Neuromolecularware and its application to pattern recognition

Unlike computer systems, organisms have high adaptability in dealing with environmental changes or noise. The ability to evolve, self-organizing dynamics, and a closed structure–function relationship are the three principle features embedded in biological structures that provide great malleability to environmental change. Computer systems have fast processing speed for performing heavy computational tasks. One of the objectives in this research is to capture these three biological features and implement them onto a digital circuit. The proposed hardware (called neuromolecular hardware), is the integration of inter- and intraneuronal information processing applied to the pattern recognition problem domain. This approach was tested on the Quartus II system, a simulation tool for digital circuits. The experimental result showed good self-organizing capability in selecting significant bits for differentiating patterns and insignificant bits for tolerating noise. The proposed digital circuit also exhibited a closed structure–function relationship. This implied that this hardware embraced an adaptive fitness landscape that facilitated processing spatiotemporal information.