  In the past few decades, there have been intense debates whether the brain
operates at a critical state. To verify the criticality hypothesis in the
neuronal networks is challenging and the accumulating experimental and
theoretical results remain controversial at this point. Here we simulate how
visual information of a nature image is processed by the finite
Kinouchi-Copelli neuronal network, extracting the trends of the mutual
information (how sensible the neuronal network is), the dynamical range (how
sensitive the network responds to external stimuli) and the statistical
fluctuations (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 the
statistical fluctuations reach the maximum. Different images and/or network
sizes of course lead to differences in details but the trend of the information
optimization remains the same. Our findings pave the first step to investigate
how the information processing is optimized in different neuronal networks and
suggest that the criticality hypothesis may not be necessary to explain why a
neuronal network can process information smartly.
