The neural path probabilistic delay model

Abstract

The neural path probabilistic delay (NPPD) algorithm models the neural network in a discrete time manner analogous to the computer network. The four stages of the basic neuron cell component, i.e., neuron ACKNOWLEDGMENT The author gratefully acknowledges the hard work of McGough in helping with the development of this device. Jeff

Computer networking topological and parametric representations for functional neural networks

“…Therefore, a prosthetic clock and phase tracking as well as individual subsystem clocking and phase tracking circuitry, will be required in the implementation for the software and hardware construction of the system in a VLSI circuitry timing procedure. The interaction between the Niznik Quantization Bound Theory determination [14] of neural path delays, and their related upper and lower bounds with the number of functional paths was illustrated previously for the Robotic Quantization Controlled Sensory[RQCS] System in references 15 and 18.…”

“…The interaction between disciplines of electrical engineering, physics and biochemistry with the interdisciplinary idea of neural prosthetic hardware and software replacement components, and its connection to computer networking, was suggested by Niznik [14], Niznik and Newcomb [16,17] and Niznik, Hoss, and Watts [15,18]. The distinct interaction theory concept represents the transfer of theoretical and heuristic concepts between the disciplines of electrical engineering, computer networking and neural networking to extend their definition of network operation and control.…”

“…neural level structures, where learning and memory have occurred. The biochemical and mathematical formalism, which is proposed and initially developed here, will realize statistical and classical entropy theory to provide a parameter which determines the following adaptive network architecture parameters: (1) graded potential and action potential intervals and the correlated informational state [15,18]; (2) neuron (computer node) input (dendrite) and output (axon) [11,12,15,18] channel probability transition matrix entries [12,14]; (3) accuracy of Probabilistic Automata representation of neurons and channels [10,14,23]; (4) number of possible routing paths for neural information [10,13,14].…”

“…The theoretical prediction of the optimal quantization interval [11,14] required between samples of the service rate minus interarrival rate probability density function for finite buffer queueing systems must be derived to obtain accurate mean waiting time solutions with minimal data input. The range of the Optimal Measurement Complexity (number of samples, quantiles, of action potential service rate minus graded potential interarrival rate) required to attain the Peak Measurement Accuracy of the optimal quantization interval for various widths (pdf areas normalized to one) of the service minus interarrival pdf, will be derived to obtain this optimal sampling interval.…”