Auryn simulator

Simulator for spiking neural networks with synaptic plasticity

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tutorials:writing_your_own_plasticity_model [2016/08/18 16:56] – [Changing the plasticity model] zenketutorials:writing_your_own_plasticity_model [2018/02/07 23:11] (current) – Adds final paragraph on unit tests zenke
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   * 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]]   * 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 |}} {{ :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.
  
  
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 ===== Understanding plasticity in Auryn ===== ===== Understanding plasticity in Auryn =====
  
-In most cases you will want to use Auryn to implement your own synapse model. The easiest to do this is by understanding and modifying an existing model. Most of the plasticity models in Auryn are implemented in source files which contain the acronym STDP, e.g. [[manual:STDPConnection]], [[manual:STDPwdConnection]], [[manual:SymmetricSTDPConnection]] or "Triplet" in the case of [[manual:TripletConnection]] which is used in this [[examples:sim_background|example]]. You will find them in the ''src/auryn'' directory, or take a look at the [[http://www.fzenke.net/auryn/doxygen/current/annotated.html|class index]]. All the classes implement [[manual:SparseConnection]] objects with plasticity on top. They already implement all the purely virtual functions of the base class [[manual:Connection]] and specific functions to implement plasticity. In the following, I will explain how to understand these classes and how to easily modify them according to your needs. In particular, I will cover how to define your own synaptic traces and how to use them to define weight updates. Finally, I will illustrate how synaptic weights can be integrate in a time continuous manner, for instance to implement a weight decay, homeostatic scaling or other continuous processes.+In most cases you will want to use Auryn to implement your own synapse model. The easiest to do this is by understanding and modifying an existing model. Most of the plasticity models in Auryn are implemented in source files which contain the acronym STDP, e.g. [[manual:STDPConnection]], [[manual:STDPwdConnection]], [[manual:SymmetricSTDPConnection]] or "Triplet" in the case of [[manual:TripletConnection]] which is used in this [[examples:sim_background|example]]. You will find them in the ''src/auryn'' directory, or take a look at the [[http://fzenke.net/auryn/doxygen/current/annotated.html|class index]]. All the classes implement [[manual:SparseConnection]] objects with plasticity on top. They already implement all the purely virtual functions of the base class [[manual:Connection]] and specific functions to implement plasticity. In the following, I will explain how to understand these classes and how to easily modify them according to your needs. In particular, I will cover how to define your own synaptic traces and how to use them to define weight updates. Finally, I will illustrate how synaptic weights can be integrate in a time continuous manner, for instance to implement a weight decay, homeostatic scaling or other continuous processes.
  
  
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 tr_post_hom = dst->get_post_trace(tau_hom); tr_post_hom = dst->get_post_trace(tau_hom);
 </code> </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) ==== ==== Weight updates at spiking events (propagate) ====
  
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 ===== Changing the plasticity model ===== ===== Changing the plasticity model =====
  
-Most of the plasticity models in Auryn follow the design principles introduced above (e.g. http://www.fzenke.net/auryn/doxygen/current/classauryn_1_1STDPConnection.html). Suppose, you would like to change the plasticity model, all you need to do is to copy TripletConnection.h and TripletConnection.cpp to YourNameConnection.h and .cpp and then the ''dw_pre'' and ''dw_post'' would be the first places start to change things. Of course you can declare new parameters in the header of the Connection object and either directly set them from the main program, hard code them m( or access them via a bunch of getters or setters. Moreover, in many cases it might be important for you to redefine ''dw_pre'' and ''dw_post'' altogether. For instance if you would like to implement a weight dependence, you will need to include our above ''*weight'' variable into the parameter list of these functions. Similarly, you might want to access the postsynaptic membrane voltage through ''dst->get_mem(NeuronID id)'' and pass it to you ''dw'' functions ... I will let you experiment and hope that I will have the time to include some more examples here soon.+Most of the plasticity models in Auryn follow the design principles introduced above (e.g. http://fzenke.net/auryn/doxygen/current/classauryn_1_1STDPConnection.html). Suppose, you would like to change the plasticity model, all you need to do is to copy TripletConnection.h and TripletConnection.cpp to YourNameConnection.h and .cpp and then the ''dw_pre'' and ''dw_post'' would be the first places start to change things. Of course you can declare new parameters in the header of the Connection object and either directly set them from the main program, hard code them m( or access them via a bunch of getters or setters. Moreover, in many cases it might be important for you to redefine ''dw_pre'' and ''dw_post'' altogether. For instance if you would like to implement a weight dependence, you will need to include our above ''*weight'' variable into the parameter list of these functions. Similarly, you might want to access the postsynaptic membrane voltage through ''dst->get_mem(NeuronID id)'' and pass it to you ''dw'' functions ... I will let you experiment and hope that I will have the time to include some more examples here soon.
  
  
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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. 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. 
tutorials/writing_your_own_plasticity_model.1471539379.txt.gz · Last modified: 2016/08/18 16:56 by zenke