2,289 research outputs found
Dimensions of Timescales in Neuromorphic Computing Systems
This article is a public deliverable of the EU project "Memory technologies
with multi-scale time constants for neuromorphic architectures" (MeMScales,
https://memscales.eu, Call ICT-06-2019 Unconventional Nanoelectronics, project
number 871371). This arXiv version is a verbatim copy of the deliverable
report, with administrative information stripped. It collects a wide and varied
assortment of phenomena, models, research themes and algorithmic techniques
that are connected with timescale phenomena in the fields of computational
neuroscience, mathematics, machine learning and computer science, with a bias
toward aspects that are relevant for neuromorphic engineering. It turns out
that this theme is very rich indeed and spreads out in many directions which
defy a unified treatment. We collected several dozens of sub-themes, each of
which has been investigated in specialized settings (in the neurosciences,
mathematics, computer science and machine learning) and has been documented in
its own body of literature. The more we dived into this diversity, the more it
became clear that our first effort to compose a survey must remain sketchy and
partial. We conclude with a list of insights distilled from this survey which
give general guidelines for the design of future neuromorphic systems
A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning
Reservoir computing (RC), first applied to temporal signal processing, is a
recurrent neural network in which neurons are randomly connected. Once
initialized, the connection strengths remain unchanged. Such a simple structure
turns RC into a non-linear dynamical system that maps low-dimensional inputs
into a high-dimensional space. The model's rich dynamics, linear separability,
and memory capacity then enable a simple linear readout to generate adequate
responses for various applications. RC spans areas far beyond machine learning,
since it has been shown that the complex dynamics can be realized in various
physical hardware implementations and biological devices. This yields greater
flexibility and shorter computation time. Moreover, the neuronal responses
triggered by the model's dynamics shed light on understanding brain mechanisms
that also exploit similar dynamical processes. While the literature on RC is
vast and fragmented, here we conduct a unified review of RC's recent
developments from machine learning to physics, biology, and neuroscience. We
first review the early RC models, and then survey the state-of-the-art models
and their applications. We further introduce studies on modeling the brain's
mechanisms by RC. Finally, we offer new perspectives on RC development,
including reservoir design, coding frameworks unification, physical RC
implementations, and interaction between RC, cognitive neuroscience and
evolution.Comment: 51 pages, 19 figures, IEEE Acces
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