Natural, Classical, and Quantum Brain for Artificial Computation

The purpose of the research reported in this article is to establish a structural explanation of physical phenomena observed or experimented on natural neural networks and especially on the brain. A simplified model based on double stochasticity properties, concentrates on the synaptic structure of any neuron. The higher the energy spent for the neural network functioning, the greater the number of used synapses. The notion of consciousness field intensity, by an analogy with electrical intensity, leads to considering mental mechanisms associated with the classical model behavior. The Gaussian formalism appears naturally that shows some conditions for an activated neuron to produce a spike signal while a principle of saturation gives an argument for the optimization of the number of synapses involved in this production. As frequently, the optimum identified previously is the place of oscillations and an analytical approach builds a Hamiltonian, the quantization of which results in the quantum harmonic oscillator of a neuron. This quantized neural model resumes all the features and results associated with this oscillator and shows the high relevance of the term “oscillation” to describe the neuron behavior in spite of the counter-intuitive nature of the model. The explained phenomena span from the strong entanglement of information with the brain physical medium, more precisely with the neuron synaptic structure, to the brain waves observed on any electroencephalogram of any living [human] being. The strangeness of this research is that its initial foundation, the electrical analogy, is coherent from the classical to the quantum brain models and probabilities or wave functions bear interpretations in terms of action potential or electrical oscillations. Several scales of synchronization and space coherence are studied and integrate various levels of complexity: in microtubules that may impose the nature of the information storage and transmission and leading to the synaptic combinatorial complexity for spike generation. The neural network or the brain energy consumption is also calculated at those levels