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Researchers developed a recurrent spiking neural network of 1,024 neurons with 41 heterogeneous synaptic delays per synapse that can store and recall 16 precise temporal activity patterns lasting 1,000 time steps each. The network achieves a mean F1 score of 0.80 for pattern retrieval using surrogate-gradient backpropagation training, and demonstrates robust performance even with 25% noise corruption of the trigger signal (F1 score of 0.53). The system represents stored patterns as polychronous chains of overlapping spiking motifs and exhibits attractor-like dynamics similar to hippocampal pattern completion.
Why it matters
This work addresses a fundamental challenge in neuromorphic computing by demonstrating that heterogeneous synaptic delays enable efficient working memory in spiking neural networks. The approach has direct implications for low-power neuromorphic hardware deployment at the edge, potentially enabling more brain-like temporal processing in energy-constrained applications.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: Working memory — the ability to store and recall precise temporal patterns of activity — remains an open challenge for spiking neural networks (SNNs). A recurrent SNN of $N=1024$ neurons is proposed, in which each synapse is equipped with $D = 41$ heterogeneous delays, parameterised as a weight tensor $mathbf{W} in mathbb{R}^{N times N times D}$ and trained end-to-end with surrogate-gradient backpropagation through time. Each stored motif is represented as a sequential chain of overlapping Spiking Motifs: contiguous context windows of length $D$ that uniquely predict the activity at the next time step. A closed-form Hebbian initialisation, derived by deconvolving the LIF membrane response and targeting a membrane potential value above the firing threshold $vartheta$, already achieves high $F_1$ scores before any gradient step on a benchmark of $M=16$ motifs of duration $T=1000$ steps. With learning, the network reaches a mean $F_1$ score of $0.80$ over the motifs, and retrieval degrades gracefully when the trigger window is corrupted, with an $F_1$ score of $0.53$ at $25%$ bit-flip noise. The network shows attractor-like retrieval dynamics consistent with hippocampal motif completion and with low-dimensional collective dynamics of population activity on manifolds. These results demonstrate that heterogeneous synaptic delays are an efficient and scalable substrate for working memory in SNNs, with direct implications for neuromorphic edge deployment.