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the claim

Spike-timing-dependent plasticity is compatible with reciprocal neural connectivity

the verdict
SUPPORTED
the evidence backs this
Recorded sources
4 sources for · 0 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

Multiple computational neuroscience studies successfully incorporate spike-timing-dependent plasticity (STDP) within recurrently connected spiking neural network models, demonstrating that STDP is fully compatible with reciprocal neural connectivity.

The analysis

The retrieved literature consistently shows that spike-timing-dependent plasticity (STDP) is successfully implemented and studied in spiking neural network models featuring recurrent and reciprocal connectivity (e.g., papers 0, 1, 6, and 10). There are no papers contradicting this compatibility.

Evidence for · 4
A brain-constrained neural model of cognition and language with NEST: transitioning from the Felix framework.
Recorded source metadata

Carriere M, Dobler F, Plesser HE, Feledyn A, Tomasello R, Wennekers T, Pulvermüller F. A brain-constrained neural model of cognition and language with NEST: transitioning from the Felix framework.. 2026. https://doi.org/10.1007/s11571-026-10415-5

Paper [0] models brain networks using spiking neurons with recurrent connections and STDP, demonstrating compatibility with complex anatomical connectivity.

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More for · 3
Plastic Arbor: A modern simulation framework for synaptic plasticity-From single synapses to networks of morphological neurons.
Recorded source metadata

Luboeinski J, Schmitt S, Shafiee S, Hater T, Bösch F, Tetzlaff C. Plastic Arbor: A modern simulation framework for synaptic plasticity-From single synapses to networks of morphological neurons.. 2026. https://doi.org/10.1371/journal.pcbi.1013926

Paper [1] demonstrates that spike-driven plasticity rules like STDP can be successfully implemented in large-scale recurrent networks of biological neurons.

Emergence of sparse coding, balance and decorrelation from a biologically-grounded spiking neural network model of learning in the primary visual cortex.
Recorded source metadata

Ruslim MA, Spencer MJ, Hogendoorn H, Meffin H, Lian Y, Burkitt AN. Emergence of sparse coding, balance and decorrelation from a biologically-grounded spiking neural network model of learning in the primary visual cortex.. 2025. https://doi.org/10.1371/journal.pcbi.1013644

Paper [6] shows that recurrent spiking neural networks utilizing STDP rules naturally develop balanced, decorrelated states with robust recurrent connectivity.

Conditions for replay of neuronal assemblies.
Recorded source metadata

Cano G, Kempter R. Conditions for replay of neuronal assemblies.. 2026. https://doi.org/10.1371/journal.pcbi.1013844

Paper [10] explicitly investigates spiking networks with recurrent connectivity, highlighting how intra-assembly recurrent connections support precise spike timing phenomena.

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first checked01 Aug 2026
judged → SUPPORTED · 7701 Aug 2026
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