BindsNET: A machine learning-oriented spiking neural networks library in Python

Abstract

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 towards 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 top of the PyTorch deep neural networks library, enabling fast CPU and GPU computation for large spiking networks. The BindsNET framework can be adjusted to meet the needs of other existing computing and hardware environments, e.g., TensorFlow. We also provide an interface into the OpenAI gym library, allowing for training and evaluation of spiking networks on reinforcement learning problems. We argue that this package facilitates the use of spiking networks for large-scale machine learning experimentation, and show some simple examples of how we envision BindsNET can be used in practice. BindsNET code is available at https://github.com/Hananel-Hazan/bindsnet

References in corpus (2)

• Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks
• First-spike based visual categorization using reward-modulated STDP

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• ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories
• SoftSNN: Low-Cost Fault Tolerance for Spiking Neural Network Accelerators under Soft Errors
• Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignments
• SparkXD: A Framework for Resilient and Energy-Efficient Spiking Neural Network Inference using Approximate DRAM
• NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips
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• EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systems
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• RescueSNN: Enabling Reliable Executions on Spiking Neural Network Accelerators under Permanent Faults
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• Precise spiking motifs in neurobiological and neuromorphic data
• Unsupervised SFQ-Based Spiking Neural Network
• Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications
• Faster and Simpler SNN Simulation with Work Queues
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