SpikeCoding

This package features MATLAB, Python and ROS implementations of spike encoding and decoding algorithms. So far, the following algorithms are available:

• Population coding:
  • [Matlab][Python][ROS] Gaussian Receptor Fields
  • [Matlab][Python] Bohté et al. (2002) GRF
• Temporal coding:
  • [Matlab][Python][ROS] Temporal contrast
  • [Matlab][Python][ROS] Step-forward
  • [Matlab][Python][ROS] Moving window
• Rate coding:
  • [Matlab][Python] Hough spike
  • [Matlab][Python] Threshold Hough
  • [Matlab][Python] Ben's spike

Supporting paper: https://arxiv.org/abs/2103.02751

TODOs:

• Finish integration of rate coding to ROS node
• Add Population schemes to ROS node
• Implement the following schemes: non-linear GRF, ROC, Poisson, LPC.
• Upload review table & add references on README
• Generate tests for MNIST dataset
• Generate tests for IMU data from the Obstacle Detection & Avoidance Dataset
• Setup framework for parameters optimisation (DEAP, PyEvolve, PyBrain)
• Include to PySNN (?)

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This work is part of the Comp4Drones project and has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No. 826610. The JU receives support from the European Union's Horizon 2020 research and innovation program and Spain, Austria, Belgium, Czech Republic, France, Italy, Latvia, Netherlands.

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1. MATLAB Implementation

The MATLAB codes are available in SpikeCodingMatlab. Within this folder, simply execute the SpikeCoding.m file to have an overview of the results (encoding/decoding) of the implemented schemes tested on 3 different functions.

2. Python Impmementation

The Python scripts are available in SpikeCodingPython. Within this folder, simply execute the main.py file to have an overview of the results (encoding/decoding) of the implemented schemes tested on 3 different functions.

Here you can find the results:

3. ROS Package

The ROS folder contains the following packages:

• spyke_coding: creates a ROS node to generate spikes based on input data and the selected coding scheme.
• spyke_coding_schemes: includes all the encoding and decoding functions used in the spyke_coding package.
• spyke_msgs: defines the custom spiking messages to be published.

Installing

Install ROS Noetic (for Ubuntu Focal 20.04 LTS) using the following tutorial:
http://wiki.ros.org/noetic/Installation/Ubuntu

Install catkin python tools:

sudo apt-get install python3-catkin-tools 

Install additionnal dependencies:

sudo apt install python3-catkin-lint python3-pip
pip3 install osrf-pycommon==0.1.9
pip3 install empy

Then create your workspace:

cd~ 
mkdir -p catkin_ws/src

Clone this repository, and copy the ROS packages to your ~/catkin_ws/src/ folder. Then compile your project:

cd /catkin_ws/
catkin_make
source ~/catkin_ws/devel/setup.bash

Testing

The spyke_coding package includes a set of launch files for each type of encoding. In its present form, the package defines a fake signal with a given sampling period dt and a signal duration T_max (in seconds). Both temporal and rate coding schemes use a buffer of size 1/dt.

Once the ROS node is launched, it automatically stops after T_max seconds, or if the user press ctrl+c. The output data is stored here: ~/.ros/encoding.bag

Example on the temporal-contrast encoding:

cd /catkin_ws/
source ~/catkin_ws/devel/setup.bash
roslaunch spyke_coding encode_temporal_contrast.launch

After completion, you can plot the results using the dedicated Python script:

cd <path_to_this_repository>/SpikeCodingPython/
python process_rosbags.py ~/.ros/encoding.bag

Here is an example result:

List of the available launch files:

• encode_temporal_contrast.launch
• encode_step_forward.launch
• encode_moving_window.launch
• encode_hough.launch
• encode_threshold_hough.launch
• encode_bsa.launch
• encode_gaussian_fields.launch

Application to images encoding

TODO:

• add other methods
• generate videos
• plot error between original and reconstruction
• include in ROS
• test on MNIST

Based on video available in the ODA Dataset: https://github.com/JuSquare/ODA_Dataset/tree/master/dataset

Step Forward (SF) Temporal Coding with Threshold = 1

Step Forward (SF) Temporal Coding with Threshold = 5

Step Forward (SF) Temporal Coding with Threshold = 10

Step Forward (SF) Temporal Coding with Threshold = 20