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We have developed a user-friendly computational platform for the simulation of SNN where to include models of novel devices and technologies for their functional validation.

The platform is named SHIP: Spiking neural network Hardware in PyTorch

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.


The work is published with open access in Frontiers in Neuroscience.

For any query, please contact Stefano Brivio