Auryn simulator

Simulator for spiking neural networks with synaptic plasticity

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tutorials:multiple_synaptic_state_variables

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tutorials:multiple_synaptic_state_variables [2014/12/15 09:21] – [Evolve function] zenketutorials:multiple_synaptic_state_variables [2014/12/15 09:25] – zenke
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 This change will now propagate weights stored in ''lpw'', but so far these weights are not plastic, which we will change in the next step. For now it is a good idea to save the changes and try compiling the new class again to see if everything runs without error. This change will now propagate weights stored in ''lpw'', but so far these weights are not plastic, which we will change in the next step. For now it is a good idea to save the changes and try compiling the new class again to see if everything runs without error.
  
-==== Evolve function ====+==== Implementing the evolve function to do the low-pass filtering ====
  
 So far the values stored in ''lpw'' do not change over time. We are now going to change that by implementing the method ''evolve()'' which will do the actual low-pass filtering of ''w'' and store the results in ''lpw''. Since this function is called in every simulation time step and it affects all the weights (~O(N^2)) is is a good idea to wrap slow processes in a construct that only runs them every so many time steps. To do this we start by adding the following lines to the header .h file. So far the values stored in ''lpw'' do not change over time. We are now going to change that by implementing the method ''evolve()'' which will do the actual low-pass filtering of ''w'' and store the results in ''lpw''. Since this function is called in every simulation time step and it affects all the weights (~O(N^2)) is is a good idea to wrap slow processes in a construct that only runs them every so many time steps. To do this we start by adding the following lines to the header .h file.
tutorials/multiple_synaptic_state_variables.txt · Last modified: 2016/08/18 16:58 by zenke