In recent years, interdisciplinary backgrounds, in particular Computer science, Statistical mathematics, Philosophy and Neuroscience, have started to merge in the hope of discovering novel algorithms inspired by biological processes in the human mind. This has led to the computational theory of mind, first introduced by Hilary Putnam in the 1960s and today conspicuously conversed by cognitive scientists such as Steven Pinker. This is but one of the many theories debated by scholars interested in Chinese rooms… However, our understanding of the brain and neural-synaptic networks in particular, seems to be steadily shifting this innate mystery to scientific perception and beneficial constructs.
Pioneers in the field of Computational Neuroscience such as Geoffrey Hinton recently demonstrated the influence of stochastic processes using Deep learning connectionist models. These methods are remarkable in both an evolutionary biological and mathematical sense. Presently, it seems to be universally typical that the performance of the practical constructions of these learning algorithms far exceeds what can rigorously be proven.
What interests me are these stochastic “neural mechanics” able to perceive structure in information and auto-associate functions with some degree of meaning, and the energy coupled to these processes. My question is: When will we back-propagate information which contains metacognition
or even cognitive dissonance, and to what degree is our own perception influenced by the process of generalisation?
Somewhere on the quest of neurological learning I hope to marry Information theory fundamentals, centred on telecommunication applications, with this art of natural information processing.
We are the embodiment of a synchronise mystery. – Gerald SchroederMy contact details: firstname.lastname@example.org