Spike and Tyke, the Quantized Neuron Model

TL;DR

This paper introduces a novel quantized neuron model called Spike and Tyke, which accurately simulates neuron spiking behavior with over 97% accuracy by quantizing resistance and energy, offering a new perspective on neural modeling.

Contribution

It proposes a new quantized neuron model that links quantum mechanics principles to neural spiking, providing a more accurate and theoretically grounded approach.

Findings

Achieves over 97% match with real neuron spiking data.

Introduces resistance and energy quantization in neuron modeling.

Provides a theoretical framework connecting quantum mechanics and neural activity.

Abstract

Modeling spike firing assumes that spiking statistics are Poisson, but real data violates this assumption. To capture non-Poissonian features, in order to fix the inevitable inherent irregularity, researchers rescale the time axis with tedious computational overhead instead of searching for another distribution. Spikes or action potentials are precisely-timed changes in the ionic transport through synapses adjusting the synaptic weight, successfully modeled and developed as a memristor. Memristance value is multiples of initial resistance. This reminds us with the foundations of quantum mechanics. We try to quantize potential and resistance, as done with energy. After reviewing Planck curve for blackbody radiation, we propose the quantization equations. We introduce and prove a theorem that quantizes the resistance. Then we define the tyke showing its basic characteristics. Finally we give the basic transformations to model spiking and link an energy quantum to a tyke. Investigation shows how this perfectly models the neuron spiking, with over 97% match.