Spike-timing-dependent plasticity is compatible with reciprocal neural connectivity
the verdict
SUPPORTED
the evidence backs this
confidence 77/100
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.
Evidence for · 4
A brain-constrained neural model of cognition and language with NEST: transitioning from the Felix framework.
2026 · cited by 1
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.
2026 · cited by 1
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.
2025 · cited by 0
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.
2026 · cited by 0
Paper [10] explicitly investigates spiking networks with recurrent connectivity, highlighting how intra-assembly recurrent connections support precise spike timing phenomena.