Abstract
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.
Highlights
This work gives a possible mathematical formulation of intentional brain dynamics following Freeman’s half century-long dynamic systems approach [1,2,3] and the electrical behavior of the brain
In this paper we present brain as an electronic system with voltages sources as sensors, currents as internal variables in the brain and electrical power as metric in non- Euclidean space of the currents
The tensor metric in the brain is given by the impedances of the neurons
Summary
This work gives a possible mathematical formulation of intentional brain dynamics following Freeman’s half century-long dynamic systems approach [1,2,3] and the electrical behavior of the brain. The real trick is to invent a representation that takes advantage of the inherent capabilities of the medium, such as the abilities to generate exponentials, to do integration with respect to time, and to implement a zero-cost addition using Kirchhoff’s law These are powerful primitives; using the nervous system as a guide, we will attempt to find a natural way to integrate them into an overall system-design strategy. The aim of this chapter is to represent the electronic circuit as an optical mirror or an optical transmitter of energy This image will be very useful to create a bridge from electronic parameters as impedances and optical property of the brain waves as scattering (reflection) and wave transmission from one point to another. Inside the electronic system the power can be transmitted to the other ports or reflected (scattering)
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