A spiking NN for amplification of feature-selectivity with specific connectivity (Sadeh et al 2015)

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Accession:225301
The model simulates large-scale inhibition-dominated spiking networks with different degrees of recurrent specific connectivity. It shows how feature-specific connectivity leads to a linear amplification of feedforward tuning, as reported in recent electrophysiological single-neuron recordings in rodent neocortex. Moreover, feature-specific connectivity leads to the emergence of feature-selective reverberating activity, and entails pattern completion in network responses.
Reference:
1 . Sadeh S, Clopath C, Rotter S (2015) Processing of Feature Selectivity in Cortical Networks with Specific Connectivity. PLoS One 10:e0127547 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Channel(s):
Gap Junctions:
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Simulation Environment: NEST; Python (web link to model);
Model Concept(s): Sensory processing; Orientation selectivity; Feature selectivity;
Implementer(s): Sadeh, Sadra [s.sadeh at ucl.ac.uk];
 
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