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ETIC SIGNED

Encoding and Transmission of Information in the Mouse Somatosensory Cortex

Total Cost €

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EC-Contrib. €

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Partnership

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Project "ETIC" data sheet

The following table provides information about the project.

Coordinator
FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA 

Organization address
address: VIA MOREGO 30
city: GENOVA
postcode: 16163
website: www.iit.it

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Italy [IT]
 Project website http://eticx.eu
 Total cost 168˙277 €
 EC max contribution 168˙277 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2015
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2016
 Duration (year-month-day) from 2016-07-01   to  2018-06-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA IT (GENOVA) coordinator 168˙277.00

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 Project objective

A major challenge in neuroscience is to understand how neurons code information. Optogenetics techniques provide the opportunity of controlling neuronal activity with high temporal and spatial resolution and thus testing causally the role of a specific encoding strategy. However, to enable us to crack the neural code, these optical approaches need to be complemented by theoretical developments that: 1) identify the response variables of a neural population that carry the most information about sensory stimuli and thus allows to build a hypothesis about the neural code it uses; 2) create stimulation protocols specifically designed to rigorously test the hypothesis; 3) apply appropriate statistical analyses that determine whether different information-carrying components of neural population activity are transmitted through downstream networks. Here I will develop a novel theoretical framework to address these issues and understand how the mammalian cortex encodes sensory information, using the mouse somatosensory cortex as an experimental model. I will develop a set of computational techniques aimed at characterizing the encoding and transmission of information in layer IV and then in one of its main targets: layer II/III. Specifically, I will develop Non-Negative Matrix Factorization methods to characterize how large-scale populations of layer IV encode whisker information. I will develop a Wavelet Transform based method to decompose the electrophysiological responses of layer II/III neurons into independently contributing temporal scales and establish which whisker-informative components of layer IV population activity are transmitted to the response of layer 2/3 neurons. This project constitutes a completely novel and comprehensive approach to crack the neural code that lies at the interface between computational neuroscience, optogenetic and neurophysiology and that will provide a crucial step towards the optimal applicability of causal optogenetic techniques.

 Publications

year authors and title journal last update
List of publications.
2017 Stefano Zucca, Giulia D’Urso, Valentina Pasquale, Dania Vecchia, Giuseppe Pica, Serena Bovetti, Claudio Moretti, Stefano Varani, Manuel Molano-Mazón, Michela Chiappalone, Stefano Panzeri, Tommaso Fellin
An inhibitory gate for state transition in cortex
published pages: , ISSN: 2050-084X, DOI: 10.7554/eLife.26177
eLife 6 2019-06-13
2017 Martini, Francisco J; Molano-Mazón, Manuel; Maravall, Miguel
Interspersed distribution of selectivity to kinematic stimulus features in supragranular layers of mouse barrel cortex
published pages: , ISSN: 1047-3211, DOI: 10.6080/K02F7KCP
Cerebral Cortex 1 2019-06-13
2016 Manuel Molano-Mazon Arno Onken Houman Safaai and Stefano Panzeri
A Novel Approach to the Analysis of Multiplexed Neural Codes
published pages: , ISSN: , DOI: 10.12751/nncn.bc2016.0136
Bernstein Conference 2016 September 2016 2019-06-13
2018 Molano-Mazon, Manuel; Onken, Arno; Piasini, Eugenio; Panzeri, Stefano
Synthesizing realistic neural population activity patterns using Generative Adversarial Networks
published pages: , ISSN: , DOI: 10.5281/zenodo.1298385
ICLR2018 April-May 2018 2019-06-13
2018 Molano-Mazon, Manuel; Onken, Arno; Piasini, Eugenio; Panzeri, Stefano
Synthesizing realistic neural population activity patterns using Generative Adversarial Networks
published pages: , ISSN: , DOI: 10.5281/zenodo.1298389
BARCCSYN2018 May 2018 2019-06-13
2018 Molano-Mazon, Manuel; Onken, Arno; Piasini, Eugenio; Panzeri, Stefano
Synthesizing realistic neural population activity patterns using Generative Adversarial Networks
published pages: , ISSN: , DOI: 10.5281/zenodo.1217050
ICLR 2018 2019-06-13
2017 Manuel Molano-Mazon; Arno Onken; Jian K. Liu; Mathew E Diamond; Tim Gollisch; Houman Safaai; Stefano Panzeri
Information Jitter Derivative Method: A Novel Approach to the Analysis of Multiplexed Neural Codes
published pages: , ISSN: , DOI: 10.5281/zenodo.1163395
SFN2017 November 2017 2019-06-13
2017 Manuel Molano-Mazon; Arno Onken; Jian K. Liu; Tim Gollisch; Houman Safaai; Stefano Panzeri
Information Jitter Derivative Method: A Novel Approach to the Analysis of Multiplexed Neural Codes
published pages: , ISSN: , DOI: 10.5281/zenodo.814832
BARCCSYN2017 June 2017 2019-06-13
2016 Manuel Molano-Mazon; Arno Onken; Jian K. Liu; Tim Gollisch; Houman Safaai; Stefano Panzeri
Information Jitter Derivative Method: A Novel Approach to the Analysis of Multiplexed Neural Codes
published pages: , ISSN: , DOI: 10.5281/zenodo.814803
NIPS conference workshop December 2016 2019-06-13

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