Molecular Cognitive Information Processing and Computing Platforms

Molecular cognitive systems are envisioned to be designed to accomplishing information processing integrating knowledge generation, perception, learning, etc. By introducing interactive cognition tasks, one attempts to expand signal processing which includes data coding, manipulation, mining, storing and computing. The information theory has been utilized to evaluate communication and coding, and there is a need to develop methods to measure and estimate knowledge generation, perception and learning. We initiate the developments of information-theoretic model of information representation and processing. Approaches in synthesis of molecular cognitive information processing platforms (^MCIPP) are reported. For^MCIPP, synthesis of novel architectures and organizations can be performed utilizing aggregated neuronal hypercells. It is documented that the signal processing at the system and device levels can be evaluated and optimized using the performance measures. The objective is to examine how systems represent and process the information utilizing information-theoretic estimates. Signal and information processing depends on the statistical structure of data. We examine these statistics to attain statistical knowledge generation, learning, reconfiguration, adaptation, robustness and self-awareness. The information-theoretic limits of cognition (knowledge generation, perception and learning) should be examined using the information-centered estimates. Baseline cognition characteristics should be analyzed in order to approach fundamental limits and benchmarks of cognitive information processing. One faces with a significant mathematical complexity and technology dependence. Though fundamentals results may be technology-independent, the implementation leads to specific technologies. The proposed results uniquely suit emerging and gradually maturing molecular technologies.