POPCODE
Statistical methods for modelling population activity in visual cortex
| Coordinatore | UNIVERSITY COLLEGE LONDON
Organization address
address: GOWER STREET contact info |
| Nazionalità Coordinatore | United Kingdom [UK] |
| Totale costo | 165˙040 € |
| EC contributo | 165˙040 € |
| Programma | FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | FP7-PEOPLE-2009-IEF |
| Funding Scheme | MC-IEF |
| Anno di inizio | 2010 |
| Periodo (anno-mese-giorno) | 2010-04-01 - 2012-03-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
UNIVERSITY COLLEGE LONDON
Organization address
address: GOWER STREET contact info |
UK (LONDON) | coordinator | 165˙040.80 |
Mappa
Word cloud
Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.
Obiettivo del progetto (Objective)
'One of the central goals of systems neuroscience is to understand how neurons in the visual system represent and analyze the environment. In order to understand the capabilities of the visual system, we have to understand how populations of neurons communicate and collectively process the visual input. Yet, while there is a wealth of knowledge about the response properties of single cells, we still know very little about coding and computations at the level of neural populations. Recent technological advances now make possible to record from many neurons simultaneously. However, in order to make sense of multi-cell measurements and understand information processing in neural ensembles, we need powerful statistical tools for analyzing this high-dimensional, complex data. The goal of this project is to get a better understanding of neural population coding in primary visual cortex. We will develop statistical methods for modelling neural population activity and apply them to multi-electrode recordings and 2-photon population measurements obtained by our experimental collaborators. We will assess interactions between neurons and the influence of local field potentials. As neural populations exhibit rich temporal dynamics that are not accessible with techniques relying on averaging across multiple stimulus presentations, our model will allow single-trial analysis of population dynamics. By accurately characterizing the structure of noise and variability in the population, we will be able constrain computational and circuit models of visual processing. We will provide the first quantitative, integrated characterization of neural activity in large cortical populations. In contrast to most studies that investigate neural coding in single neurons or pairs of neurons, our study has the potential to reveal properties of neural coding on a population level—which could be qualitatively different, and therefore provide a new view of neural coding in the visual system.'