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.
Model Type: Realistic Network
Region(s) or Organism(s): Neocortex
Cell Type(s): Abstract integrate-and-fire leaky neuron
Model Concept(s): Sensory processing; Orientation selectivity; Feature selectivity
Simulation Environment: NEST; Python (web link to model)
Implementer(s): Sadeh, Sadra [s.sadeh at ucl.ac.uk]
References:
Sadeh S, Clopath C, Rotter S. (2015). Processing of Feature Selectivity in Cortical Networks with Specific Connectivity. PloS one. 10 [PubMed]