{"id":5510,"date":"2026-06-02T15:16:03","date_gmt":"2026-06-02T15:16:03","guid":{"rendered":"https:\/\/e-nns.org\/icann2026\/?page_id=5510"},"modified":"2026-06-15T09:05:53","modified_gmt":"2026-06-15T09:05:53","slug":"invited-speakers","status":"publish","type":"page","link":"https:\/\/e-nns.org\/icann2026\/invited-speakers\/","title":{"rendered":"Invited Speakers"},"content":{"rendered":"\n
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Prof. Marco Gori (University of Siena)<\/h3>\n\n\n\n

Marco Gori received the Ph.D. degree in 1990 from Universit\u00e0 di Bologna, Italy, working partly at the School of Computer Science (McGill University, Montreal). He is currently full professor of computer science at the University of Siena, where he is leading the Siena Artificial Intelligence Lab. He is mostly interested in Machine Learning with emphasis on Neural Computation. <\/p>\n\n\n\n

The impact of his research on neural networks emerged mainly from the growing interest in Graph Neural Networks. He introduced the first ideas in the paper \u201cA New Model for Learning in Graph Domains\u201d, by M. Gori, M. Monfardini and F. Scarselli (IJCNN2005) where the keyword Graph Neural Network was coined. A few years later, the most significant paper \u201cGraph Neural Networks,\u201d
IEEE-TNN, 2009 provided a more robust analysis and an accurate experimental evaluation. To date, the paper has received about 14,000 citations (more than 8 citations\/day in the last year). Professor Gori has been the chair of the Italian Chapter of the IEEE Computation Intelligence Society and the President of the Italian Association for Artificial Intelligence. He is a Fellow of IEEE, EurAI, IAPR, and ELLIS.<\/p>\n\n\n\n

Talk: Generative System Dynamics\u00a0 in Recurrent Neural Networks<\/h5>\n\n\n\n

In this talk, I discuss an alternative view of learning from interaction, one that casts time in the leading role. I then show that this alternative path is a candidate to replace transformer-based architectures in carrying out generative processes. The emphasis is on mechanisms that favor generative processes arising simply from the initial conditions of the neural network. I prove that linear units enable universal generation capabilities, which can be extended to piecewise-linear units. Finally, I present evidence of the underlying symbolic structure that emerges from the proposed generation process<\/em><\/p>\n<\/div>\n<\/div>\n\n\n\n

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Prof. \u2060Jos\u00e9 Mar\u00eda Delgado Garc\u00eda (Siviglia, Spain)<\/h3>\n\n\n\n

Jos\u00e9 Mar\u00eda Delgado Garc\u00eda was born in Seville (Spain) in 1945 and graduated in Medicine at Seville University in 1969. He obtained his PhD in 1972 with a study on the electrophysiology of the limbic system. He completed his scientific training at several European (Oxford, Paris) and American (Iowa, New York) research centers.<\/p>\n\n\n\n

In Spain, he founded the Laboratorio de Neurociencia at Seville University (1978\u20131999), where young researchers received training in various aspects of neural motor control and the regenerative capabilities of the central nervous system (CNS). His main scientific contributions were related to the study of the neural mechanisms underlying eye and postural position holding, as well as the roles of nitric oxide, glutamate, and acetylcholine in these processes.<\/p>\n\n\n\n

Later, at Pablo de Olavide University (2000\u20132026), he contributed to the description of the complex premotor neural system involved in the generation of learned motor responses, using in vivo electrophysiology and Pavlovian, instrumental, Go\/No-Go, and social conditioning paradigms. An electrophysiological study from his laboratory, conducted with behaving wild-type and transgenic mice during associative learning, was recognized by Science as one of the ten scientific breakthroughs of 2006.<\/p>\n\n\n\n

Other important contributions of his research group are related to the neural control of the ocular, facial, and respiratory systems, as well as to the regenerative capabilities of the mammalian central nervous system.<\/p>\n\n\n\n

Talk: Functional states corresponding to different types of associative and social learning taks<\/strong><\/h5>\n\n\n\n

The most important thing that I have learned across my scientific life is that the complexity of brain functions can only be approached by multidisciplinary, comparative, and at live approaches. The availability of\u00a0 genetically manipulated mammals (mice and rats) and of sophisticated electrophysiological and pharmacological techniques, susceptible of being applied in behaving animals during the acquisition of new motor and cognitive abilities, have largely facilitated my scientific approach in the past 20 years. In this regard, our group has studied the contribution of cortical, subcortical and cerebellar circuits to associative (Pavlovian, instrumental), social, brain-machine, and decision-making learning paradigms. For this, we have recorded unitary firing rates, local field potentials, and activity dependent changes in synaptic strength in cortical and subcortical neurons during the respective acquisition processes. The main output of our studies is that learning is the result of the activity of wide cortical and subcortical circuits activating particular functional properties of involved synaptic nodes and that brain functions during learning processes have to be studied at the very moment of the acquisition process.<\/em><\/p>\n<\/div>\n<\/div>\n\n\n\n