Anthony Joseph Kenyon

arXiv2023

Artificial Dendritic Computation: The case for dendrites in neuromorphic circuits

Bio-inspired computing has focused on neuron and synapses with great success. However, the connections between these, the dendrites, also play an important role. In this paper, we investigate the motivation for replicating dendritic computation and present a framework to guide future attempts in their construction. The framework identifies key properties of the dendrites and presents and example of dendritic computation in the task of sound localisation. We evaluate the impact of dendrites on an BiLSTM neural network's performance, finding that dendrite pre-processing reduce the size of network required for a threshold performance.

cs.NE↗

arXiv2020

Nanoscale plasticity and neuromorphic dynamics in silicon suboxide RRAM

Resistive random-access memories, also known as memristors, whose resistance can be modulated by the electrically driven formation and disruption of conductive filaments within an insulator, are promising candidates for neuromorphic applications due to their scalability, low-power operation and diverse functional behaviours. However, understanding the dynamics of individual filaments, and the surrounding material, is challenging, owing to the typically very large cross-sectional areas of test devices relative to the nanometre scale of individual filaments. In the present work, conductive atomic force microscopy is used to study the evolution of conductivity at the nanoscale in a fully CMOS-compatible silicon suboxide thin film. Distinct filamentary plasticity and background conductivity enhancement are reported, suggesting that device behaviour might be best described by composite core (filament) and shell (background conductivity) dynamics. Furthermore, constant current measurements demonstrate an interplay between filament formation and rupture, resulting in current-controlled voltage spiking in nanoscale regions, with an estimated optimal energy consumption of 25 attojoules per spike. This is very promising for extremely low-power neuromorphic computation and suggests that the dynamic behaviour observed in larger devices should persist and improve as dimensions are scaled down.

cond-mat.mtrl-sci↗