Based on BindsNET – a spiking neural network simulation library geared towards the development of biologically inspired algorithms for machine learning – this Bachelor-Project tries to replace the forward-step of the "supervised_mnist"-example (in network.run(...) in bindsnet_qa/network/network.py) with the usage of a D-Wave Quantum Annealer or a simulator thereof using D-Waves qbsolv Package.

Documentation for the BindsNET-package can be found here.

Requirements

• Python 3.6
• requirements.txt

Setting things up

Using pip

To build the bindsnet_qa package from source, clone the GitHub repository, change directory to the top level of this project, and issue

pip install .

Or, to install in editable mode (allows modification of package without re-installing):

pip install -e .

Getting started

To run a near-replication of the SNN from this paper, issue

cd examples/mnist
python supervised_mnist.py --time 5 --update_interval 10 --n_train 250 --n_neurons 10

Caveat: Runs a little slow.

There are a number of optional command-line arguments which can be passed in, including --plot (displays useful monitoring figures), --time [int] (determines the number of forward-timesteps per MNIST-Datum), --n_train [int] (total number of training iterations), --update_interval [int] (determines how often the current accuracy is shown), and more. Run the script with the --help or -h flag for more information.

If you want to make plots and save them to a certain directory, use the arguments --plot -- directory [the path to the directory you want to save them in].

Running the tests

Issue the following to run the tests:

python -m pytest test/

Some tests will fail if Open AI gym is not installed on your machine.

Background

TODO

Benchmarking

As of now, it runs slower compared to the original BindsNET-version. Reasons for this are being investigated.

Citation

As I am using BindsNET, I'm hereby citing article:

@ARTICLE{10.3389/fninf.2018.00089,
	AUTHOR={Hazan, Hananel and Saunders, Daniel J. and Khan, Hassaan and Patel, Devdhar and Sanghavi, Darpan T. and Siegelmann, Hava T. and Kozma, Robert},   
	TITLE={BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python},      
	JOURNAL={Frontiers in Neuroinformatics},      
	VOLUME={12},      
	PAGES={89},     
	YEAR={2018}, 
	URL={https://www.frontiersin.org/article/10.3389/fninf.2018.00089},       
	DOI={10.3389/fninf.2018.00089},      
	ISSN={1662-5196},
}

Contributors

• Daniëlle Schuman

To BindsNET:

• Daniel Saunders (email)
• Hananel Hazan (email)
• Darpan Sanghavi (email)
• Hassaan Khan (email)
• Devdhar Patel (email)