Fri Feb 4, 2011. 10.30am-2.30pm. Salon de Actos Escuela Tecnica Superior de Ingenieria Informatica y Telecomunicacion. UGR Follow the event in live streaming at the link CEVUG/UGR Hashtag for twitter, #sejnowski Download Final Schedule Donwload the poster for the event Funded by: GENIL: Granada Excellence Network of Innovation Laboratories MSc in Soft Computing and Intelligent Systems. UGR MSc in Multimedia Systems. UGR MSc in Computer and Network Engineering. UGR MSc […]
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Dr. Terrence Sejnowski HHMI and Salk Insitute, USA Convergence and Divergence between Biological and Artificial Intelligence Fri Feb 4, 2011. 10.30am. Terrence Sejnowski is a pioneer in computational neuroscience and his goal is to understand the principles that link brain to behavior. His laboratory uses both experimental and modeling techniques to study the biophysical properties of synapses and neurons and the population dynamics of large networks of neurons. New computational […]
Dr Sebastiano Stramaglia University of Bari, Italy Causal approaches to the inference of dynamical networks Thu Sep 30, 2010. 11.00am. Granger causality is a major approach to measure the information flow between variables. In its original formulation, Granger causality is based on linear modelling of the data. In the last years, the problem of causality attracted interest also in the machine learning community, and a nonlinear version of Granger causality […]
Dr Adam Barrett Sackler Centre for Consciousness Science University of Sussex, Brighton, UK The Challenge of Measuring Consciousness Thu Mar 18, 2010. 11.30am. For much of the twentieth century, consciousness research was exclusively carried out as philosophical discourse. However, due to modern advances in neuroimaging, coupled with new multi-disciplinary approaches to neuroscience and psychology, a new science of consciousness has started to emerge. In this talk, I discuss some of […]
Dr Adam Barrett ANC, University of Edinburgh, UK Shannon Information Capacity of Discrete Synapses Fri May 15, 2009. 11.00am. Memory in biological neural systems is believed to be stored in the synaptic weights. Computational models of such memory systems have been constructed in order to investigate, for example, optimal learning rules and storage capacity. Commonly, a synaptic weight in such models is represented by an unbounded, continuous real number. However, […]
Dr Daniele Marinazzo CNRS, Paris Machine Learning applications to EEG data: phase synchronization, predictability and causality in multi-EEG recordings Monday May 4, 2009. 11.00 am The fundamental mechanisms underlying biological systems can be understood by studying complexity of recorded data and measuring in what proportion the individual components exchange information among each other. The study of phase synchronization may, in particular, reveal interactions which are highly nonlinear and do not […]