Codes and data regarding the article: "Neuromorphic Photonic Circuits with Nonlinear Dynamics and Memory for Time Sequence Classification".

This upload complements the paper "Neuromorphic Photonic Circuits with Nonlinear Dynamics and Memory for Time Sequence Classification".

Regarding Section 3.2 " Classification using Single Pixel Representations with Optical Memory":

• "ML_singlePix2.py" employs the output traces (measured at the output ports of the MRR network) to train and test a linear machine learning (ML) model (logistic regression) employing as features a single time sample for each output trace.
• "ML_singlePix2_expBaseline.py" and "ML_singlePix2_teoBaseline.py" are similar to "ML_singlePix2.py" and provide respectively the experimental baseline results (based on acquistion of optical data from the chip, without the processing from the MRR network, i.e. out of resonance) and the theoretical baseline results (where the linear classifier is directly applied to the original datasets, without any optical/analog convertion).
• "ML_singlePix2_increasingNumberOfRpepr" is also similar to "ML_singlePix2.py", with the difference that, given a time sample, it trains and evaluates the linear ML model  considering an increasing number of output representations (i.e., an increasing number of features).
• "ResultsAnalysis_singlePix2.py" analyzes the results from the previous codes, and produces the plots presented on the paper.

Regarding Section "5.2.1. Data Acquisition":

• "main_parsing.py" and "parsing.py" parse the measured raw data and save them into a series of flattened images.

Regarding Section "5.4.1. Linear Classifier (LC)":

• "MNIST_logistic_regression.py" performs the logistic regression on the original (non photonics processed) dataset.
• "MRR_singleport.py" applies the logistic regression to a single photonic representation.
• "MRR_Nports.py" applies it to N representations coming from different ports.

Regarding Section "5.4.2. Multilayer ANN in Software":

•  "MNIST_multi_layers.py" trains and test a ANN with 1 hidden layer on the original (non photonics processed) dataset

The raw experimental data (i.e. the output traces of the MRR network) can be obtained from the authors by reasonable request.