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<span class="" style="font-size: 11pt; font-family: Arial, serif, EmojiFont; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;">Call for papers: Special Session
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IJCNN-24 Neural Techniques for Artificial and Natural Locomotions</strong> in IJCNN 2018</span></div>
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<span class="" style="font-size: 11pt; font-family: Arial, serif, EmojiFont; vertical-align: baseline; white-space: pre-wrap;">International Joint Conference on Neural Networks, hosted at IEEE World Congress on Computational Intelligence (IEEE WCCI 2018)</span></div>
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<span class="" style="font-size: 11pt; font-family: Arial, serif, EmojiFont; vertical-align: baseline; white-space: pre-wrap;">8-13 July 2018, Rio de Janeiro, Brazil -
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<span class="" style="font-size: 11pt; font-family: Arial, serif, EmojiFont; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;">Neural Techniques for Artificial and Natural Locomotions
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<i>Organized by Zhijun Yang (Z.Yang@mdx.ac.uk), Vaibhav Gandhi, Mehmet Karamanoglu, and Felipe França</i></p>
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There are evidences showing that walking with adaptive gait patterns may be an acquired characteristic possessed by legged animals and humans. A baby animal or infant usually experiences an inept process to learn walking before becoming fully adaptive to a
complex terrain. This learning process starts with reflexes reflecting involuntary responses to stimuli. It may involve a complex sequence of activities for sensorimotor integration, synchronization and coordination of cortical neurons and muscles. After the
relevant cortical regions are well acquainted with the external world, the animals are considered as trained and represent the most capable walking machine in nature.</p>
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Many theoretical and experimental approaches have been proposed intending to decipher the mechanisms underlying the natural locomotion while presenting its artificial intelligence (AI) counterpart. For instance, the finite state machine (FSM), in both deterministic
and probabilistic variants, are traditionally used to model the gait pattern generation and transition. Recent years have seen the interest in this area of research growing rapidly thanks to the emergence of new computing methodologies using spiking neurons
and neuronal populations, with the neuron complexity rangin from the simplest integrate-and- firing type to the classic Hodgkin-Huxley type. A special<br style="box-sizing: border-box;">
Izhikevich neuron can display an abundant spectrum of real neuron activities. These methods show great potential in modelling the natural locomotion models in this relatively new research field. On the other hand, the modern technology provides us means of
implementing the theoretic models by using high performance computing (HPC) techniques such as dedicated neuromorphic circuits, GPUs, FPGAs as well as deep learning tools.</div>
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<span style="box-sizing: border-box; border: 0px; font-family: inherit; font-style: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;"><u><b>Scope and Topics</b></u></span></div>
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This special session brings together the new research works from academics and industry related researchers in this prevalent area. The workshop aims to promote the applications of multidisciplinary methods in investigating and exploiting the neural mechanisms
of natural locomotion. We invite papers on both theory and applications of the broad area of neural control for natural locomotion. The artificial locomotion systems, built upon the mechanisms underlying the natural locomotion systems, are particularly welcome.
The topics of interest include, but are not limited to, the following.
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Neuroscience studies of natural locomotion and control</li><li style="box-sizing: border-box; border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">
Novel mathematic models for gait pattern generation and transition</li><li style="box-sizing: border-box; border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">
Central pattern generation models and applications</li><li style="box-sizing: border-box; border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">
Somatosensory system, sensorimotor interaction and impacts on locomotion</li><li style="box-sizing: border-box; border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">
Machine learning and deep learning methods applicable for motion control</li><li style="box-sizing: border-box; border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">
Neuromorphic hardware implementation, parallel computing platforms using state-of- the-art<br style="box-sizing: border-box;">
hardware such as GPU or FPGA for neural control of locomotion</li><li style="box-sizing: border-box; border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">
Software frameworks, such as robot operating system (ROS), applicable for robot control</li><li style="box-sizing: border-box; border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">
Bayesian inference and hidden Markov models for decision making on robot motion</li><li style="box-sizing: border-box; border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">
Novel finite state machine methods, implementation and applications in robotics</li><li style="box-sizing: border-box; border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">
State-of- the-art robot projects using leading edge hardware and/or software</li></ul>
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<span class="" style="font-size: 11pt; font-family: Arial, serif, EmojiFont; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;">IMPORTANT DATES:</span></div>
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<span class="" style="font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">Paper submission: 15th January 2018</span></div>
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<span class="" style="font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">Paper acceptance: 15th March 2018</span></div>
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IEEE WCCI 2018 conference: 8-13 July 2018<br>
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<p>Kind regards,<br>
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<p>Zhijun</p>
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<div class="PlainText">Zhijun Yang, BEng, MSc, PhD, PGCertHE, FHEA<br>
Senior Lecturer in Design Engineering<br>
Department of Design Engineering and Mathematics<br>
Faculty of Science and Technology<br>
Middlesex University, London NW4 4BT, UK<br>
Voice: +44 (0)7882681333</div>
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