Information dynamics in neuromorphic nanowire networks

Ruomin Zhu,Joseph T Lizier,Joel Hochstetter,Adrian Diaz-Alvarez,Tomonobu Nakayama,Alon Loeffler,Zdenka Kuncic

doi:10.1038/s41598-021-92170-7

Abstract

Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology. Additionally, various information processing tasks have been demonstrated with neuromorphic nanowire networks. Here, we investigate the dynamics of how these unique systems process information through information-theoretic metrics. In particular, Transfer Entropy (TE) and Active Information Storage (AIS) are employed to investigate dynamical information flow and short-term memory in nanowire networks. In addition to finding that the topologically central parts of networks contribute the most to the information flow, our results also reveal TE and AIS are maximized when the networks transitions from a quiescent to an active state. The performance of neuromorphic networks in memory and learning tasks is demonstrated to be dependent on their internal dynamical states as well as topological structure. Optimal performance is found when these networks are pre-initialised to the transition state where TE and AIS are maximal. Furthermore, an optimal range of information processing resources (i.e. connectivity density) is identified for performance. Overall, our results demonstrate information dynamics is a valuable tool to study and benchmark neuromorphic systems.

Highlights

Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology
An externally applied voltage is redistributed across the networked junctions, causing some to switch from a low-conductance state to a high-conductance state
This study is the first to investigate the spatio-temporal dynamics of information transfer and storage in neuromorphic nanowire networks

Summary

Introduction

Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology. Neuromorphic information processing capabilities have been demonstrated in low-dimensional nanomaterials constructed by bottom-up methods, e.g. quantum d ots[16], carbon n anotubes17, nanoparticles18 - see Sangwan and Hersham (2020)[19] for a comprehensive review These represent a unique class of neuromorphic systems, as bio-inspired self-assembly can produce highly disordered structures with emergent collective computational abilities[20,21]. This draws strong similarities to complex systems approaches used to study the brain’s network dynamics[22]. TE is useful in measuring how information carried by signals is propagated across a network, and can help identify the most important components involved in the dynamics of a n etwork[44,51], and performance of the network can be optimized a ccordingly[52]

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Conclusion