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In the past few decades, there have been intense debates whether the brainoperates at a critical state. To verify the criticality hypothesis in theneuronal networks is challenging and the accumulating experimental andtheoretical results remain controversial at this point. Here we simulate howvisual information of a nature image is processed by the finiteKinouchi-Copelli neuronal network, extracting the trends of the mutualinformation (how sensible the neuronal network is), the dynamical range (howsensitive the network responds to external stimuli) and the statisticalfluctuations (how criticality is defined in conventional statistical physics).It is rather remarkable that the optimized state for visual recognition,although close to, does not coincide with the critical state where thestatistical fluctuations reach the maximum. Different images and/or networksizes of course lead to differences in details but the trend of the informationoptimization remains the same. Our findings pave the first step to investigatehow the information processing is optimized in different neuronal networks andsuggest that the criticality hypothesis may not be necessary to explain why aneuronal network can process information smartly.

Critical Neurons: optimized visual recognition in a neuronal network