Gemo Emanuele, Spiga Sabina, Brivio Stefano
CNR-IMM, Unit of Agrate Brianza, Agrate Brianza, Italy.
Front Neurosci. 2024 Jan 8;17:1270090. doi: 10.3389/fnins.2023.1270090. eCollection 2023.
Investigations in the field of spiking neural networks (SNNs) encompass diverse, yet overlapping, scientific disciplines. Examples range from purely neuroscientific investigations, researches on computational aspects of neuroscience, or applicative-oriented studies aiming to improve SNNs performance or to develop artificial hardware counterparts. However, the simulation of SNNs is a complex task that can not be adequately addressed with a single platform applicable to all scenarios. The optimization of a simulation environment to meet specific metrics often entails compromises in other aspects. This computational challenge has led to an apparent dichotomy of approaches, with model-driven algorithms dedicated to the detailed simulation of biological networks, and data-driven algorithms designed for efficient processing of large input datasets. Nevertheless, material scientists, device physicists, and neuromorphic engineers who develop new technologies for spiking neuromorphic hardware solutions would find benefit in a simulation environment that borrows aspects from both approaches, thus facilitating modeling, analysis, and training of prospective SNN systems. This manuscript explores the numerical challenges deriving from the simulation of spiking neural networks, and introduces SHIP, Spiking (neural network) Hardware In PyTorch, a numerical tool that supports the investigation and/or validation of materials, devices, small circuit blocks within SNN architectures. SHIP facilitates the algorithmic definition of the models for the components of a network, the monitoring of states and output of the modeled systems, and the training of the synaptic weights of the network, by way of user-defined unsupervised learning rules or supervised training techniques derived from conventional machine learning. SHIP offers a valuable tool for researchers and developers in the field of hardware-based spiking neural networks, enabling efficient simulation and validation of novel technologies.
脉冲神经网络（SNN）领域的研究涵盖了多样但相互重叠的科学学科。例子包括纯粹的神经科学研究、神经科学计算方面的研究，或旨在提高SNN性能或开发人工硬件对应物的面向应用的研究。然而，SNN的模拟是一项复杂的任务，无法通过适用于所有场景的单一平台充分解决。为满足特定指标而对模拟环境进行优化通常会在其他方面做出妥协。这种计算挑战导致了方法上明显的二分法，即模型驱动算法致力于生物网络的详细模拟，而数据驱动算法则设计用于高效处理大型输入数据集。尽管如此，为脉冲神经形态硬件解决方案开发新技术的材料科学家、器件物理学家和神经形态工程师将从借鉴这两种方法的模拟环境中受益，从而便于对未来的SNN系统进行建模、分析和训练。本文探讨了脉冲神经网络模拟带来的数值挑战，并介绍了SHIP，即PyTorch中的脉冲（神经网络）硬件，这是一种数值工具，可以支持对SNN架构中的材料、器件、小型电路模块进行研究和/或验证。SHIP通过用户定义的无监督学习规则或源自传统机器学习的监督训练技术，促进了网络组件模型的算法定义、对建模系统状态和输出的监测以及网络突触权重的训练。SHIP为基于硬件的脉冲神经网络领域的研究人员和开发人员提供了一个有价值的工具，能够对新技术进行高效模拟和验证。
