A Bibliometric Review of Neuromorphic Computing and Spiking Neural
Networks
- URL: http://arxiv.org/abs/2304.06897v1
- Date: Fri, 14 Apr 2023 02:14:38 GMT
- Title: A Bibliometric Review of Neuromorphic Computing and Spiking Neural
Networks
- Authors: Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun and Richard
Dodson
- Abstract summary: spiking neural networks hold the potential to advance artificial intelligence.
Neuromorphic computing hardware is transitioning from laboratory prototype devices to commercial chipsets.
As a nexus of biological, computing, and material sciences, the literature surrounding these concepts is vast.
- Score: 27.535725907849216
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Neuromorphic computing and spiking neural networks aim to leverage biological
inspiration to achieve greater energy efficiency and computational power beyond
traditional von Neumann architectured machines. In particular, spiking neural
networks hold the potential to advance artificial intelligence as the basis of
third-generation neural networks. Aided by developments in memristive and
compute-in-memory technologies, neuromorphic computing hardware is
transitioning from laboratory prototype devices to commercial chipsets;
ushering in an era of low-power computing. As a nexus of biological, computing,
and material sciences, the literature surrounding these concepts is vast,
varied, and somewhat distinct from artificial neural network sources. This
article uses bibliometric analysis to survey the last 22 years of literature,
seeking to establish trends in publication and citation volumes (III-A);
analyze impactful authors, journals and institutions (III-B); generate an
introductory reading list (III-C); survey collaborations between countries,
institutes and authors (III-D), and to analyze changes in research topics over
the years (III-E). We analyze literature data from the Clarivate Web of Science
using standard bibliometric methods. By briefly introducing the most impactful
literature in this field from the last two decades, we encourage AI
practitioners and researchers to look beyond contemporary technologies toward a
potentially spiking future of computing.
Related papers
- A Review of Neuroscience-Inspired Machine Learning [58.72729525961739]
Bio-plausible credit assignment is compatible with practically any learning condition and is energy-efficient.
In this paper, we survey several vital algorithms that model bio-plausible rules of credit assignment in artificial neural networks.
We conclude by discussing the future challenges that will need to be addressed in order to make such algorithms more useful in practical applications.
arXiv Detail & Related papers (2024-02-16T18:05:09Z) - Advanced Computing and Related Applications Leveraging Brain-inspired
Spiking Neural Networks [0.0]
Spiking neural network is one of the cores of artificial intelligence which realizes brain-like computing.
This paper summarizes the strengths, weaknesses and applicability of five neuronal models and analyzes the characteristics of five network topologies.
arXiv Detail & Related papers (2023-09-08T16:41:08Z) - Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
Predictive coding (PC) has shown promising performance in machine intelligence tasks.
PC can model information processing in different brain areas, can be used in cognitive control and robotics.
arXiv Detail & Related papers (2023-08-15T16:37:16Z) - Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in
Scientific Computing [0.0]
Recent breakthroughs in computing power have made it feasible to use machine learning and deep learning to advance scientific computing.
Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data is sparse.
Neural networks offer a strong foundation to digest physical-driven or knowledge-based constraints.
arXiv Detail & Related papers (2022-11-14T15:44:07Z) - Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing [73.0977635031713]
Neural-symbolic computing (NeSy) has been an active research area of Artificial Intelligence (AI) for many years.
NeSy shows promise of reconciling the advantages of reasoning and interpretability of symbolic representation and robust learning in neural networks.
arXiv Detail & Related papers (2022-10-28T04:38:10Z) - Neuromorphic Artificial Intelligence Systems [58.1806704582023]
Modern AI systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the brain.
This article discusses such limitations and the ways they can be mitigated.
It presents an overview of currently available neuromorphic AI projects in which these limitations are overcome.
arXiv Detail & Related papers (2022-05-25T20:16:05Z) - The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity [0.0]
We describe the second generation of the BrainScaleS neuromorphic architecture, emphasizing applications enabled by this architecture.
It combines a custom accelerator core supporting the accelerated physical emulation of bio-inspired spiking neural network primitives with a tightly coupled digital processor and a digital event-routing network.
arXiv Detail & Related papers (2022-01-26T17:13:46Z) - Neural Fields in Visual Computing and Beyond [54.950885364735804]
Recent advances in machine learning have created increasing interest in solving visual computing problems using coordinate-based neural networks.
neural fields have seen successful application in the synthesis of 3D shapes and image, animation of human bodies, 3D reconstruction, and pose estimation.
This report provides context, mathematical grounding, and an extensive review of literature on neural fields.
arXiv Detail & Related papers (2021-11-22T18:57:51Z) - Neuromorphic Processing and Sensing: Evolutionary Progression of AI to
Spiking [0.0]
Spiking Neural Network algorithms hold the promise to implement advanced artificial intelligence using a fraction of the computations and power requirements.
This paper explains the theoretical workings of neuromorphic technologies based on spikes, and overviews the state-of-art in hardware processors, software platforms and neuromorphic sensing devices.
A progression path is paved for current machine learning specialists to update their skillset, as well as classification or predictive models from the current generation of deep neural networks to SNNs.
arXiv Detail & Related papers (2020-07-10T20:54:42Z) - Spiking Neural Networks Hardware Implementations and Challenges: a
Survey [53.429871539789445]
Spiking Neural Networks are cognitive algorithms mimicking neuron and synapse operational principles.
We present the state of the art of hardware implementations of spiking neural networks.
We discuss the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level.
arXiv Detail & Related papers (2020-05-04T13:24:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.