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The human brain has extremely powerful thinking and computing capabilities, but it only needs about 20W of ultra-low power, and its energy consumption is much lower than that of electronic computers
.
The key to the "low energy consumption and high efficiency" of the brain network is that the neural connections are sparse in the global, but tight in the local area, forming a modular structure, which in turn changes the dynamic properties: the local modularity greatly reduces the total amount of connections used to establish connections.
Resource consumption, and the sparse and irregular firing (fire) of each neuron makes the neural network show a certain degree of synchronization in the group firing behavior, forming a neural avalanche with scale-free characteristics.
, Can respond quickly to external stimuli
.
Recently, the research team of the Department of Physics and Nonlinear Research Center of Hong Kong Baptist University [Dr.
Liang Junhao, Professor Wang Shengjun (Shaanxi Normal University), Professor Zhou Changsong] carefully studied the spatial network through large-scale numerical simulations combined with new mean field theory methods.
The excitement-inhibition balance neural circuit dynamics model reveals the key to the optimization of the brain's structure and dynamics at the same time
.
Related results were published in the "National Science Review" (National Science Review, NSR)
.
The author reconnected the globally randomly connected network (RN) to a modular network (MN) that is more in line with biological reality.
After reconnecting, the network's operating consumption (issuance rate) and connection consumption are both significantly reduced, and the dynamic mode There is a scale-free avalanche (ie, criticality), which enables the network to respond more effectively to external stimuli (see the figure below)
.
(A) Schematic diagram of the structure of RN and MN; (b) Neural firing rate and network connection consumption changes with network reconnection; (c) Neural avalanche scale distribution frequency of RN and MN; (d) RN and MN stimulated Response
.
Further analysis found that the key to the above performance changes lies in the increase in the density of the modules during the reconnection process: the increase in network topology correlation leads to the increase in dynamic correlation, making it easier for neurons to fire
.
Using the new mean field theory, the author derives the macroscopic field equation of a single module, revealing that the increase of module density causes the decrease of nerve firing rate and the characteristics of making the system close to the Hopf bifurcation
.
This explains the formation of critical avalanches and the increase in sensitivity to external stimuli at a lower delivery cost
.
The author also obtained a coupled oscillator model by coupling multiple modules, revealing the dynamics of the original network reconnection process (see the figure below)
.
(a) Examples of neuron firing patterns in modules of different densities; (b) The dynamic properties of a single module predicted by the mean field theory change with the module density; (c) The network dynamics predicted by the mean field theory follow the network reconnection process Changes
.
The study clearly gives the principles of the interaction between brain structure and dynamic properties to achieve a common efficiency optimization (rather than a trade-off between the two), in order to understand the efficient operation principle of biological brains and high-performance brain-like computing devices The design provides strong support
.
This work is supported by the National Natural Science Foundation of China, the University Education Grants Committee (Hong Kong), and the Strategic Development Fund of Hong Kong Baptist University
.
Literature information: [click the link below or read the original text] Lessis more: Wiring-economical modular networks support self-sustained firing-economical neural avalanches for efficient processing https://doi.
org/10.
1093/nsr/nwab1029 publications are located in the Q1 area, The SCI academic indicators of "Science in China" magazine continue to improve in an all-round way
.
The key to the "low energy consumption and high efficiency" of the brain network is that the neural connections are sparse in the global, but tight in the local area, forming a modular structure, which in turn changes the dynamic properties: the local modularity greatly reduces the total amount of connections used to establish connections.
Resource consumption, and the sparse and irregular firing (fire) of each neuron makes the neural network show a certain degree of synchronization in the group firing behavior, forming a neural avalanche with scale-free characteristics.
, Can respond quickly to external stimuli
.
Recently, the research team of the Department of Physics and Nonlinear Research Center of Hong Kong Baptist University [Dr.
Liang Junhao, Professor Wang Shengjun (Shaanxi Normal University), Professor Zhou Changsong] carefully studied the spatial network through large-scale numerical simulations combined with new mean field theory methods.
The excitement-inhibition balance neural circuit dynamics model reveals the key to the optimization of the brain's structure and dynamics at the same time
.
Related results were published in the "National Science Review" (National Science Review, NSR)
.
The author reconnected the globally randomly connected network (RN) to a modular network (MN) that is more in line with biological reality.
After reconnecting, the network's operating consumption (issuance rate) and connection consumption are both significantly reduced, and the dynamic mode There is a scale-free avalanche (ie, criticality), which enables the network to respond more effectively to external stimuli (see the figure below)
.
(A) Schematic diagram of the structure of RN and MN; (b) Neural firing rate and network connection consumption changes with network reconnection; (c) Neural avalanche scale distribution frequency of RN and MN; (d) RN and MN stimulated Response
.
Further analysis found that the key to the above performance changes lies in the increase in the density of the modules during the reconnection process: the increase in network topology correlation leads to the increase in dynamic correlation, making it easier for neurons to fire
.
Using the new mean field theory, the author derives the macroscopic field equation of a single module, revealing that the increase of module density causes the decrease of nerve firing rate and the characteristics of making the system close to the Hopf bifurcation
.
This explains the formation of critical avalanches and the increase in sensitivity to external stimuli at a lower delivery cost
.
The author also obtained a coupled oscillator model by coupling multiple modules, revealing the dynamics of the original network reconnection process (see the figure below)
.
(a) Examples of neuron firing patterns in modules of different densities; (b) The dynamic properties of a single module predicted by the mean field theory change with the module density; (c) The network dynamics predicted by the mean field theory follow the network reconnection process Changes
.
The study clearly gives the principles of the interaction between brain structure and dynamic properties to achieve a common efficiency optimization (rather than a trade-off between the two), in order to understand the efficient operation principle of biological brains and high-performance brain-like computing devices The design provides strong support
.
This work is supported by the National Natural Science Foundation of China, the University Education Grants Committee (Hong Kong), and the Strategic Development Fund of Hong Kong Baptist University
.
Literature information: [click the link below or read the original text] Lessis more: Wiring-economical modular networks support self-sustained firing-economical neural avalanches for efficient processing https://doi.
org/10.
1093/nsr/nwab1029 publications are located in the Q1 area, The SCI academic indicators of "Science in China" magazine continue to improve in an all-round way
