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In spike-timing-dependent plasticity (STDP), synap-tic weights are modified according to the relative time difference between pre and post-synaptic spikes of spiking neural network (SNN). A triplet STDP model was proposed since this model can better take account of a series of spikes and thus more closely mimic the activity in biological neural systems. Circuit that can switch between different STDP rules was also introduced to improve the range of STDP applications. To apply the advantages of triplet STDP to various tasks, a mixed-signal triplet reconfigurable STDP circuit and its hardware prototype are proposed in this paper. The performance analysis of the STDP training algorithm is carried out with a hardware testbench as well as Pytorch-based SNN. This triplet STDP design achieves 3.28% and 3.63% higher accuracy than the pair STDP learning rule through datasets such as MNIST and CIFAR-10. Our design shows one of the best reconfigurability while keeping a relatively low energy per spike operation (SOP) through the performance comparison with the state of the arts.
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This content will become publicly available on January 1, 2027
Causal Spike Timing-Dependent Plasticity Prevents Assembly Fusion in Recurrent Networks
The organization of neurons into functionally related assemblies is a fundamental feature of cortical networks, yet our understanding of how these assemblies maintain distinct identities while sharing members remains limited. Here we analyze how spike timing-dependent plasticity (STDP) shapes the formation and stability of overlapping neuronal assemblies in recurrently coupled networks of spiking neuron models. Using numerical simulations and an associated mean-field theory, we demonstrate that the temporal structure of the STDP rule, specifically its degree of causality, critically determines whether assemblies that share neurons maintain segregation or merge together after training is completed. We find that causal STDP rules, where potentiation or depression occurs strictly when presynaptic spikes precede or follow postsynaptic spikes, allow assemblies to remain distinct even with substantial overlap in membership. This stability arises because causal STDP effectively cancels the symmetric correlations introduced by common inputs from shared neurons. In contrast, acausal STDP rules lead to assembly fusion when overlap exceeds a critical threshold, due to unchecked growth of common input correlations. Our results provide theoretical insight into how spike timing-dependent learning rules can support distributed representations where individual neurons participate in multiple assemblies while maintaining functional specificity.
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- Award ID(s):
- 2235451
- PAR ID:
- 10686933
- Publisher / Repository:
- American Physical Society
- Date Published:
- Journal Name:
- PRX Life
- Volume:
- 4
- Issue:
- 1
- ISSN:
- 2835-8279
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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