Books and Special Issues

Fuchun Sun, Dewen Hu, Stefan Wermter, Lei Yang, Huaping Liu, Bin Fang.
Springer Nature Singapore Pte Ltd. 2022.

This book constitutes the refereed post-conference proceedings of the 6th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2021, held in Suzhou, China, in November 2021.

The 41 revised papers presented were carefully reviewed and selected from 105 submissions. The papers are organized in topical sections on algorithm; vision; and robotics and application..

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Jianwei Zhang, Stefan Wermter, Fuchun Sun, Changshui Zhang, Andreas K. Engel, Brigitte Röder, Xiaolan Fu and Gui Xue.
Frontiers in Integrative Neuroscience, Frontiers in Robotics and Ai and Frontiers in Neurorobotics, 2021.

The purpose of this Research Topic is to reflect and discuss links between neuroscience, psychology, computer science and robotics with regards to the topic of cross-modal learning which has, in recent years, emerged as a new area of interdisciplinary research. The term cross-modal learning refers to the synergistic synthesis of information from multiple sensory modalities such that the learning that occurs within any individual sensory modality can be enhanced with information from one or more other modalities. Cross-modal learning is a crucial component of adaptive behavior in a continuously changing world, and examples are ubiquitous, such as: learning to grasp and manipulate objects; learning to walk; learning to read and write; learning to understand language and its referents; etc. In all these examples, visual, auditory, somatosensory or other modalities have to be integrated, and learning must be cross-modal. In fact, the broad range of acquired human skills are cross-modal, and many of the most advanced human capabilities, such as those involved in social cognition, require learning from the richest combinations of cross-modal information. In contrast, even the very best systems in Artificial Intelligence (AI) and robotics have taken only tiny steps in this direction. Building a system that composes a global perspective from multiple distinct sources, types of data, and sensory modalities is a grand challenge of AI, yet it is specific enough that it can be studied quite rigorously and in such detail that the prospect for deep insights into these mechanisms is quite plausible in the near term. Cross-modal learning is a broad, interdisciplinary topic that has not yet coalesced into a single, unified field. Instead, there are many separate fields, each tackling the concerns of cross-modal learning from its own perspective, with currently little overlap. We anticipate an accelerating trend towards integration of these areas and we intend to contribute to that integration. By focusing on cross-modal learning, the proposed Research Topic can bring together recent progress in artificial intelligence, robotics, psychology and neuroscience.

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Farkaš, I., Masulli, P., Otte, S., Wermter, S. (Eds.).
Springer International Publishing, 2021.

The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.*

The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes.

In this volume, the papers focus on topics such as adversarial machine learning, anomaly detection, attention and transformers, audio and multimodal applications, bioinformatics and biosignal analysis, capsule networks and cognitive models.

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Igor Farkaš, Paolo Masulli, Stefan Wermter (Eds.).
Springer International Publishing, 2020.

The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks, ICANN 2020, held in Bratislava, Slovakia, in September 2020.

The total of 139 full papers presented in these proceedings was carefully reviewed and selected from 249 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics and biosignal analysis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action.

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Stefan Wermter, Cornelius Weber, Wlodzislaw Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Allessandro E.P Villa (Eds.).
Springer International Publishing, 2014.

The book contains the results of the 24th International Conference on Artificial Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014. The research areas of the papers include: recurrent networks; competitive learning and self-organisation; clustering and classification; trees and graphs; human-machine interaction; deep networks; theory of neural networks; reinforcement learning and action; neural networks for vision; supervised learning; dynamical models and time series; neuroscience models and neural network applications.

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Stefan Wermter, Martin Page, Michael Knowles, Vittorio Gallese, Friedemann Pulvermüller and John Taylor.
Elsevier, Volume 22, Number 2, March 2009.

The general aim of this special issue is to focus on "What it means to communicate" and to understand the neural, cognitive, formal, computational and developmental features that have led to communication differences between humans and animals.

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Stefan Wermter, Günther Palm, Mark Elshaw.
Springer-Verlag, Heidelberg, Germany, July 2005.

This book presents research performed as part of the EU project on biomimetic multimodal learning in a mirror neuron-based robot (MirrorBot) and contributions presented at the International AI-Workshop in NeuroBotics. The overall aim of the book is to present a broad spectrum of current research into biomimetic neural learning for intelligent autonomous robots. There is a need for a new type of robots which is inspired by nature and so performs in a more flexible learned manner than current robots. This new type of robots is driven by recent new theories and experiments in neuroscience indicating that a biological and neuroscience-oriented approach could lead to new life-like robotic systems.

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Stefan Wermter, Jim Austin, David Willshaw.
Springer-Verlag, Heidelberg, Germany, March 2001.

This book is the result of a series of International Workshops organised by the EmerNet project on Emergent Neural Computational Architectures based on Neuroscience sponsored by the Engineering and Physical Sciences Research Council (EPSRC). The overall aim of the book is to present a broad spectrum of current research into biologically inspired computational systems and hence encourage the emergence of new computational approaches based on neuroscience. It is generally understood that the present approaches for computing do not have the performance, flexibility and reliability of biological information processing systems. Although there is a massive body of knowledge regarding how processing occurs in the brain and central nervous system this has had little impact on mainstream computing so far.

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Stefan Wermter, Ron Sun.
Springer-Verlag, Heidelberg, Germany, March 2000.

The aim of this book is to present a broad spectrum of current research in hybrid neural systems, and advance the state of the art in neural networks and artificial intelligence. Hybrid neural systems are computational systems which are based mainly on artificial neural networks but which also allow a symbolic interpretation or interaction with symbolic components.

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Stefan Wermter, Ellen Riloff, Gabriele Scheler
Springer Verlag, Berlin, Germany, 1996.

The purpose of this book is to provide an introduction to the field of connectionist, statistical and symbolic approaches to learning for natural language processing, based on the contributions in this book.

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Stefan Wermter.
Chapman and Hall, International Thomson Computer Press, London, UK, January 1995.

The objective of this book is to describe a new approach in hybrid connectionist natural language processing which bridges the gap between strictly symbolic and connectionist systems. This objective is tackled in two ways:the book gives an overview of hybrid connectionist architectures for naturallanguage processing; and it demonstrates that a hybrid connectionist architecture can be used for learning real-world natural language problems.

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