  Magnetic skyrmions, as scalable and non-volatile spin textures, can
dynamically interact with fields and currents, making them promising for
unconventional computing. This paper presents a neuromorphic device based on
skyrmion manipulation chambers to implement spike-timing-dependent plasticity
(STDP), a mechanism for unsupervised learning in brain-inspired computing. STDP
adjusts synaptic weights based on the timing of pre- and post-synaptic spikes.
The proposed three-chamber design encodes synaptic weight in the number of
skyrmions in the center chamber, with left and right chambers for pre-synaptic
and post-synaptic spikes. Micromagnetic simulations demonstrate that the timing
between applied currents across the chambers controls the final skyrmion count
(weight). The device exhibits adaptability and learning capabilities by
manipulating chamber parameters, mimicking Hebbian and dendritic location-based
plasticity. The device's ability to maintain state post-write highlights its
potential for advancing adaptable neuromorphic devices.
