Consciousness Studies

The concepts of consciousness and self-reflection in the context of related epistemological and ontological structures that imply insights for understanding causal mechanism in Kashmir Saivism are studied. The cognitive and the aesthetic characteristics that underlie the philosophical discussions on emotions have been brought out in the light of understanding perceptual and experiential reality vis- a- vis the metaphysical structures of the concept of pratyabhijna.

The major intent is to focus on the fundamental discussions pertaining to philosophical and psychological questions raised by the thinkers of Kashmir Saivism and attempt the contextualising of Information theory, consciousness studies and artificial intelligence, from the view point of analysing causal structures of thought, experiences and entities. The study is the first comparative attempt to juxtapose emotions, aesthetic process, memory, cognitive structures and the self in the context of understanding consciousness in Indian philosophy.

The indigenous healing and wellness traditions of Kerala are being documented to understand the psychological and psychiatric implications with the help of examining antiquity, tribal medicine and native landscapes, ritual healing, and indigenous living. The association of language with consciousness, specifically how ambiguity as a function of language might be influential in psychological healing through Mantravada, is being addressed.

The depth and quality of yoga and meditation on well-being has been addressed. A host of complexity measures of consciousness have been evaluated and have converged on a novel measure that we have proposed – Network Causal Activity.

A new algorithm – Compression-Complexity-Causality (CCC) to identify causal relationships between time series was invented. CCC performs reliably on short, noisy, sampled and filtered time series that occur in practical scenarios can be applied to wide range of applications in econometrics, cognitive science, climatology, measuring consciousness and sustainability research.  

Network Causal Activity (NCA) was tested on ECoG datasets from monkeys to characterise the level of consciousness in awake and anesthesia states based on causal activity in brain networks. NCA could be applied to help neurosurgeons determine the level of consciousness in patients who are unable to report their experience. A causal stability synchronisation theorem which is a necessary and sufficient condition for coupled dynamical systems to achieve complete synchronization has been proved. Its potential applications include determining when a group of coupled neurons will synchronise indicating the onset of a seizure.

A compression-complexity distance measure was proposed to identify the novel SARS-CoV-2 virus that caused global pandemic of virus disease (COVID-19) and distinguishing it from SARS-CoV-1 virus using only short contiguous fragments of nucleotide sequences. The proposed algorithm could potentially be used in vaccine research by enabling rapid matching of genomic sequences and could avoid the need for sequence assembly.  

Neuro-chaos Inspired AI: ChaosNet, a chaos based artificial neural network architecture for classification has been proposed mimicking the chaotic firing of neurons in the brain (which conventional algorithms do not). ChaosNet produces state-of-the art performance on classification tasks on publicly available datasets. With as low as 0.05% of the total data used for training, the proposed method reports accuracies ranging 73% to 98% while also claiming to be robust to external noise.