This is the code for the paper Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition, available here.

• numpy
• pandas
• matplotlib
• scipy
• scikit_learn
• scikit_plot
• nni
• seaborn
• Brian2

Requirements can be installed with :

pip3 install -r requirements.txt

Please note that the requirement file might use outdated dependencies. To get latest versions :

pip3 install numpy pandas matplotlib scipy scikit_learn scikit_plot nni seaborn Brian2

Finally, make sure the source of the project is in the Python path.

• On Linux : export PYTHONPATH="$PWD/src"
• On Windows : set PYTHONPATH="$PWD/src"

You can also modify it permanently : see here.

Different datasets for Electromyography (EMG) are provided in the folder datasets/ :

• Roshambo (classes: rock, paper, scissor)
• 5_class (classes: pinky, elle, yo, index, thumb)
• Pinch (classes: Pinch1, Pinch2, Pinch3, Pinch4)

This is the first pipeline described in the paper. To execute it :

python3 src/evaluate_encoder.py [options]

This is the second pipeline described in the paper. To execute it :

python3 src/evaluate_reservoir.py [options] 

All the parameters are listed and detailed in the file src/args_emg.py. They can be specified as follow :

python3 <<script>> --dataset="5_class" --learning_algorithm="critical" --cbf=1

Pipelines can also be used in Python scripts :

import random
import numpy as np
from evaluate_encoder import *
from evaluate_reservoir import *
from args_emg import args as my_args

# Get default arguments 
# Can be modified (eg. args.adaptiveProb = 1)
args = my_args()

# Fix the seed of all random number generator
seed = int(args.seed)
random.seed(seed)
np.random.seed(seed)

# Spike encoding & Evaluation baseline
svm_score_enc,firing_rate_enc,svm_score_baseline_enc = evaluate_encoder(args)

# Reservoir 
lda_score,lda_score_input,svm_linear_score,svm_linear_score_input,svm_score,svm_score_input,firing_rate,nbsynapses,nbneurons = evaluate_reservoir(args)

Experiments presented in the paper can be reproduced by executing the files in the folder experiments/. These files are for parameter exploration : a grid search algorithm is used to test different combinations of parameters. Files can be executed with :

python3 -m experiments.experiment_exploration_v10

Default parameters reproduce the best results presented in the paper. Note that a reservoir with 320 neurons is used for a 3 class problem, and 2048 neurons for a 5 class problem. Hence, for 5_class dataset, macrocolumn shape must be modified to [4,4,4].

University of Sherbrooke. NEuro COmputational & Intelligent Signal Processing Research Group (NECOTIS)