Dataset Information

Self-organization versus top-down planning in the evolution of a city.

ABSTRACT: Interventions of central, top-down planning are serious limitations to the possibility of modelling the dynamics of cities. An example is the city of Paris (France), which during the 19th century experienced large modifications supervised by a central authority, the 'Haussmann period'. In this article, we report an empirical analysis of more than 200 years (1789-2010) of the evolution of the street network of Paris. We show that the usual network measures display a smooth behavior and that the most important quantitative signatures of central planning is the spatial reorganization of centrality and the modification of the block shape distribution. Such effects can only be obtained by structural modifications at a large-scale level, with the creation of new roads not constrained by the existing geometry. The evolution of a city thus seems to result from the superimposition of continuous, local growth processes and punctual changes operating at large spatial scales.

SUBMITTER: Barthelemy M

PROVIDER: S-EPMC3703887 | biostudies-other | 2013

REPOSITORIES: biostudies-other

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Self-organization versus top-down planning in the evolution of a city.

Barthelemy Marc M Bordin Patricia P Berestycki Henri H Gribaudi Maurizio M

Scientific reports 20130101

Interventions of central, top-down planning are serious limitations to the possibility of modelling the dynamics of cities. An example is the city of Paris (France), which during the 19th century experienced large modifications supervised by a central authority, the 'Haussmann period'. In this article, we report an empirical analysis of more than 200 years (1789-2010) of the evolution of the street network of Paris. We show that the usual network measures display a smooth behavior and that the m ...[more]

PMID: 23835429

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Project description:Unified visual perception relies on the integration of bottom-up and top-down inputs in the primary visual cortex (V1), with top-down inputs known to provide behavior-related modulation on visual processing. However, the organization of top-down inputs in V1 remains unclear. Here, using optogenetics-assisted circuit mapping, we characterized how multiple top-down inputs from higher-order cortical and thalamic areas engage excitatory and inhibitory neurons in V1. Systematic layer- and cell-type-specific profiling of the innervation properties of top-down inputs ultimately revealed that each top-down input employs a unique laminar profile when innervating V1. These profiles partially overlap in superficial layers, bypass layer 4 (L4), and clearly segregate upon reaching deep layers. Specifically, inputs from the medial secondary visual cortex (V2M) and anterior cingulate cortex (ACA) preferentially activate L6 neurons, while inputs from the ventrolateral orbitofrontal cortex (ORBvl) and lateral posterior thalamic nucleus (LP) activate L5 neurons. Having defined the inputs, we conducted independent optogenetic activation studies and discovered that ORBvl and LP inputs selectively activate two types of pyramidal neurons (Pyrs) in L5: Pyr <-- ORBvl and Pyr <-- LP neurons, each with specific electrophysiological properties and gene expression profiles. Retrograde mapping subsequently revealed that Pyr <-- ORBvl neurons preferentially innervate subcortical areas and Pyr <-- LP neurons innervate cortical areas, indicating parallel processing of ORBvl and LP inputs in Pyr-type-specific subnetworks. Further, we found mutual inhibition between these two subnetworks, as LP inputs indirectly inhibit Pyr <-- ORBvl neurons and ORBvl inputs inhibit Pyr <-- LP neurons through local inhibitory neurons. Our study thus deepens understanding of the neuronal mechanisms involved in top-down modulation of visual processing by providing a valuable resource characterizing the layer- and cell-type-specific organization of top-down inputs in V1 and by revealing that L5 Pyr-type-specific subnetworks engage in parallel processing of corticocortical and thalamocortical top-down inputs.

Dataset Information

Self-organization versus top-down planning in the evolution of a city.

Publications

Self-organization versus top-down planning in the evolution of a city.

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