Neuromorphic Computing and Engineering

Scope & Guideline

Advancing the Intersection of AI and Hardware

Introduction

Welcome to the Neuromorphic Computing and Engineering information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of Neuromorphic Computing and Engineering, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN-
PublisherIOP Publishing Ltd
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationNEUROMORPH COMPUT EN / Neuromorphic Comput. Eng.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressTEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND

Aims and Scopes

The journal "Neuromorphic Computing and Engineering" aims to advance the field of neuromorphic computing by publishing cutting-edge research that integrates hardware and software developments, focusing on biologically inspired computational models and systems.
  1. Neuromorphic Hardware Development:
    Research on the design and implementation of hardware systems that mimic the neural structure and function of biological brains, including memristors, synaptic devices, and photonic systems.
  2. Algorithms and Architectures for Neuromorphic Systems:
    Exploration of novel algorithms, architectures, and methodologies that leverage neuromorphic principles for efficient processing, learning, and information representation.
  3. Integration of Biological Principles:
    Studies that draw inspiration from biological neural networks and cognitive processes, aiming to replicate or model their functionalities in artificial systems.
  4. Event-Driven and Spiking Neural Networks:
    Focus on computational models that utilize event-driven processing and spiking neural networks, emphasizing real-time learning and adaptability.
  5. Applications in Robotics and AI:
    Application of neuromorphic computing principles to enhance robotic systems, artificial intelligence, and edge computing, promoting intelligent and autonomous behaviors.
  6. Energy-Efficient Computing Solutions:
    Research targeting the development of low-power, energy-efficient neuromorphic systems suitable for various applications, particularly in mobile and embedded environments.
Recent publications in "Neuromorphic Computing and Engineering" reveal a shift towards innovative themes and methodologies that are gaining traction. This section outlines the emerging trends within the journal.
  1. Hybrid Neuromorphic Systems:
    There is a growing interest in hybrid systems that combine various neuromorphic technologies, such as integrating optical and electronic components, to enhance computational capabilities and efficiency.
  2. Bioinspired Learning Mechanisms:
    Emerging research focuses on bioinspired learning mechanisms, including spiking neural networks that mimic biological learning processes, highlighting their potential for real-time adaptation and efficiency.
  3. 2D Materials and Emerging Devices:
    The use of two-dimensional materials in neuromorphic devices is becoming increasingly prominent, showcasing their unique properties for creating advanced synaptic and neuronal elements.
  4. Event-Driven Processing Techniques:
    Event-driven processing methodologies are trending, emphasizing real-time data processing and low-power operation, which aligns with the needs of modern AI applications.
  5. Applications in Edge Computing and IoT:
    Research is increasingly directed towards the application of neuromorphic computing in edge devices and IoT systems, focusing on efficient processing capabilities for on-device AI solutions.
  6. Exploration of Non-Volatile Memory Technologies:
    There is heightened interest in non-volatile memory technologies for neuromorphic computing, as they offer potential for enhanced data retention and energy efficiency in neural architectures.

Declining or Waning

As the field of neuromorphic computing evolves, certain themes that were once prevalent are becoming less prominent in recent publications. This section highlights those waning areas of focus.
  1. Traditional Neural Network Architectures:
    The exploration of classical neural network architectures has decreased as researchers pivot towards more biologically inspired and neuromorphic models that offer enhanced efficiency and adaptability.
  2. Purely Software-Based Neural Implementations:
    There is a noticeable decline in research focused solely on software-based neural network implementations without hardware integration, as the field increasingly emphasizes the interplay between hardware and software.
  3. Single-Domain Device Research:
    Studies that focus exclusively on individual device types without considering integrated systems or multi-device architectures are waning, as the trend shifts towards holistic approaches that encompass complex interactions.
  4. Generic Machine Learning Techniques:
    Research employing generic machine learning techniques without a focus on neuromorphic principles is becoming less common, reflecting a shift in interest towards specialized methods that align with neuromorphic computing paradigms.
  5. Theoretical Studies without Practical Application:
    There is a decline in purely theoretical explorations of neuromorphic concepts without tangible experimental validation or practical application, as the field moves towards more applied research.

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