  In the last decade, Artificial Intelligence (AI) models have rapidly
integrated into production pipelines propelled by their excellent modeling
performance. However, the development of these models has not been matched by
advancements in algorithms ensuring their safety, failing to guarantee robust
behavior against Out-of-Distribution (OoD) inputs outside their learning
domain. Furthermore, there is a growing concern with the sustainability of AI
models and their required energy consumption in both training and inference
phases. To mitigate these issues, this work explores the use of the
Forward-Forward Algorithm (FFA), a biologically plausible alternative to
Backpropagation, adapted to the spiking domain to enhance the overall energy
efficiency of the model. By capitalizing on the highly expressive topology
emerging from the latent space of models trained with FFA, we develop a novel
FF-SCP algorithm for OoD Detection. Our approach measures the likelihood of a
sample belonging to the in-distribution (ID) data by using the distance from
the latent representation of samples to class-representative manifolds.
Additionally, to provide deeper insights into our OoD pipeline, we propose a
gradient-free attribution technique that highlights the features of a sample
pushing it away from the distribution of any class. Multiple experiments using
our spiking FFA adaptation demonstrate that the achieved accuracy levels are
comparable to those seen in analog networks trained via back-propagation.
Furthermore, OoD detection experiments on multiple datasets prove that FF-SCP
outperforms avant-garde OoD detectors within the spiking domain in terms of
several metrics used in this area. We also present a qualitative analysis of
our explainability technique, exposing the precision by which the method
detects OoD features, such as embedded artifacts or missing regions.
