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