Bio-inspired minimal hardware implementation of spike-time dependent plasticity for intelligent spiking neural networks
Abstract
We introduce a hardware circuit model that implements spike-time dependent plasticity (STDP) to endow spiking neural networks with learning capabilities. Our circuit model is characterized as both minimal and bio-inspired, due to its simplicity and to a novel active dendrite compartment that mimics the synaptic potentiation mechanism. The active dendrite consists of an integrate and-fire stage which produces a train of pulses whose number is inversely related to the timing between pre- and post-synaptic spikes. The dendrite pulses modulate in a reliable manner the synaptic efficacy (conductance) that we implemented with a digipot, considered as an idealized non-volatile memristor. We demonstrate the behavior of the circuit by implementing a minimal spiking neuron model of associative learning by STDP, which is analog to the classic conditioning experiment of Pavlov’s dog.
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BibTeX
@article{dhollande2026bioinspired,
title = {{Bio-inspired minimal hardware implementation of spike-time dependent plasticity for intelligent spiking neural networks}},
author = {Adrien D’hollande and Olivier Schneegans and Kang Wang and Marcelo Rozenberg},
journal = {Neuromorphic Computing and Engineering},
year = {2026},
month = aug,
doi = {10.1088/2634-4386/ae943a},
url = {https://centralesupelec.hal.science/hal-05765227v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Bio-inspired minimal hardware implementation of spike-time dependent plasticity for intelligent spiking neural networks
AU - Adrien D’hollande
AU - Olivier Schneegans
AU - Kang Wang
AU - Marcelo Rozenberg
PY - 2026
DA - 2026/08/24
JO - Neuromorphic Computing and Engineering
DO - 10.1088/2634-4386/ae943a
UR - https://centralesupelec.hal.science/hal-05765227v1
AB - We introduce a hardware circuit model that implements spike-time dependent plasticity (STDP) to endow spiking neural networks with learning capabilities. Our circuit model is characterized as both minimal and bio-inspired, due to its simplicity and to a novel active dendrite compartment that mimics the synaptic potentiation mechanism. The active dendrite consists of an integrate and-fire stage which produces a train of pulses whose number is inversely related to the timing between pre- and post-synaptic spikes. The dendrite pulses modulate in a reliable manner the synaptic efficacy (conductance) that we implemented with a digipot, considered as an idealized non-volatile memristor. We demonstrate the behavior of the circuit by implementing a minimal spiking neuron model of associative learning by STDP, which is analog to the classic conditioning experiment of Pavlov’s dog.
SN - 2634-4386
ER -