Event-based learning of delays in SNNs

Event-based learning of delays in SNNs

In a collaboration between Melika Payvand's EIS Lab and Mihai Petrovici's NeuroTMA group, we've been developing a fully event-driven training algorithm for learning delays and weights in SNNs. In the established Fast&Deep algorithm, spike times are treated as the quantity central for information propagation. From this perspective, learning of delays appears naturally and can profit from the efficiency of event-based formalisms over time-stepped algorithms, while maintaining mathematical exactness.

In this workshop we want to present our findings on the efficacy of delays, show how delay-learning appears natively in an event-based formalism, apply our method to datasets of other participants to test its applicability and hope to implement our work on available hardware.


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Day Time Location
Thu, 02.05.2024 14:00 - 16:00 Disco Room


Julian Goeltz
Jimmy Weber


Muhammad Aitsam
Andreas Andreou
Sirine Arfa
Paolo Gibertini
Julian Goeltz
Jesse Hagenaars
Sanja Karilanova
James Knight
Antony N'dri
Eleni Nisioti
Thomas Nowotny
Andrea Ortone
Jimmy Weber