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--- tutorials:writing_your_own_plasticity_model [2017/04/24 19:15] – Changes links to fzenke.net zenke
+++ tutorials:writing_your_own_plasticity_model [2018/02/07 23:11] (current) – Adds final paragraph on unit tests zenke
@@ Line 6: / Line 6: @@
   * Zenke, F., and Gerstner, W. (2014). Limits to high-speed simulations of spiking neural networks using general-purpose computers. Front Neuroinform 8, 76. [[http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00076/abstract|full text]]
-If you can write down a learning rule as a differential equation involving spike trains, synaptic traces and specific postsynaptic quantities, such as the membrane potential, Auryn will bring everything need to so so intuitively. Here is an example from Gerstner and Kistler (2002):
+If you can write down a learning rule as a differential equation involving spike trains, synaptic traces and specific postsynaptic quantities, such as the membrane potential, Auryn will bring everything you need to implement this learning rule intuitively. Here is an example from Gerstner and Kistler (2002):
 {{ :tutorials:stdpgeneral.png |}}
-In Auryn you can implement this type of learning rule very intuitively if the ''u'' can be written as a function of synaptic traces and postsynaptic quantities (for many standard cases the ''u'' are synaptic traces themselves). To that end, Auryn has native support for such pre- or postsynaptic traces.  For historical reasons, I typically use the letter ''z'' for synaptic traces, which is what you will find below.
+In Auryn you can implement this type of learning rule if the ''u'' can be written as a function of synaptic traces and postsynaptic quantities (for many standard cases the ''u'' are synaptic traces themselves). To that end, Auryn has native support for such pre- or postsynaptic traces.  For historical reasons, I typically use the letter ''z'' for synaptic traces, which is what you will find below.
@@ Line 47: / Line 47: @@
 tr_post_hom = dst->get_post_trace(tau_hom);
 </code>
-which initializes the traces using their respective time constants tau_* and registers them to either the presynaptic (''src'') or the postsynaptic (''dst'') [[manual:NeuronGroup]] respectively. By doing so, the traces will be automatically evolve (decay over time) and be incremented by one upon each spike of the corresponding [[manual:NeuronGroup]]. Moreover, if multiple [[manual:Connection]] objects were to define a trace with the same time constant for the same [[manual:NeuronGroup]], Auryn 'knows' about this and internally only computes the trace once to speed up computation. The current value of a trace can then be accessed in the code via ''tr_post->get(NeuronID id)'' which we will use in the next section to compute weight updates.
+which initializes the traces using their respective time constants tau_* and registers them to either the presynaptic (''src'') or the postsynaptic (''dst'') [[manual:NeuronGroup]] respectively. By doing so, the traces will be automatically evolved (decay over time) and incremented by one upon each spike of the corresponding [[manual:NeuronGroup]]. Moreover, if multiple [[manual:Connection]] objects were to define a trace with the same time constant for the same [[manual:NeuronGroup]], Auryn 'knows' about this and internally only computes the trace once to speed up computation. The current value of a trace can then be accessed in the code via ''tr_post->get(NeuronID id)'' which we will use in the next section to compute weight updates.
 ==== Weight updates at spiking events (propagate) ====
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 where you will have to define and initialize the variable time_scaling somewhere in your connection object. It will give you the new step width in multiples of Auryn's time step dt.
+This should give you the necessary tools to start out with writing your own plasticity models. As with all code, write simple unit tests to ensure you are simulating what you want to simulate. Happy coding.