Auryn simulator

Simulator for spiking neural networks with synaptic plasticity

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manual:start [2016/07/05 06:00] – [Neurons and populations] zenkemanual:start [2016/12/01 17:12] – [IO File Formats] zenke
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-Auryn is a set of lightweight classes combined with some examples of how to use them. The power of the framework derives from its speed and extensibility if you do not hesitate to write a few lines of C++ code. +====== Compiling the library ====== 
 + 
 +To get started you need to download and compile Auryn. See [[:quick start]] or [[CompileAuryn]] for instructions.
  
  
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 ===== Neurons and populations ===== ===== Neurons and populations =====
  
-Objects exchanging spikes such as neurons are simulated as groups in Auryn (in most cases you won't use Auryn to simulate one neuron in isolation, but many of them). Managing neurons in groups makes allows efficient simulation through vectorization, but it also makes it easier to declare and connect a simulation, because these groups can best be thought of as populations of functionally identical (or at least similar) units. A vanilla balanced network can, for instance, be thought of as consisting of a population of excitatory and inhibitory neurons and maybe a third population of Poisson input neurons. In a Auryn simulation this would correspond to distinct [[NeuronGroup|neuron groups]] of a particular type. For instance, Auryn comes with a bunch of standard integrate-and-fire neuron models already implemented. They are all descendants of [[NeuronGroup]]. A good general purpose neuron model to start which implements conductance based synapses is for instance [[IFGroup]] (or [[TIFGroup]] if you don't want a slow NMDA-like conductance) or the [[AdExGroup|AdEx model]] by Brette and Gerstner (2005). If you are used to delta-current synaptic input you might want to take a look at [[IafPscDeltaGroup]], which was written after the example of a neuron model in NEST with the same name. Finally, in many cases you will ultimately want to write your own neuron model which is straight forward in Auryn (see [[tutorials::neurongroup|creating your own NeuronGroup]]).+Objects exchanging spikes such as neurons are simulated as groups in Auryn (in most cases you won't use Auryn to simulate one neuron in isolation, but many of them). Managing neurons in groups makes allows efficient simulation through vectorization, but it also makes it easier to declare and connect a simulation, because these groups can best be thought of as populations of functionally identical (or at least similar) units.  
 +{{:manual:network_sketch1.png?150 |}} A vanilla balanced network can, for instance, be thought of as consisting of a population of excitatory and inhibitory neurons and maybe a third population of Poisson input neurons. In a Auryn simulation this would correspond to distinct [[NeuronGroup|neuron groups]] of a particular type for the excitatory and the inhibitory populations respectively. External Poisson neurons would be modeled as a third group and so forth. Each NeuronGroup is represented as a C++ object and synaptic connections are then defined between these objects (see below). Auryn comes with a bunch of standard integrate-and-fire neuron models already implemented. They are all descendants of [[NeuronGroup]]. A good general purpose neuron model to start which implements conductance based synapses is for instance [[IFGroup]] (or [[TIFGroup]] if you don't want a slow NMDA-like conductance) or the [[AdExGroup|AdEx model]] by Brette and Gerstner (2005). If you are used to delta-current synaptic input you might want to take a look at [[IafPscDeltaGroup]], which was written after the example of a neuron model in NEST with the same name. Finally, in many cases you will ultimately want to write your own neuron model which is straight forward in Auryn (see [[tutorials::neurongroup|creating your own NeuronGroup]]).
  
  
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 In many cases you will want to connect at least some of your populations with random sparse connectivity. To do so you simply instantiate an object of type [[SparseConnection]] which does exactly that. [[SparseConnection]] comes with a set of tools to directly add Hebbian assemblies or other simple structures into the synaptic weight matrix. However, if you want even more structure [[SparseConnection]] can import MatrixMarket files, which conveniently allows you to build complicated connectivity structures in MATLAB or Python and then load them into your simulation. In many cases you will want to connect at least some of your populations with random sparse connectivity. To do so you simply instantiate an object of type [[SparseConnection]] which does exactly that. [[SparseConnection]] comes with a set of tools to directly add Hebbian assemblies or other simple structures into the synaptic weight matrix. However, if you want even more structure [[SparseConnection]] can import MatrixMarket files, which conveniently allows you to build complicated connectivity structures in MATLAB or Python and then load them into your simulation.
  
-If you want to simulate plastic synapses simply use a Connection object of type [[STPDConnection]] or for instance [[TripletConnection]] for triplet STDP (after Pfister and Gerstner). You want short-term plasticity instead, then [[STPConnection]] is for you. It implements the Tsodyks-Markram model. You want to define arbitrarily shaped STDP windows and do not care about all-to-all spike interactions? Use [[PairInteractionConnection]]. In man cases you will want to write your own plasticity model -- have a look at this tutorial which will help you to get started [[tutorials:writing_your_own_plasticity_model]].+If you want to simulate plastic synapses simply use a Connection object of type [[STPDConnection]] or for instance [[TripletConnection]] for triplet STDP (after Pfister and Gerstner). You want short-term plasticity instead, then [[STPConnection]] is for you. It implements the Tsodyks-Markram model. You want to define arbitrarily shaped STDP windows and do not care about all-to-all spike interactions? Use [[PairInteractionConnection]]. In most cases you will want to write your own plasticity model -- have a look at this tutorial which will help you to get started [[tutorials:writing_your_own_plasticity_model]].
  
