BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python

@article{Hazan2018BindsNETAM,
  title={BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python},
  author={Hananel Hazan and Daniel J. Saunders and Hassaan Khan and Devdhar Patel and Darpan T. Sanghavi and Hava T. Siegelmann and Robert Thijs Kozma},
  journal={Frontiers in Neuroinformatics},
  year={2018},
  volume={12}
}
The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithms. [] Key Method BindsNET is built on the PyTorch deep neural networks library, facilitating the implementation of spiking neural networks on fast CPU and GPU computational platforms. Moreover, the BindsNET framework can be adjusted to utilize other existing computing and hardware backends; e.g., TensorFlow and SpiNNaker.
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104 Citations
Highly Influential Citations
7
Background Citations
17
Methods Citations
38

104 Citations

Exploring the Connection Between Binary and Spiking Neural Networks
TLDR
It is shown that training Spiking Neural Networks in the extreme quantization regime results in near full precision accuracies on large-scale datasets like CIFAR-100 and ImageNet.
Spiking Neural Networks and Their Applications: A Review
TLDR
A comprehensive review of theories of biological neurons, which have been studied in neuroscience, is given and existing spiking neural network applications in computer vision and robotics domains are covered.
Direct Training for Spiking Neural Networks: Faster, Larger, Better
TLDR
This work proposes a neuron normalization technique to adjust the neural selectivity and develops a direct learning algorithm for deep SNNs and presents a Pytorch-based implementation method towards the training of large-scale Snns.
Neuromorphic Processing and Sensing: Evolutionary Progression of AI to Spiking
TLDR
The theoretical workings of neuromorphic technologies based on spikes are explained, the state-of-art in hardware processors, software platforms and neuromorphic sensing devices are overviewed, and a progression path is paved for current machine learning specialists to update their skillset.
Minibatch Processing in Spiking Neural Networks
TLDR
To the knowledge, this is the first general-purpose implementation of mini-batch processing in a spiking neural networks simulator, which works with arbitrary neuron and synapse models and shows the effectiveness of large batch sizes in two SNN application domains.
BioLCNet: Reward-modulated Locally Connected Spiking Neural Networks
TLDR
This work proposes a reward-modulated locally connected spiking neural network, BioLCNet, for visual learning tasks and assesses the robustness of the rewarding mechanism to varying target responses in a classical conditioning experiment.
SPAIC: A Spike-based Artificial Intelligence Computing Framework
TLDR
A Python based spiking neural network (SNN) simulation and training framework, aka SPAIC, that aims to support brain-inspired model and algorithm researches integrated with features from both deep learning and neuroscience.
Benchmarking the performance of neuromorphic and spiking neural network simulators
PymoNNto: A Flexible Modular Toolbox for Designing Brain-Inspired Neural Networks
TLDR
The Python Modular Neural Network Toolbox (PymoNNto) provides a versatile and adaptable Python-based framework to develop and investigate brain-inspired neural networks and comes with convenient high level behaviour modules, allowing differential equation-based implementations similar to Brian2.
Learning from Sparse and Delayed Rewards with a Multilayer Spiking Neural Network
TLDR
The proposed architecture has four distinct layers and addresses the limitation of previous models in terms of scalability with input dimensions, and outperforms Q-learning on a task with six-dimensional observation space.
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