Topic: surrogate gradients

Invasive cortical brain-machine interfaces (BMIs) can significantly improve life quality for motor-impaired patients. However, externally mounted BMIs carry the risk of wound infection, necessitating fully implanted systems with integrated signal processing. Such systems must ensure reliable and accurate data processing while meeting tight latency and energy constraints. To meet these processing requirements, spiking neural networks (SNNs) seem an attractive solution due to their minimal preprocessing requirements of cortical spike trains (CSTs) and their suitability for ultra-low-power, low-

Spiking Neural Networks (SNNs) promise energy efficiency and offer a more brain-like alternative to artificial neural networks. A major challenge to unleashing their potential, however, is to make them trainable on challenging tasks that require credit assignment across long time horizons. Gradient-based learning, which has driven much of the recent success in ANNs, is hampered in spiking networks by their discrete spiking interactions, which make the error landscape non-differentiable. A recent approach to this challenge is to replace the spike interaction with a surrogate gradient in the bac