PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 15359013

Quantitative analysis of connectivity in the visual cortex: extracting function from structure.

Julien Vezoli·Arnaud Falchier·Bertrand Jouve·Kenneth Knoblauch·Malcolm Young·Henry Kennedy
View original source ↗Export metadata ↓

Abstract

It is generally agreed that information flow through the cortex is constrained by a hierarchical architecture. Lack of precise data on areal connectivity leads to indeterminacy of existing models. The authors introduce two quantitative parameters (SLN and FLN) that hold the promise of resolving such indeterminacy. In the visual system, using a very incomplete database, provisional hierarchies are in line with the recent proposal of higher functions of area V1 and suggest a hitherto unsuspected central function of the frontal eye field.

Explore related subjects

Nerve NetNeural PathwaysVisual CortexJournal ArticleResearch Support, Non-U.S. Gov'tReview

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Julien Vezoli, Arnaud Falchier, Bertrand Jouve, Kenneth Knoblauch, Malcolm Young, Henry Kennedy. 2004. Quantitative analysis of connectivity in the visual cortex: extracting function from structure.. https://doi.org/10.1177/1073858404268478

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed1999

Quantum dissipation and neural net dynamics.

Inspired by the dissipative quantum model of brain, we model the states of neural nets in terms of collective modes by the help of the formalism of Quantum Field Theory. We exhibit an explicit neural net model which allows to memorize a sequence of several informations without reciprocal destructive interference, namely we solve the overprinting problem in such a way last registered information does not destroy the ones previously registered. Moreover, the net is able to recall not only the last registered information in the sequence, but also anyone of those previously registered.

Nerve Net↗
PubMed1998

Irregular synchronous activity in stochastically-coupled networks of integrate-and-fire neurons.

We investigate the spatial and temporal aspects of firing patterns in a network of integrate-and-fire neurons arranged in a one-dimensional ring topology. The coupling is stochastic and shaped like a Mexican hat with local excitation and lateral inhibition. With perfect precision in the couplings, the attractors of activity in the network occur at every position in the ring. Inhomogeneities in the coupling break the translational invariance of localized attractors and lead to synchronization within highly active as well as weakly active clusters. The interspike interval variability is high, consistent with recent observations of spike time distributions in visual cortex. The robustness of our results is demonstrated with more realistic simulations on a network of McGregor neurons which model conductance changes and after-hyperpolarization potassium currents.

Nerve Net↗
PubMedDate not supplied

Stochastic resonance in a pulse neural network with a propagational time delay.

Stochastic resonance in a coupled FitzHugh-Nagumo equation with a propagational time delay is investigated. With an appropriate set of parameter values. i.e. the frequency of the periodic input, the propagational time delay, and the coupling strength, a deterministic firing induced by additive noise is observed, and its dependence on the number of neurons is examined. It is also found that a network composed of two assemblies shows a competitive behavior under control of the noise intensity.

Nerve Net↗