AR
A. Radu
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Recent interest in biologically plausible alternatives to backpropagation has renewed attention on Spiking Neural Networks and the Forward-Forward algorithm, where learning is driven by local layer-wise goodness functions rather than global error gradients. In most Forward-Forward learning implementations of Spiking Neural Networks, goodness is defined as spike-count activity, leaving temporal properties of neural activity unused. This work investigates whether temporal spike stability, measured using the inter-spike interval coefficient of variation (ISI-CV), can improve Forward-Forward learning in fully connected leaky integrate-and-fire spiking neural networks. Using MNIST as a benchmark, we evaluate several ISI-CV-based extensions, including direct temporal penalties, contrastive gap losses, plasticity based approaches, and candidate scoring. Directly optimizing for temporal regularity conflicts with the Forward-Forward goodness margin and destabilizes training. The use of ISI-CV as a plasticity control signal, that reduces updates to temporally stable neurons, can be used to fine-tune the model. ISI-CV-based candidate scoring performs above chance, indicating that spike timing contains class-related information, but remains weaker than standard goodness-based classification.
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Recent interest in biologically plausible alternatives to backpropagation has renewed attention on Spiking Neural Networks and the Forward-Forward algorithm, where learning is driven by local layer-wise goodness functions rather than global error gradients. In most Forward-Forward learning implementations of Spiking Neural Networks, goodness is defined as spike-count activity, leaving temporal properties of neural activity unused. This work investigates whether temporal spike stability, measured using the inter-spike interval coefficient of variation (ISI-CV), can improve Forward-Forward learning in fully connected leaky integrate-and-fire spiking neural networks. Using MNIST as a benchmark, we evaluate several ISI-CV-based extensions, including direct temporal penalties, contrastive gap losses, plasticity based approaches, and candidate scoring. Directly optimizing for temporal regularity conflicts with the Forward-Forward goodness margin and destabilizes training. The use of ISI-CV as a plasticity control signal, that reduces updates to temporally stable neurons, can be used to fine-tune the model. ISI-CV-based candidate scoring performs above chance, indicating that spike timing contains class-related information, but remains weaker than standard goodness-based classification.