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This paper presents an extension of the BrainScaleS accelerated analogneuromorphic hardware model. The scalable neuromorphic architecture is extendedby the support for multi-compartment models and non-linear dendrites. Thesefeatures are part of a \SI{65}{\nano\meter} prototype ASIC. It allows toemulate different spike types observed in cortical pyramidal neurons: NMDAplateau potentials, calcium and sodium spikes. By replicating some of thestructures of these cells, they can be configured to perform coincidencedetection within a single neuron. Built-in plasticity mechanisms can modify notonly the synaptic weights, but also the dendritic synaptic composition toefficiently train large multi-compartment neurons. Transistor-level simulationsdemonstrate the functionality of the analog implementation and illustrateanalogies to biological measurements.

An Accelerated Analog Neuromorphic Hardware System Emulating NMDA- and Calcium-Based Non-Linear Dendrites