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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 23:02] – [Synapse and Plasticity Models] 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.