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
翻译:神经网络模拟软件的开发是建立神经系统模型和开发生物启发的算法的关键组成部分。现有的软件框架支持广泛的神经功能、软件抽象水平和硬件设备,但通常不适合机器学习领域的各种问题的快速原型或应用。在本文件中,我们描述了用于模拟神经网络模拟的一个新的Python软件包,具体针对机器学习和强化学习。我们的软件称为BindsNET,能够快速建立和模拟神经网络和功能方便用户、简洁的合成法。BindsNET建在PyTocher深层神经网络图书馆的顶部,为大型喷射网络提供快速的CPU和GPU计算功能。BindsNET框架可以调整以满足其他现有计算机和硬件环境的需求,例如TensorFlow。我们还提供了OpenAI健身图书馆的接口,允许对强化学习问题的系统进行训练和评价。我们说,这个软件包便于在PyToch 深神经网络上使用快速的CPU和GPU计算系统。我们在Binstal数据库中可以使用普通的Sninet网络,用来在数据库中进行大规模实验。