Fig. 1. Dendritic RRAM (DenRAM) concept.

a Depiction of a biological Neuron, receiving input spikes through multiple dendritic branches. The packet of neurotransmitters travels across the dendritic branch before reaching the neuron’s soma, where it is integrated. b Scheme of the Dendritic Network, formed by several Dendritic circuits grouped into Dendritic branches macro-circuits, highlighted by different colors. The branches' outputs are integrated into a Leaky-Integrate-and-Fire Neuron. c State-of-the-art results on the SHD dataset as a function of the number of parameters. Delay-based networks show higher accuracy and lower memory footprint compared to recurrent architectures (SRNN: recurrent spiking neural networks, A-SRNN: augmented-SRNN). d Recurrent Neural Networks are hard to train and yield low performance. Dendritic SNNs are feed-forward models that perform better than RNNs despite reduced Memory Footprint and Power Consumption. e Applications for the Dendritic SNN include Key-Word-Spotting and Heartbeat anomaly detection, and possibly many other sequence processing tasks. Illustrations in a, d, and e were created with Inkscape.