| Summary: | This thesis formulates and evaluates a mathematical model from an engineer's point of view based on the currently-known information-processing processes and structures of biological neurons. The specification and evaluation of the RealNeuron model form a baseline for current use in engineering solutions and future developments.
The RealNeuron is a carefully-reduced model that retains the essential features of more complex models. A systems engineering approach is used to formulate it, i.e. the model is described as using multiple resolution levels with configurable modular elements at each resolution level and is then implemented, verified and validated in a bottom-up method. It is computationally efficient and only adds or subtracts ion concentrations based on the states at the membrane structure's level. The results are integrated at the lower levels of resolution. The RealNeuron's simple calculations make simulations on personal computers possible by using standard spreadsheet software for a seven-neuron classical-conditioning neural circuit. All the simulated states at the highest level of resolution (i.e. pumps, channels, etc.), the intermediate levels of resolution (i.e. membrane potentials, neurotransmitters in the synapse, etc.) and the lowest level of resolution (i.e. conditioning signal, conditioned signal, conditioned reaction, etc.) are available on a spreadsheet.
The RealNeuron is verified in a bottom-up manner. The pumps, channels and receptors are verified first. These components are then integrated into the different membrane types (post-synaptic membrane, main membrane, axonal membrane) and verified while the membrane components are validated simultaneously. This process is repeated until individual neurons have been built up and RealNeuron networks have finally been constructed. The RealNeuron is verified and validated in configurations for AND, NAND, OR, NOR, NOT and XOR logic functions. It is also verified and validated by the implementation of classical conditioning.
In a noisy environment, the RealNeuron's performance is dependent on the pump's parameters in the main membrane of the sensor neurons.
This thesis proposes that a grade of machine intelligence is used to distinguish between the different synthesis requirements for intelligent machines.
An engineering synthesis of a RealNeuron network, based on classical conditioning, demonstrates how to implement a RealNeuron network that can be used in machines built to the grade of machine intelligence requirement which is classical-conditioning learning implemented with neural networks that can change learned associations in a dynamic environment. === This thesis formulates and evaluates a mathematical model from an engineer's point of view based on the currently-known information-processing processes and structures of biological neurons. The specification and evaluation of the RealNeuron model form a baseline for current use in engineering solutions and future developments.
The RealNeuron is a carefully-reduced model that retains the essential features of more complex models. A systems engineering approach is used to formulate it, i.e. the model is described as using multiple resolution levels with configurable modular elements at each resolution level and is then implemented, verified and validated in a bottom-up method. It is computationally efficient and only adds or subtracts ion concentrations based on the states at the membrane structure's level. The results are integrated at the lower levels of resolution. The RealNeuron's simple calculations make simulations on personal computers possible by using standard spreadsheet software for a seven-neuron classical-conditioning neural circuit. All the simulated states at the highest level of resolution (i.e. pumps, channels, etc.), the intermediate levels of resolution (i.e. membrane potentials, neurotransmitters in the synapse, etc.) and the lowest level of resolution (i.e. conditioning signal, conditioned signal, conditioned reaction, etc.) are available on a spreadsheet.
The RealNeuron is verified in a bottom-up manner. The pumps, channels and receptors are verified first. These components are then integrated into the different membrane types (post-synaptic membrane, main membrane, axonal membrane) and verified while the membrane components are validated simultaneously. This process is repeated until individual neurons have been built up and RealNeuron networks have finally been constructed. The RealNeuron is verified and validated in configurations for AND, NAND, OR, NOR, NOT and XOR logic functions. It is also verified and validated by the implementation of classical conditioning.
In a noisy environment, the RealNeuron's performance is dependent on the pump's parameters in the main membrane of the sensor neurons.
This thesis proposes that a grade of machine intelligence is used to distinguish between the different synthesis requirements for intelligent machines.
An engineering synthesis of a RealNeuron network, based on classical conditioning, demonstrates how to implement a RealNeuron network that can be used in machines built to the grade of machine intelligence requirement which is classical-conditioning learning implemented with neural networks that can change learned associations in a dynamic environment. === Thesis (Ph.D. (Electrical and Electronic Engineering))--North-West University, Potchefstroom Campus, 2008.
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