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Advances in Neural Data Science : Data Research Camp 2022, Venice, Italy, July 12–15 / edited by Antonio Canale, Alessandra Luati, Stefano Mazzuco, Raffaella Piccarreta, Nicola Sartori, Piercesare Secchi.
Springer Nature - Springer Mathematics and Statistics (R0) eBooks 2025 English International Available online
View online- Format:
- Book
- Author/Creator:
- Canale, Antonio.
- Series:
- Springer Proceedings in Mathematics & Statistics, 2194-1017 ; 475
- Language:
- English
- Subjects (All):
- Machine learning.
- Machine Learning.
- Local Subjects:
- Machine Learning.
- Physical Description:
- 1 online resource (190 pages)
- Edition:
- 1st ed. 2025.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
- Summary:
- 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.
- Contents:
- 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.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 9783031706387
- 3031706382
- OCLC:
- 1499718732
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