Advances in Neural Data Science
Springer International Publishing (Verlag)
978-3-031-70637-0 (ISBN)
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This proceeding volume will contain a collection of peer-reviewed articles arising from the Data Research Camp 2022. The workshop took place on July 12-15, 2022, at the Venice International University, in the venetian island of San Servolo. The Data Research Camp has been a stimulating experience bringing together 28 early-career researchers in statistics and seven international professors with the common task of developing novel statistical methods for complex brain imaging data.
The workshop was motivated by the recent advancements in miniaturized fluorescence microscopy that have made it possible to collect complex data on neuronal responses to stimuli in awake behaving animals. Several ongoing challenges are related to this novel technology including the deconvolution of the temporal signals to extract the spike trains from the noisy calcium data, the estimation of neuronal activation intensity distribution, the spatio-temporal dependence or covariate effect estimation, among others.
Antonio Canale is associate professor of Statistics at the Department of Statistical Sciences of the University of Padova (Italy). His research covers Bayesian parametric and nonparametric methods, factor analysis, functional data analysis, both from the methodological and applied viewpoints. He is the author of a number of papers on methodological and applied statistics, and has served on the scientific committees of national and international conferences.
Alessandra Luati is Professor in Statistics at the Department of Mathematics of Imperial College London (UK) and at the Department of Statistics at the Alma Mater Studiorum University of Bologna (Italy). She has been Adjunct Professor at the Johns Hopkins School of Advanced International Studies (EU), Visiting Erskine Fellow at the University of Canterbury, Christchurch (NZ). Her main research interests are in mathematical statistics, time series analysis and non-linear dynamic models.
Stefano Mazzuco is full professor of Demography at the Department of Statistical Sciences of the University of Padova (Italy). His research mainly focuses on mortality, health and causes of death data analysis, using methods ranging from hierarchical models, functional and compositional data analysis and other methods. He has authored several articles both in demographic and statistical journals and has been part of the scientific and organizing committees of several national and international conferences.
Raffaella Piccarreta is Associate Professor of statistics at Bocconi University, and she is a fellow of the Dondena Centre and of the Bidsa centre at the same University. Her recent research interests focus on the analysis of longitudinal categorical data, on cluster analysis, and on the comparison of models for complex data.
Nicola Sartori is professor of Statistics and head of the faculty board of the PhD program at the Department of Statistical Sciences of the University of Padova (Italy) . His research covers likelihood and pseudo-likelihood methods, inference in the presence of nuisance parameters, higher-order asymptotics, computational statistics. He is the author of several papers on methodological and computational statistics.
Piercesare Secchi is Professor of Statistics at the Department of Mathematics, Politecnico di Milano, member of MOX, the departmental laboratory in modeling and scientific computing, and head of the faculty board of the Data Analytics and Decision Sciences PhD program. His recent research interests focus on statistical methods for object oriented spatial statistics, classification of complex data, functional data analysis, data fusion and integration.
D'Angelo, Exploring the challenges of the analysis of the Allen Brain Observatory dataset.- Alfonzetti, Model free estimation of causal effects of different stimuli on neuron activities.- Barile, Assessing neuron response to external stimuli with a data-driven procedure for spike train extraction and GAMLSS regressions.- Bianco, Bayesian signal extraction in noisy uorescence traces.- Mascaretti and Friel, Bayesian Global-Local Deconvolution of Neurological Data.- Burzacchi, A point process approach for the classification of noisy calcium imaging data.- Girardi, Time Series Methodology for Analyzing Calcium ImagingData.
| Erscheint lt. Verlag | 17.1.2025 |
|---|---|
| Reihe/Serie | Springer Proceedings in Mathematics & Statistics |
| Zusatzinfo | X, 140 p. 30 illus. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Schlagworte | Calcium imaging data • causal inference • machine learning • model-based clustering • Multidimensional time series • Quantitative neuroscience • Spatio-temporal data analysis |
| ISBN-10 | 3-031-70637-4 / 3031706374 |
| ISBN-13 | 978-3-031-70637-0 / 9783031706370 |
| Zustand | Neuware |
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