Overview of code for the paper 'long- and short term history effects in a spiking network model of statistical learning'.
All code is written in MATLAB. If something is unclear, please feel free to contact me at firstname.lastname@example.org.
createUniform.m(the uniform sampler network)
createReadOutRNN.m(the sensory network)
compute_error.mtracks error during learning, plastic weights are saved in data folder)
compute_weight_corrs.m(suppl fig 3.A)
test_setup.m. Once you have trained the model, and recorded the plastic weights, you can then compute the slope of the psychometric curve with learning, or the short-term effect or the weight correlations. This data is not included because it is a few 100MB large.
target_distr2.mat: hardcoded target distribution (see figure 2.B)
wRE10_50_100_small_v3_100.mat: example plastic weights after 500 samples given to sensory network