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

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. Existing software frameworks support a wide range of neural functionality, software abstraction levels, and hardware...

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Bibliographic Details
Published in:Frontiers in neuroinformatics 2018-12, Vol.12, p.89-89
Main Authors: Hazan, Hananel, Saunders, Daniel J, Khan, Hassaan, Patel, Devdhar, Sanghavi, Darpan T, Siegelmann, Hava T, Kozma, Robert
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Back propagation
Computer applications
Computer programs
Datasets
Deep learning
Firing pattern
GPU-computing
Learning algorithms
Libraries
Machine learning
Neural networks
Neuroinformatics
Neurons
Neurosciences
Ordinary differential equations
python (programming language)
PyTorch
Realism
Reinforcement
reinforcement learning (RL)
Software
Software packages
spiking Network
Teaching methods
Trends
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Summary: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. Existing software frameworks support a wide range of neural functionality, software abstraction levels, and hardware devices, yet are typically not suitable for rapid prototyping or application to problems in the domain of machine learning. In this paper, we describe a new Python package for the simulation of spiking neural networks, specifically geared toward machine learning and reinforcement learning. Our software, called BindsNET, enables rapid building and simulation of spiking networks and features user-friendly, concise syntax. 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. We provide an interface with the OpenAI gym library, allowing for training and evaluation of spiking networks on reinforcement learning environments. We argue that this package facilitates the use of spiking networks for large-scale machine learning problems and show some simple examples by using BindsNET in practice.
ISSN:1662-5196
1662-5196
DOI:10.3389/fninf.2018.00089