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Understanding this mechanism is important not only in neuroscience, but also in developing brain-inspired calculations
IMAGE: (A) Schematic diagram of the structure of RN and MN; (b) Neural firing rate and wiring cost change with network rewiring; (c) Neural avalanche size distribution of RN and MN; (d) Stimulus-response of RN and MN Examples of processes
Source: China Science Press
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
Recently, Dr.
Research shows that when the globally randomly connected network (RN) is reconnected to a more biologically realistic module network (MN), the network's operating consumption (distribution rate) and connection consumption are significantly reduced, and there is no standard in the dynamic mode The degree of avalanche (that is, criticality), which makes the network more effective in responding to external stimuli (see Figure 1)
Further analysis found that the increase in the density of the module during the reconnection process is the key to the overall property change: the increase in network topology correlation leads to the increase in dynamic correlation, making it easier for neurons to fire
(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 density of the module; (c) The network dynamics predicted by the mean field theory follow the network reconnection process Changes
The research clearly gives guidelines for the interaction of brain structure and dynamic properties to achieve a common efficiency optimization (rather than a trade-off between the two), and provides people with an understanding of the efficient operation principle of biological brains and high-performance brain-like computing devices.
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