|Title:||Study and reflections on the functional and organisational role of neuromessenger nitric oxide in learning: an artificial and biological approach||Authors:||Suárez-Araujo, Carmen Paz||UNESCO Clasification:||120304 Inteligencia artificial
Depression, et al
|Issue Date:||2000||Journal:||AIP Conference Proceedings||Conference:||3rd International Conference on Computing Anticipatory Systems (CASYS 99)||Abstract:||We present in this work a theoretical and conceptual study and some reflections on a fundamental aspect concerning with the structure and brain function: the Cellular Communication. The main interests of our study are the signal transmission mechanisms and the neuronal mechanisms responsible to learning. We propose the consideration of a new kind of communication mechanisms, different to the synaptic transmission, ''Diffusion or Volume Transmission". This new alternative is based on a diffusing messenger as nitric oxide (NO). Our study aims towards the design of a conceptual framework, which covers implications of NO in the artificial neural networks (ANNs), both in neural architecture and learning processing. This conceptual frame might be able to provide possible biological support for many aspects of ANNs and to generate new concepts to improve the structure and operation of them. Some of these new concepts are The Fast Diffusion Neural Propagation (FDNP), the Diffuse Neighbourhood (DNB), (1), the Diffusive Hybrid Neuromodulation (DHN), the Virtual Weights. Finally we will propose a new mathematical formulation for the Hebb learning law, taking into account the NO effect. Along the same lines, we will reflect on the possibility of a new formal framework for learning processes in ANNs, which consist of slow and fast learning concerning with co-operation between the classical neurotransmission and FDNP.We will develop this work from a computational neuroscience point of view, proposing a global study framework of diffusion messenger NO (GSFNO), using a hybrid natural/artificial approach.Finally it is important to note that we can consider this paper the first paper of a set of scientific work on nitric oxide (NO) and artificial neural networks (ANNs): NO and ANNs Series. We can say that this paper has a character of search and query on both subjects their implications and co-existence.||URI:||http://hdl.handle.net/10553/73118||ISBN:||1-56396-933-5||ISSN:||0094-243X||DOI:||10.1063/1.1291268||Source:||Computing Anticipatory Systems [ISSN 0094-243X], v. 517, p. 296-307, (2000)|
|Appears in Collections:||Actas de congresos|
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