July 15, 2025

Neural Circuits: Σ(35-59) Artificial Sound Environments Interconnected to an Evolving Autocentric Rotating Cellular Cube

Key Points

Artificial sound environments evolved from initial random setups into structured circuits, enhancing audio processing capabilities.
Machine learning enables circuits to optimise connection and functionality, leveraging 24 rotating cube partitions.
The system integrates 68 modular circuits with electronic grids, facilitating dynamic audio effects processing.
Transition adapted principles apply across physical and biochemical sciences, highlighting broad implications for circuit development.

Abstract

Constructed by using multiple computer-fragmented WAV files retrieved from disposed digital audio data. 68 modular circuits and three ports each equipped with electronic grids, were developed. The action outcomes of each circuit are controlled by command processing, 24 partitions of an evolving rotating cube with 576 cellular blocks. Attached to each cell are wavelets, one to four plugins and a similar number of effects or a combination of both. The automaton tends to evolve from an initial random distribution of a selection of circuits, WAV files, effects and plugins to a deterministic selection of numbers used in further fragmentation processes of circuit-processed WAV files allowing accessibility in the distribution state so that wavelets can reach their destination. Circuits are part of the neural network of commands, offering machine learning capabilities. The development draws on transition adapted for modelling purposes in areas from physical and biochemical sciences.

Mark Helpful

Bookmark

Relay