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The brain in space| old_uid | 15161 |
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| title | The brain in space |
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| start_date | 2015/02/27 |
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| schedule | 14h30 |
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| online | no |
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| details | Invité par Frédéric Chavane. Séminaire tutoré |
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| summary | Surprisingly little is known about the statistics of cortical networks due to an absence of investigation of their weighted and spatial properties. Using brain-wide retrograde tracing experiments in macaque, we are generating a consistent database of between area connections with projection densities, and distances. The network is neither a sparse small-world graph nor scale-free (Markov et al. 2013). Local connectivity accounts for 80% of labeled neurons, meaning that cortex is heavily involved in local function (Marcov et al. 2014). Importantly link weights, are highly characteristic across animals, follow a heavy-tailed lognormal distribution over 6 orders of magnitude, and decay exponentially with distance (Markov et al. 2014a). The statistical properties of the cortex will give insight into the nature of the processing mode of the cortex (Markov and Kennedy, 2013). We are making a weighted network analysis, this reveals a trade off between local and global efficiencies. An important finding is that a distance rule predicts the binary features, the global and local communication efficiencies as well as the clustered topography of the graph (Ercsey-Ravasz et al. 2013). These findings underline the importance of weight-based hierarchical layering in cortical architecture and hierarchical processing, and point to the need to consider the embedded properties of the cortex (Markov and Kennedy 2013, Markov et al. 2014b). |
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| responsibles | Riehle |
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