NEURAL NETWORKS FOR CONSCIOUSNESS - PROVIDING ANOTHER DIMENSION TO COGNITIVE NEUROSCIENCES

摘要

Inspite of the fact that the Artificial Neural Networksrn(ANNs) are yet to receive a universal definition, theyrnhave received recognition and application prospects in arnwide variety of subjects including diverse fields likernradar, pattern recognition and trading strategies. Inrnparallel to these non-neurological applications (or for thatrnmatter non-biological applications), there have beenrnefforts of implying ANNs in cognitive neurosciences also.rnAt the cognitive-function level, ANNs are defined asrnconnectionist models for cognitive processing. However,rnanother important way of modelling ANN is in itsrnbiological form, using all the biological constraints. Thisrnbiological model of ANN, first conceptualised byrnMcCollough (4) has seldom been used for exploringrncognitive functions because of its high complexity (5). Inrnthis article, we highlight an important application of thisrnbiological model of ANN in cognitive neurosciencesrnfield. We explore how such models can be used inrnenhancing our abilities of perceiving neural behaviours inrndifferent states of consciousness. In this present article,rnwe focus on three different states of consciousness whichrncan be modelled. These are the death, sleep and the staternof meditation, like the much studied inner-light perceptionrnstate of Vihangam Yoga. For this purpose, we present arnstep-wise approach, consisting of five steps needed forrnthis modeling.