 ===== External Input ===== ===== External Input =====
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 Auryn was specifically designed to study synaptic plasticity. To see plasticity in action you can monitor single synaptic weights or groups of them using a [[WeightMonitor]] object. If you are only interested in let's say the mean weight change in one of the plastic [[Connection]] objects your defined (i.e. the mean weight between two neuronal populations in your simulation), you can use [[WeightStatsMonitor]]. You will find other Monitor classes in the Auryn ''src'' directory (see also the [[http://www.fzenke.net/auryn/doxygen/current/annotated.html|Class index]]; Monitor classes should be suffixes with ''Monitor'') or you can write your own for your specific needs.  Auryn was specifically designed to study synaptic plasticity. To see plasticity in action you can monitor single synaptic weights or groups of them using a [[WeightMonitor]] object. If you are only interested in let's say the mean weight change in one of the plastic [[Connection]] objects your defined (i.e. the mean weight between two neuronal populations in your simulation), you can use [[WeightStatsMonitor]]. You will find other Monitor classes in the Auryn ''src'' directory (see also the [[http://www.fzenke.net/auryn/doxygen/current/annotated.html|Class index]]; Monitor classes should be suffixes with ''Monitor'') or you can write your own for your specific needs. 
  
-In most cases Auryn [[Monitor]] objects will record data to files on disk on perform a minimum of online analysis. This can be cumbersome if you would for instance like to monitor the activity in your network and stop a simulation if the activity explodes or the network falls quiescent. To tackle this problem, Auryn has a specific class which allows minimalistic online monitoring. They are called [[Checker|Checkers]].+In most cases Auryn [[Monitor]] instances will individually record data to separate files on disk and perform a minimum of on-line analysis (here are some [[extensions|default extensions]] I am using for these output files). This can be cumbersome if you would for instance like to monitor the activity in your network and stop a simulation if the activity explodes or the network falls quiescent. To tackle this problem, Auryn has a specific class which allows minimalistic online monitoring. They are called [[Checker|Checkers]].
  
 ==== What's a Checker? ==== ==== What's a Checker? ====
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 Most objects in Auryn that have a direct network function can be subdivided into [[SpikingGroup]], [[Connection]] and [[Monitor]]. These three base classes implement the important interfaces that are called consecutively during the Auryn [[duty cycle]]. Instances of their descendants are registered and managed by the [[manual:System]] class, an instance of which is global to each Auryn [[simulation]]. Similarly global is the the [[manual:Logger]] class which takes care of logging output on the console and into log files. Most objects in Auryn that have a direct network function can be subdivided into [[SpikingGroup]], [[Connection]] and [[Monitor]]. These three base classes implement the important interfaces that are called consecutively during the Auryn [[duty cycle]]. Instances of their descendants are registered and managed by the [[manual:System]] class, an instance of which is global to each Auryn [[simulation]]. Similarly global is the the [[manual:Logger]] class which takes care of logging output on the console and into log files.
  
 +The most complete description of available classes and how to use them you will find in the doxygen generated class library. However, below are some entries for commonly used simulation components (which might, in some cases, not be too up to date any more)
 ===== SpikingGroups ===== ===== SpikingGroups =====
  
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     * [[manual:IF2Group]]     * [[manual:IF2Group]]
     * [[manual:AdExGroup]]     * [[manual:AdExGroup]]
 +    * [[manual:IzhikevichGroup]]
     * [[manual:AIFGroup]]     * [[manual:AIFGroup]]
     * [[manual:AIF2Group]]     * [[manual:AIF2Group]]
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   * [[manual:WeightMonitor]]   * [[manual:WeightMonitor]]
  
-===== IO File Formats ===== +Most monitor instances write their output to an individual file. Many file formats are human readable, while others are binary. See [[IO File Formats]] for a more complete overview of typical file formats. 
- +
-To minimize complicated IO operations Auryn dumps most of its monitor data directly into human readable text files that can be post processes using standard Linux command line software and visualized easily (e.g. with Gnuplot). +
- +
-  * [[manual:ras]] files +
-  * [[manual:spk]] binary raster files +
-  * [[manual:wmat]] files +
-  * [[manual:mem]] and other 1d time series files (such as ampa, gaba, nmda) +
-  * [[manual:syn]] file +
-  * [[manual:prate]] file and [[manual:pact]] file +
-  * [[manual:pat]] files +
-  * [[manual:stim]] stim files+
  
  
manual/start.txt · Last modified: 2018/02/07 08:58 by zenke