Memristive Reservoirs Learn to Learn. (arXiv:2306.12676v1 [cond-mat.dis-nn])

Memristive Reservoirs Learn to Learn. (arXiv:2306.12676v1 [cond-mat.dis-nn])
By: <a href="">Ruomin Zhu</a>, <a href="">Jason K. Eshraghian</a>, <a href="">Zdenka Kuncic</a> Posted: June 23, 2023

Memristive reservoirs draw inspiration from a novel class of neuromorphic
hardware known as nanowire networks. These systems display emergent brain-like
dynamics, with optimal performance demonstrated at dynamical phase transitions.
In these networks, a limited number of electrodes are available to modulate
system dynamics, in contrast to the global controllability offered by
neuromorphic hardware through random access memories. We demonstrate that the
learn-to-learn framework can effectively address this challenge in the context
of optimization. Using the framework, we successfully identify the optimal
hyperparameters for the reservoir. This finding aligns with previous research,
which suggests that the optimal performance of a memristive reservoir occurs at
the `edge of formation’ of a conductive pathway. Furthermore, our results show
that these systems can mimic membrane potential behavior observed in spiking
neurons, and may serve as an interface between spike-based and continuous

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