Welcome to Energy-Efficient-Touch-Localization’s documentation!

This is the documentation for the code published on GitHub at this page. The code allows for the simulation and training of Spiking Neural Networks. It has been developed in C++ and is structured in such a way that general networks of supported point neurons can be implemented. Supported neuron types are (name-conventions of NEST-simulator are adopted, when possible):

  • iaf_cond_alpha

  • iaf_curr_alpha (current based version of iaf_cond_alpha)

  • aeif_cond_exp

  • aeif_curr_exp (current based version of aeif_cond_exp)

  • aqif_cond_exp (adaptive quadratic integrate and fire)

  • aqif_curr_exp (current based version of aqif_cond_exp)

  • iaf_cond_exp

  • iaf_curr_exp (current based version of iaf_cond_exp)

  • parrot_neuron

In combination with this code, a python3 module has been implemented in order to handle the training process and import and preliminarily analyze the results of the simulations (see Examples).

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