Connectionists: CFP: IEEE TNNLS Special Issue on "Learning in Neuromorphic Systems and Cyborg Intelligence"

[Huajin Tang]

Call For Papers

*IEEE Transactions on Neural Networks and Learning Systems*

Special Issue on

*Learning in Neuromorphic Systems and Cyborg Intelligence*


Emulating brain-like learning performance has been a key challenge for
research in neural networks and learning systems, including recognition,
memory and perception. In the last few decades, a variety of approaches for
brain-like learning and information processing have been proposed,
including approaches based on sparse representations or  hierarchical/deep
architectures. While capable of achieving impressive performance, these
methods still perform poorly compared to biological systems under a wide
variety of conditions. With the availability of neuromorphic hardware
providing a fundamentally different technique for data representation,
neuromorphic systems, using neural spikes to represent the outputs of
sensors and for communication between computing blocks, and using spike
timing based learning algorithms, have shown appealing computing
characteristics.  However, current neuromorphic learning systems cannot yet
achieve the performance figures comparable to what machine learning
approaches can offer. Neuromorphic systems are also compatible with another
framework called cyborg intelligence. Cyborg intelligence aims to deeply
integrate machine intelligence with biological intelligence by connecting
machines and living beings via brain-machine interfaces, enhancing
strengths and compensating for weaknesses by combining the biological
cognition capability with the machine computational capability. In cyborg
intelligence, the real-time interaction and exchange of information between
biological and artificial neural systems is still an important open
challenge, and existing learning approaches would not be able to meet such
a challenge. The goal of the special issue is to consolidate the efforts
for developing a suitable learning framework for neuromorphic systems and
cyborg intelligence and promote research activities in this area.



*SCOPE OF THE SPECIAL ISSUE*

We invite original contributions related to learning in neuromorphic
systems and cyborg intelligence, from theories, algorithms, modelling and
experiment studies to applications. Topics include but are not limited to:

   - Cognitive computing and cyborg intelligence
   - Neuromorphic information/signal processing
   - Brain-inspired data representation models
   - Neuromorphic learning and cognitive systems
   - Co-learning in bio-machine systems
   - Spike-based sensing and learning
   - Neuromorphic sensors and hardware systems
   - Intelligence for embedded systems
   - Cognition mechanisms for big data
   - Embodied cognition and neuro-robotics.


*Important Dates *

15 Nov 2014 – Deadline for manuscript submission

15 Feb 2015 – Notification of authors

15 Apr 2015–  Deadline for submission of revised manuscripts

15 May 2015 – Final decision



*Guest Editors *

Zhaohui Wu          Zhejiang University, China (wzh at zju.edu.cn)

Ryad Benosman     University of Pierre and Marie Curie, France (
ryad.benosman at upmc.fr)

Huajin Tang          Institute for Infocomm Research, Singapore and Sichuan
University  (huajin.tang at ieee.org)

Shih-Chii Liu         Institute of Neuroinformatics, University of Zurich
and ETH Zurich (shih at ini.phys.ethz.ch)



*Submission Instructions*

1. Read the information for Authors at http://cis.ieee.org/tnnls

2. Submit the manuscript by 15th Nov 2014 at the TNNLS webpage (
http://mc.manuscriptcentral.com/tnnls) and follow the submission procedure.
Please, clearly indicate on the first page of the manuscript and in the
cover letter that the manuscript has been submitted to the special
issue on *Learning
in Neuromorphic Systems and Cyborg Intelligence. *Send also an email to the
guest editors with subject “TNNLS special issue submission” to notify about
your submission.
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