Parameter on TIDIGIT dataset

[qianhuiliu]

Hello! Could you please introduce how to set the parameters in "network.yaml" in TIDIGIT dataset? My settings are simulation: Ts: 1 tSample: 64 nTimeBins: 64 nSample: 100 neuron: type: SRMALPHA theta: 10 tauSr: 10.0 tauRef: 1.0 scaleRef: 2 # relative to theta tauRho: 1 # relative to theta #0.43429448190325176 scaleRho: 1 training: error: type: NumSpikes #ProbSpikes #NumSpikes probSlidingWin: 20 # only valid for ProbSpikes tgtSpikeRegion: {start: 0, stop: 64} # only valid for NumSpikes and ProbSpikes tgtSpikeCount: {true: 20, false: 5} # only valid for NumSpikes

But it only produces ~93.6% accuracy. Could you give me some advice?

Many thanks!

[bamsumit]

The performance on TIDIGIT depends on the spike encoding. What is the spike encoding you are using?

If you are using NTIDIGITS, the config and accuracy seems fine.

[qianhuiliu]

Thank you, Bam sumit. I conduct the experiment on TIDIGIT. My encoding is using MFCC+SOM, which was introduced in [Wu 2018]. The slayer network structure is set as 484-500-500-11, which also follows the paper said.

And I change the "nSample" in "network.yaml" to 12 and get an accuracy of 95.5%, which is still 4% lower than the paper said. I have no idea how to optimize it and hope you can give me some advice. Many thanks!

[bamsumit]

@qianhuiliu are you learning axonal delay as well?

[chenjiefighting]

Hello, I would like to ask how NTIDIGITS dataset is preprocessed. Could you please provide the corresponding code? Thank you.