Understanding the impact of recurrent interactions on population tuning: Application to MT cells characterization

Abstract : A ring network model under neural fields formalism with a structured input is studied. Bifurcation analysis is applied to understand the behaviour of the network model under different connectivity regimes and input conditions. The parameter regimes over which the localised input bumps could be preserved, combined or selected are used to identify the potential network regimes under which direction selective cells in MT area exhibiting analogous behaviour could be operating. The parameter regimes are further explored to identify possible transitions in the tuning behaviour with respect to change of driving stimuli as observed in experimental recordings.
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Poster
AREADNE: Research in Encoding And Decoding of Neural Ensembles, Jun 2016, Santorini, Greece
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https://hal.inria.fr/hal-01377606
Contributeur : N V Kartheek Medathati <>
Soumis le : vendredi 7 octobre 2016 - 11:35:05
Dernière modification le : jeudi 18 janvier 2018 - 02:25:25
Document(s) archivé(s) le : vendredi 3 février 2017 - 18:57:15

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N V Kartheek Medathati, Andrew Isaac Meso, Guillaume Masson, Pierre Kornprobst, James Rankin. Understanding the impact of recurrent interactions on population tuning: Application to MT cells characterization. AREADNE: Research in Encoding And Decoding of Neural Ensembles, Jun 2016, Santorini, Greece. 〈hal-01377606〉

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