Journal of Applied & Computational Mathematics

ISSN: 2168-9679

Open Access

Computation by Intention and Electronic Image of the Brain


Resconi G and Licata I

Neurons as active unities are connected one with the others by synapses in an electronic way. We argue
that brain is not comparable with digital computer with algorithms because intention as software is introduced as transformation in the neural states without any digital reduction. Any electronic system has voltages and currents sources and complex interconnected impedances. By electronic system and neural network we have different possibilities to introduce Freeman intentional transformation in the brain. One is to use source voltages (sensor) to generate wanted behavior of currents (internal flows of the signals) with the same impedance network. We can also reverse the process: given the behavior of the currents we generate wanted voltages transformation (effectors as muscles) with the same impedance. Another possibility is to change the impedance network (memory) to generate wanted internal current. When intention is transformation of references, geometry changes and also the form of
straight line (geodesic). Special reference and geometry can be modeled by the electrical power as metric. Different types of brain geometries as hyperbolic geometry of waves and elliptic geometry of stable states are discussed with examples. Because we have waves in brain, Karl Pribram created holographic model of brain that by scattering and transmitted matrix can be joined to electronic model. Mechanical system metrics are implemented in the neural network as electronic network.


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