81 research outputs found
Neuromorphic hardware for somatosensory neuroprostheses
In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies
2022 roadmap on neuromorphic computing and engineering
Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community
Toward a formal theory for computing machines made out of whatever physics offers: extended version
Approaching limitations of digital computing technologies have spurred
research in neuromorphic and other unconventional approaches to computing. Here
we argue that if we want to systematically engineer computing systems that are
based on unconventional physical effects, we need guidance from a formal theory
that is different from the symbolic-algorithmic theory of today's computer
science textbooks. We propose a general strategy for developing such a theory,
and within that general view, a specific approach that we call "fluent
computing". In contrast to Turing, who modeled computing processes from a
top-down perspective as symbolic reasoning, we adopt the scientific paradigm of
physics and model physical computing systems bottom-up by formalizing what can
ultimately be measured in any physical substrate. This leads to an
understanding of computing as the structuring of processes, while classical
models of computing systems describe the processing of structures.Comment: 76 pages. This is an extended version of a perspective article with
the same title that will appear in Nature Communications soon after this
manuscript goes public on arxi
Robust learning algorithms for spiking and rate-based neural networks
Inspired by the remarkable properties of the human brain, the fields of machine learning, computational neuroscience and neuromorphic engineering have achieved significant synergistic progress in the last decade. Powerful neural network models rooted in machine learning have been proposed as models for neuroscience and for applications in neuromorphic engineering. However, the aspect of robustness is often neglected in these models. Both biological and engineered substrates show diverse imperfections that deteriorate the performance of computation models or even prohibit their implementation. This thesis describes three projects aiming at implementing robust learning with local plasticity rules in neural networks. First, we demonstrate the advantages of neuromorphic computations in a pilot study on a prototype chip. Thereby, we quantify the speed and energy consumption of the system compared to a software simulation and show how on-chip learning contributes to the robustness of learning. Second, we present an implementation of spike-based Bayesian inference on accelerated neuromorphic hardware. The model copes, via learning, with the disruptive effects of the imperfect substrate and benefits from the acceleration. Finally, we present a robust model of deep reinforcement learning using local learning rules. It shows how backpropagation combined with neuromodulation could be implemented in a biologically plausible framework. The results contribute to the pursuit of robust and powerful learning networks for biological and neuromorphic substrates
Exploring the landscapes of "computing": digital, neuromorphic, unconventional -- and beyond
The acceleration race of digital computing technologies seems to be steering
toward impasses -- technological, economical and environmental -- a condition
that has spurred research efforts in alternative, "neuromorphic" (brain-like)
computing technologies. Furthermore, since decades the idea of exploiting
nonlinear physical phenomena "directly" for non-digital computing has been
explored under names like "unconventional computing", "natural computing",
"physical computing", or "in-materio computing". This has been taking place in
niches which are small compared to other sectors of computer science. In this
paper I stake out the grounds of how a general concept of "computing" can be
developed which comprises digital, neuromorphic, unconventional and possible
future "computing" paradigms. The main contribution of this paper is a
wide-scope survey of existing formal conceptualizations of "computing". The
survey inspects approaches rooted in three different kinds of background
mathematics: discrete-symbolic formalisms, probabilistic modeling, and
dynamical-systems oriented views. It turns out that different choices of
background mathematics lead to decisively different understandings of what
"computing" is. Across all of this diversity, a unifying coordinate system for
theorizing about "computing" can be distilled. Within these coordinates I
locate anchor points for a foundational formal theory of a future
computing-engineering discipline that includes, but will reach beyond, digital
and neuromorphic computing.Comment: An extended and carefully revised version of this manuscript has now
(March 2021) been published as "Toward a generalized theory comprising
digital, neuromorphic, and unconventional computing" in the new open-access
journal Neuromorphic Computing and Engineerin
Reframing superintelligence: comprehensive AI services as general intelligence
Studies of superintelligent-level systems have typically posited AI functionality that plays the role of a mind in a rational utility-directed agent, and hence employ an abstraction initially developed as an idealized model of human decision makers. Today, developments in AI technology highlight intelligent systems that are quite unlike minds, and provide a basis for a different approach to understanding them: Today, we can consider how AI systems are produced (through the work of research and development), what they do (broadly, provide services by performing tasks), and what they will enable (including incremental yet potentially thorough automation of human tasks).
Because tasks subject to automation include the tasks that comprise AI research and development, current trends in the field promise accelerating AI-enabled advances in AI technology itself, potentially lead- ing to asymptotically recursive improvement of AI technologies in distributed systems, a prospect that contrasts sharply with the vision of self-improvement internal to opaque, unitary agents.
The trajectory of AI development thus points to the emergence of asymptotically comprehensive, superintelligent-level AI services that— crucially—can include the service of developing new services, both narrow and broad, guided by concrete human goals and informed by strong models of human (dis)approval. The concept of comprehensive AI services (CAIS) provides a model of flexible, general intelligence in which agents are a class of service-providing products, rather than a natural or necessary engine of progress in themselves.
Ramifications of the CAIS model reframe not only prospects for an intelligence explosion and the nature of advanced machine intelligence, but also the relationship between goals and intelligence, the problem of harnessing advanced AI to broad, challenging problems, and fundamental considerations in AI safety and strategy. Perhaps surprisingly, strongly self-modifying agents lose their instrumental value even as their implementation becomes more accessible, while the likely context for the emergence of such agents becomes a world already in possession of general superintelligent-level capabilities. These prospective capabilities, in turn, engender novel risks and opportunities of their own.
Further topics addressed in this work include the general architecture of systems with broad capabilities, the intersection between symbolic and neural systems, learning vs. competence in definitions of intelligence, tactical vs. strategic tasks in the context of human control, and estimates of the relative capacities of human brains vs. current digital systems
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