In experimental neuroscience, the coding of either a single neuron or population has its limitation that demands effective coding theory to describe perceptual coding, cognitive coding and behavioral coding in each scale. Although a series of study has so far achieved to describe the activity in a single cortex area, the global description of interaction among large scale neurons remains to be solved. The neural activity coded in different patterns has its certain scope for application, so it is necessary to explore the features and correlations within each scale by developing effective coding methods.
The spatial and temporal distribution of neural energy is vital for explanation of the mechanism of human brain, however, relative study, e.g. BOLD signal analysis of fMRI paradigms, is restricted to low temporal resolution and high experimental expenses, etc. Besides, the importance of default network that consumes the majority of the total energy has been paid less attention than task-related paradigms that only increases little energy. Progress of computational neuroscience should be made to describe neural energy and to quantify the spatial and temporal features precisely with mathematical methods, so as to overcome the limitations brought from experimental measures and conditions. It can serve as combination and compensation for recent experimental theory.
A more interesting question is whether neural information can be coded and decoded by neural energy? A series of study aimed to answer this question has been published, including an electric physical model, computational energy results of a single neuron and neural networks, etc., but the number of achievements in this field is still far from it should be while the question itself is so important. If the hypothesis is true, then we could look into our whole brain in a more simple and feasible way, since the computational complexity caused by structure of topology is significantly reduced when considering the linear superposition of neural energy.
In this research topic, we wish to review and summarize what neural coding and neural energy have accomplished so far to solve important computational and experimental neural issues. We also wish to develop a whole set of sophisticated theory to describe the relation between neural coding and neural energy. Finally, presentation of ongoing original works is greatly encouraged.
In experimental neuroscience, the coding of either a single neuron or population has its limitation that demands effective coding theory to describe perceptual coding, cognitive coding and behavioral coding in each scale. Although a series of study has so far achieved to describe the activity in a single cortex area, the global description of interaction among large scale neurons remains to be solved. The neural activity coded in different patterns has its certain scope for application, so it is necessary to explore the features and correlations within each scale by developing effective coding methods.
The spatial and temporal distribution of neural energy is vital for explanation of the mechanism of human brain, however, relative study, e.g. BOLD signal analysis of fMRI paradigms, is restricted to low temporal resolution and high experimental expenses, etc. Besides, the importance of default network that consumes the majority of the total energy has been paid less attention than task-related paradigms that only increases little energy. Progress of computational neuroscience should be made to describe neural energy and to quantify the spatial and temporal features precisely with mathematical methods, so as to overcome the limitations brought from experimental measures and conditions. It can serve as combination and compensation for recent experimental theory.
A more interesting question is whether neural information can be coded and decoded by neural energy? A series of study aimed to answer this question has been published, including an electric physical model, computational energy results of a single neuron and neural networks, etc., but the number of achievements in this field is still far from it should be while the question itself is so important. If the hypothesis is true, then we could look into our whole brain in a more simple and feasible way, since the computational complexity caused by structure of topology is significantly reduced when considering the linear superposition of neural energy.
In this research topic, we wish to review and summarize what neural coding and neural energy have accomplished so far to solve important computational and experimental neural issues. We also wish to develop a whole set of sophisticated theory to describe the relation between neural coding and neural energy. Finally, presentation of ongoing original works is greatly encouraged.
