Harmonic fractal transformation, 4R-regeneration and noise shaping for ultra wide-band reception in FitzHugh-Nagumo neuronal model

TL;DR

This paper introduces a novel harmonic fractal transformation and noise shaping method based on the FitzHugh-Nagumo model, enabling ultra-wideband signal processing and regeneration in neuronal systems without complex architectures.

Contribution

It reveals a new harmonic fractal transformation for frequency and bandwidth scaling and demonstrates noise shaping in a simple RLC circuit, advancing neuronal signal processing models.

Findings

Harmonic fractal transformation enables frequency multiplication beyond neuronal spiking range.

Noise shaping achieved in simple RLC circuit without delay lines.

The 4R-regeneration process enhances signal re-amplification, re-shaping, re-timing, and re-modulating.

Abstract

Human hearing range significantly surpasses the typical neuronal spiking frequency. Yet, neurons with their modest frequency range not only efficiently receive and process multiple orders higher frequency signals, but also demonstrate remarkable stability and adaptability to frequency variations in brain functional connectivity. Ability to process signals beyond the limitations of the receiver temporal or frequency (bandwidth) resolution is highly desirable yet requires complex design architectures. Using the FitzHugh-Nagumo model we reveal the harmonic fractal transformation of frequency and bandwidth, which enables the Nyquist rate integer (for low frequencies) and sub-integer (for high frequencies) multiplication. We also demonstrate for the first time that noise shaping can be achieved in a simple RLC-circuit without a requirement of a delay line. The discovered effect presents a novel regeneration type - 4R: re-amplifying, re-shaping, re-timing, and re-modulating and due to the fractal nature of transformation offers a remarkable regenerative efficiency. The effect is a generalization of phase locking to non-periodic encoded signals. The discovered physical mechanism explains how using neuronal functionality one can receive and process signals over an ultra-wide band (below or higher the spiking neuronal range by multiple orders) and below the noise floor.