their neural energy expenditure, which makes it possible to convert the firing patterns of a variety of complex, highly nonlinear membrane potentials into new neural information processing methods that are processed in an energy's firing pattern and have been named as neural energy coding [7][8][9][10][11][12][13]. According to such a coding theory, we found that neural energy coding can well integrate the reductionism and holism in the field of cognitive neuroscience and be used as the main basis for the study of global neural activity of brain functionality [11].…”

“…However, neural energy provides an effective scheme for solving the above problems. (4) Since the energy is a scalar, whether it is a single or neuron population, or whether it is a network or a behavioral, as well as a linear or a nonlinear neural model, their dynamic responses can be used to describe patterns of energy coding by superposition of neural energy [4,[6][7][8][9][10][11][12]. Thus, global information about the inherent, intrinsic, and functional neural activities can be obtained, while it cannot be achieved by other traditional coding theories [15,[17][18][19][20][21].…”