Michele Giugliano, Univ. of Modena and Reggio Emilia, Italy

MG is Principal Investigator and Associate Professor of Bioengineering at the Univ. of Modena & Reggio Emilia (Italy), as well as Professor of Physiology at the International School of Advanced Studies, Trieste (Italy). His research lab is active in Neuroscience, Neuroengineering, nervous system modeling and high-tech cellular electrophysiology and focuses on two directions: A) the study of the molecular, cellular and tissue mechanisms underlying the functions of the brain; and B) the exploitation of technologies, methods, and quantitative approaches typical of physics and engineering to study, repair, recreate, or increase brain functions. In the space defined by these directions, during the past 15 years his lab was productive in the area of common overlap among cellular electrophysiology, computational neuroscience and Neurotechnology.

‘Broadband’ cortical neuronal ensembles
Both in Neuroscience and in AI, the input-output transfer function of the units composing a large network is a pivotal element. Known as "activation function" (in Machine Learning) or as the frequency-current curve (in Neurobiology), it is a known mechanism of non-linearity as well as a biophysical primitive for neural computation. We also know that its knowledge is instrumental (in Computational Neuroscience) to analyse and predict the collective behavior of real neuronal circuits by mean-field theories. Finally, in experimental Neuroscience, the estimate of the frequency-current curve of nerve cells has been used for over three decades to classify and make sense of neuronal diversitys. However, such a static description is inadequate to interpret how microcircuits and large networks of the brain process time- varying stimuli. Early theoretical studies and late experimental work from our group and others revealed how probing single-cell dynamical response properties is necessary to interpret of ultra-fast ensemble responses and other collective network phenomena. In this talk, I will review the results on dynamical response properties of neurons and neuronal ensembles and put them into context of the findings of unexpected differences between rodent and human cortical neurons. Whether these generalised biological features should be also considered for (Neuromorphic) implementation of AI systems, based on spiking neural models, is a question I will leave open to the audience.