Neural Networks for Consciousness - Providing Another Dimension to Cognitive Neurosciences

R. Prakash, O. Prakash (India), N. Shevchenko (Russia), and G.J. Sahay (India)



Inspite of the fact that the Artificial Neural Networks (ANNs) are yet to receive a universal definition, they have received recognition and application prospects in a wide variety of subjects including diverse fields like radar, pattern recognition and trading strategies. In parallel to these non-neurological applications (or for that matter non-biological applications), there have been efforts of implying ANNs in cognitive neurosciences also. At the cognitive-function level, ANNs are defined as connectionist models for cognitive processing. However, another important way of modelling ANN is in its biological form, using all the biological constraints. This biological model of ANN, first conceptualised by McCollough (4) has seldom been used for exploring cognitive functions because of its high complexity (5). In this article, we highlight an important application of this biological model of ANN in cognitive neurosciences field. We explore how such models can be used in enhancing our abilities of perceiving neural behaviours in different states of consciousness. In this present article, we focus on three different states of consciousness which can be modelled. These are the death, sleep and the state of meditation, like the much studied inner-light perception state of Vihangam Yoga. For this purpose, we present a step-wise approach, consisting of five steps needed for this modeling.

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