Neuromorphic Computing - An Overview

• URL: http://arxiv.org/abs/2510.06721v2
• Date: Fri, 17 Oct 2025 14:49:51 GMT
• Title: Neuromorphic Computing - An Overview
• Authors: Benedikt Jung, Maximilian Kalcher, Merlin Marinova, Piper Powell, Esma Sakalli,
• Abstract summary: A new field has emerged seeking to follow the example of the human brain into a new era: neuromorphic computing.<n>This paper provides an introduction to neuromorphic computing, why this and other new computing systems are needed, and what technologies currently exist in the neuromorphic field.
• Score: 0.07249400282852116
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: With traditional computing technologies reaching their limit, a new field has emerged seeking to follow the example of the human brain into a new era: neuromorphic computing. This paper provides an introduction to neuromorphic computing, why this and other new computing systems are needed, and what technologies currently exist in the neuromorphic field. It begins with a general introduction into the history of traditional computing and its present problems, and then proceeds to a general overview of neuromorphic systems. It subsequently discusses the main technologies currently in development. For completeness, the paper first discusses neuromorphic-style computing on traditional hardware, and then discusses the two top branches of specialized hardware in this field; neuromorphic chips and photonic systems. Both branches are explained as well as their relative benefits and drawbacks. The paper concludes with a summary and an outlook on the future.

Related papers

• Reservoir Computing: A New Paradigm for Neural Networks [0.0]
  In the early 1940s the first artificial neuron models were created as purely mathematical concepts.<n>Recurrent Neural Networks (RNNs) exacerbates the difficulties of traditional neural nets.<n>Reservoir Computing emerges as a solution to these problems.
  arXiv  Detail & Related papers  (2025-04-03T14:34:51Z)
• Neuromorphic Programming: Emerging Directions for Brain-Inspired Hardware [0.0]
  Currently, neuromorphic hardware often relies on machine learning methods adapted from deep learning. Neuromorphic computers have potential far beyond deep learning if we can only harness their energy efficiency and full computational power. This paper presents a conceptual analysis of programming within the context of neuromorphic computing.
  arXiv  Detail & Related papers  (2024-10-15T10:08:15Z)
• Neuromorphic Spintronics [0.0]
  Neuromorphic spintronics combines two advanced fields in technology, neuromorphic computing and spintronics, to create brain-inspired, efficient computing systems. We discuss concrete examples of neuromorphic spintronics, including computing based on fluctuations, artificial neural networks, and reservoir computing.
  arXiv  Detail & Related papers  (2024-09-16T13:57:39Z)
• Concepts and Paradigms for Neuromorphic Programming [0.0]
  Currently, neuromorphic computers are mostly limited to machine learning methods adapted from deep learning. Neuromorphic computers have potential far beyond deep learning if we can only make use of their computational properties to harness their full power.
  arXiv  Detail & Related papers  (2023-10-27T16:48:11Z)
• Brain-inspired Computational Intelligence via Predictive Coding [73.42407863671565]
  Predictive coding (PC) has shown promising properties that make it potentially valuable for the machine learning community.<n>PC-like algorithms are starting to be present in multiple sub-fields of machine learning and AI at large.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• Constraints on the design of neuromorphic circuits set by the properties of neural population codes [61.15277741147157]
  In the brain, information is encoded, transmitted and used to inform behaviour. Neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain.
  arXiv  Detail & Related papers  (2022-12-08T15:16:04Z)
• 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)
• Neurocompositional computing: From the Central Paradox of Cognition to a new generation of AI systems [120.297940190903]
  Recent progress in AI has resulted from the use of limited forms of neurocompositional computing. New, deeper forms of neurocompositional computing create AI systems that are more robust, accurate, and comprehensible.
  arXiv  Detail & Related papers  (2022-05-02T18:00:10Z)
• 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)
• Photonics for artificial intelligence and neuromorphic computing [52.77024349608834]
  Photonic integrated circuits have enabled ultrafast artificial neural networks. Photonic neuromorphic systems offer sub-nanosecond latencies. These systems could address the growing demand for machine learning and artificial intelligence.
  arXiv  Detail & Related papers  (2020-10-30T21:41:44Z)

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.