Neuromorphic CMOS Circuits implementing a Novel Neural Segmentation Model based on Symmetric STDP Learning

We designed a simple neural segmentation model that is suitable for analog circuit implementation. The model consists of excitable neural oscillators and adaptive synapses, where the learning is governed by a symmetric spike-timing dependent plasticity (STDP). We numerically demonstrate basic operations of the proposed model as well as fundamental circuit operations using a simulation program with integrated circuit emphasis (SPICE).