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SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties.
Predictive Coding Approximates Backprop along Arbitrary Computation Graphs
Recently, it has been shown that backprop in multilayer-perceptrons (MLPs) can be approximated using predictive coding, a biologically-plausible process theory of cortical computation which relies only on local and Hebbian updates.
Spyx: A Library for Just-In-Time Compiled Optimization of Spiking Neural Networks
As the role of artificial intelligence becomes increasingly pivotal in modern society, the efficient training and deployment of deep neural networks have emerged as critical areas of focus.
PeleNet: A Reservoir Computing Framework for Loihi
Such frameworks have emerged in the last years for traditional computers, but programming neuromorphic hardware is still a challenge.
PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
We also use PyCARL to analyze these SNNs for a state-of-the-art neuromorphic hardware and demonstrate a significant performance deviation from software-only simulations.
