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:
Receptor(s):
Gene(s):
Transmitter(s):
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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# --- General description

The model simulates spiking networks with different degrees of specific connectivity,
as described in:

Sadeh, Clopath and Rotter (PLOS ONE, 2015).
Processing of Feature Selectivity in Cortical Networks with Specific Connectivity.

Model codes contributed by Sadra Sadeh (s.sadeh@ucl.ac.uk)

Requirements: NEST, Python

[The current codes are written compatible with NEST 2.6.0 and Python 3;
efforts have been made, however, to be backward compatible.]

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# --- List of files

[1] SpecNet_source.py
Source class / functions for simulating networks of spiking neurons
with a specified level of specific connectivity in response to oriented stimuli

[2] defaultParams.py
Default parameters for network simulations

[3] SpecNet_run.py
Runs simulations of networks with different degrees of specific connectivity
in response to different stimulus orientations

[4] SpecNet_preprocess
Sample code for preprocessing the raw results of network simulations,
e.g. to extract mean firing rates and tuning curves

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# --- Testing the model

(i) Set the parameters of your network simulations in [2];

(ii) Run [3] to simulate the networks and save the resulting simulated data;

(iii) Use [4] to preprocess the raw data and plot example network tuning curves.

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20170403 Sadra Sadeh fixed a small typo (line 227 of SpecNt_source.py:
fs_ei changed to fs_ie) that would have caused problem for further
extensions of the model.