The general objective of the proposed research is to provide the essential mathematical and computational framework which will help to unravel some of the design principles underlying cannabinoid signalling, at both the cellular and the network level, and shed light on the neurobiological processes by which cannabinoids mediate memory and learning in the human brain. The proposed methodology combines two actions of fundamental importance to tackle the different aspects of the role of cannabinoids in memory and learning, aiming to provide testable predictions in a variety of disciplines.
The first action incorporates the modelling of the underlying cannabinoid biological mechanisms directly and the combination of cellular and network levels to treat both the local (due to endogenous) and the global (due to exogenous) cannabinoid-related phenomena with biological realism. This novel approach includes the development of a biophysically realistic as well as a mathematically reduced model of cannabinoid retrograde signalling at the synaptic level and the numerical implementation of these models as building blocks for the exploration of the effects of cannabinoid signalling on brain rhythms in spiking neural networks.
The second action investigates the contribution of cannabinoids in the embodiment of knowledge in networks, manifested by the modulation of synaptic weights, in the framework of brain learning paradigms.This activity includes the implementation and development of learning methods, such as supervised, unsupervised and reinforcement learning, for spike-time coding that can make use of retrograde cannabinoid signalling to enhance existing neural learning strategies as well as to derive new ones. The project DIDAKTOR/0609/12 is co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research Promotion Foundation.
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