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PD #6
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PD #6
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Thanks for the CI stuff. I have no clue on how that stuff works! I d be very happy if you do it for the main SpikingneuralNetworks package! I kept the tests updated in One could also write tests for SNNUtils, but in that case one has to manage the dependency on SpikingNeuralNetworks, and even more than before, I have no clue how to do it! Thanks |
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…n/2024-12-07-01-23-10-026-04288858743 CompatHelper: bump compat for Colors to 0.13, (keep existing compat)
…/2024-12-07-01-23-08-085-03067453206 CompatHelper: add new compat entry for ProgressBars at version 1, (keep existing compat)
…/2024-12-07-01-23-06-323-03052093067 CompatHelper: add new compat entry for BenchmarkTools at version 1, (keep existing compat)
…/2024-12-07-01-23-04-290-00742901801 CompatHelper: add new compat entry for LoopVectorization at version 0.12, (keep existing compat)
…/2024-12-07-01-23-02-516-00447042253 CompatHelper: add new compat entry for Revise at version 3, (keep existing compat)
…/2024-12-07-01-23-00-552-03794009119 CompatHelper: add new compat entry for Parameters at version 0.12, (keep existing compat)
…/2024-12-07-01-22-58-788-01699314212 CompatHelper: add new compat entry for RollingFunctions at version 0.8, (keep existing compat)
…/2024-12-07-01-22-56-908-03654651017 CompatHelper: add new compat entry for TimerOutputs at version 0.5, (keep existing compat)
…/2024-12-07-01-22-54-968-03397221776 CompatHelper: add new compat entry for DrWatson at version 2, (keep existing compat)
…/2024-12-07-01-22-48-264-01099416947 CompatHelper: add new compat entry for StaticArrays at version 1, (keep existing compat)
get upstream changes
…n/2025-01-14-01-14-17-469-00004537629 CompatHelper: add new compat entry for Unitful at version 1, (keep existing compat)
…n/2025-01-14-01-14-12-020-04089253229 CompatHelper: bump compat for CairoMakie to 0.13, (keep existing compat)
Hi @aquaresima,
This is just a draft PR so its easier to see the code where the figures came from. I am using PD informed synaptic delay distributions, and non uniform synaptic weights (drawn from PD informed random distributions too).
The main thing I have to do now is figure out how to make the network in proportion to the input stimulus, expose the network to the different training stimulus for a long time, and then try to run the network in inference mode, and try to show evidence of recognition.