Un seminario del Dott. Filippo Dainese, Azienda Ospedale-Università Padova

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Un seminario della Prof.ssa Luisa Barzon, Dipartimento di Medicina Molecolare, Università di Padova

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The closing conference of the H2020 EU Project “PD_Pal”: Palliative Care in Parkinson’s Disease will take place on November 25th and 26th, 2022 in Padua.

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by dr. Davide Bernardi, Center for Translational Neurophysiology of Speech and Communication @ Istituto Italiano di Tecnologia (Ferrara, Italy)

When: October 6th, 2022 – 3:15 pm

Where: Sala Seminari, VIMM. Recording available on Mediaspace

Abstract: There is increasing experimental evidence that the activity of single cortical neurons can make a difference to the brain. One particularly striking example is that rats can be trained to respond to the stimulation of a single cell in the barrel cortex. It is not clear how this finding can be reconciled with the usual textbook view that only large neuronal populations can reliably encode information, as is often argued on the basis of the large noise and chaotic dynamics of cortical networks. This talk shows how this problem can be framed theoretically by studying the stimulation of a single neuron in a large random network of integrate-and-fire neurons. In this model, chaotic noise-like fluctuations arise naturally from the combination of spiking dynamics and the random interactions.

A combination of numerical simulations and analytical calculations demonstrates how a simple readout strategy can be used to detect the single neuron stimulation in the activity of a readout subpopulation. Furthermore, I will discuss how a second integrate-and-fire network can perform the readout in a way that is both more realistic and more efficient. In the final part, I will argue how such a readout network, tuned to approximate a differentiator circuit, can detect the single-neuron stimulation in a more biologically detailed model. Most importantly, the effect size does not depend appreciably on the duration or intensity of a constant stimulation, whereas the response probability increases significantly upon injection of an irregular current, in agreement with experimental findings.

Taken together, these results hint at a possible general readout strategy: feed-forward and recurrent inhibitory synapses ensure both the macroscopic stability and the sensitivity to single-neuron perturbation through a selective imbalance in the topological (spatial) and temporal sense.

Short bio: Born in Padova, Davide Bernardi obtained a diploma as a cellist at the Conservatorio di Musica di Padova and a B.A. in physics at the University of Padova. After spending several years as a chamber music and orchestra player, he continued his studies at the Freie Universität Berlin (Germany), where he graduated in physics. As a member of the graduate school “Sensory computation in neural systems” based at the Bernstein Center for Computational Neuroscience Berlin, he achieved a PhD in theoretical physics at the Humboldt University of Berlin with highest distinction (summa cum laude). He currently holds a Post-Doc position at the Center for Translational Neurophysiology of Speech and Communication hosted by the Italian Institute of Technology and the University of Ferrara.

by dr. Thierry Nieus, Università degli Studi di Milano

When: October 6th, 2022 – 2:30 pm

Where: Sala Seminari, VIMM. Recording available on Mediaspace

Abstract: In neuroscience, the structural connectivity matrix of synaptic weights between neurons is one of the critical factors determining the overall function of a network of neurons. The mechanisms of signal transduction have been intensively studied at different time and spatial scales and at both the cellular and molecular level. While a better understanding and knowledge of some basic processes of information handling by neurons has been achieved, little is known about the organization and function of complex neuronal networks. Experimental methods are now available to simultaneously monitor neural activities from a large number of sites in real time.

Here, we present a methodology to infer the connectivity of a population of neurons from their voltage traces.

At first, spikes and putative synaptic events are detected. Then, a multi-class logistic regression is used to fit the putative events to spiking activities. The fit is further constrained, by including a penalization term that regulates the sparseness of the inferred network. The proposed weighted Multi-Class Logistic Regression with L1 penalization (MCLRL) was benchmarked against data obtained from in silico network simulations.

MCLRL properly inferred the connectivity of all tested networks (up to 500 neurons), as indicated by the Matthew correlation coefficient (MCC), already with small samples of network activity (5 to 10 seconds). Then, we tested MCLRL against different conditions, that are of interest in concrete applications. First, MCLRL accomplished to reconstruct the connectivity among subgroups of neurons randomly sampled from the network. Second, the robustness of MCLRL to noise was assessed and the performances remained high (0.95) even in extremely high noise conditions (95% noisy synaptic events). Third, we devised a data driven procedure to gather a proxy of the optimal penalization term, thus envisioning the application of MCLRL to experimental data. The proposed approach is ideally suited for populations recordings, where spikes and post-synaptic recordings can simultaneously be recorded (e.g. genetic encoded voltage indicators). Yet, the main message here is that a small fraction (5%) of genuine synaptic events is sufficient to properly infer the underlying connectivity of a network.

Short bio: Thierry Nieus earned a PhD in Applied Mathematics in 2004 working on computational models of neuronal cells. During his PhD he spent a period abroad in Belgium and France collaborating at the EU projects Cerebellum and Spikeforce. He used information theory tools to quantify the processing of the inputs by in silico and experimentally recorded neural cells. At the fall of 2006, he moved to the Italian Institute of Technology (IIT) working on detailed models of synaptic dynamics. At the IIT he also started working on designing data analysis pipelines to process large scale recordings of brain tissues gathered with cutting-edge technologies. During his stay at the IIT he has acquired a good knowledge of the cellular and sub-cellular processes determining neuron’s activities, of the mathematical and computational approaches used for modeling and of the machine learning, graph theory and information theory tools to investigate the underlying computation. In 2016, he moved to the iTCF laboratory headed by Marcello Massimini (Università degli Studi di Milano – Italy) working on entropy based measures to quantify the complexity of brain signals in humans. He also brought these measures to cell culture networks, cerebellar brain slices and computational models. He has a longstanding experience in teaching computational neuroscience, computer programming and data analysis tools to undergraduate and PhD students. He also supervised the research activity of 4 PhD students. At the fall of 2021, he moved to the HPC Indaco Unitech (Università degli Studi di Milano – Italy), where he is involved in teaching the basics of HPC as well as in data science and neuroscience projects with private companies and research groups.

Promossa dall’Università di Padova, la manifestazione Science4All è rivolta a tutta la cittadinanza e mira a comunicare la scienza in modo semplice e divertente con eventi a libero accesso.

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Il XXX congresso della Associazione Italiana di Psicologia si terrà presso la Scuola di Psicologia dell’Università di Padova: le giornate congressuali inizieranno martedì 27 settembre e termineranno venerdì 30 settembre 2022.

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Neuroscience PhD Retreat will take place on 20 and 21 May in Asiago, Vicenza: the right moment for PhD students and faculty for sharing their ideas, perspectives and experience. There will be a focus on the goals already achieved, improvement suggestions and the path ahead.