Reward modulated STDP (Legenstein et al. 2008)

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Accession:116837
"... This article provides tools for an analytic treatment of reward-modulated STDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning effect. These analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but also temporal firing patterns of presynaptic neurons. They also can learn to respond to specific presynaptic firing patterns with particular spike patterns. Finally, the resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP. This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker. In this experiment monkeys were rewarded for increasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment problem. ... In addition our model demonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics."
Reference:
1 . Legenstein R, Pecevski D, Maass W (2008) A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback. PLoS Comput Biol 4:e1000180 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Python; PCSIM;
Model Concept(s): Pattern Recognition; Spatio-temporal Activity Patterns; Reinforcement Learning; STDP; Biofeedback; Reward-modulated STDP;
Implementer(s):
This directory contains the scripts for computer simulation 3 from 

	Legenstein R, Pecevski D, Maass W 2008 A Learning Theory 
    for Reward-Modulated Spike-Timing-Dependent Plasticity with 
    Application to Biofeedback. PLoS Computational Biology 4(10): e1000180, Oct, 2008 
    
The produced result is figure 8.

To create these figures you need to:

1. The simulation performs 6 runs of an experiment, possibly on different machines 
   in parallel. To change the names of the machines where the experiments should run, edit 
   the start_simulation.py file.  
   
2. Execute:

    start_simulation.py
    
    This is an executable file, you don't need to run 'python start_simulation.py'.
    The program will create a new directory where the output files will reside. 
    Wait until the simulation finishes. You can monitor how simulations progress 
    in the sim[0-5].out files in the newly created directory.

4. Then, to create figure 8 run:
   
      ipython -pylab create_table_values.py