CCMB 2014 - IEEE Symposium on Computational Intelligence (CCMB 2014)

Computational Intelligence (CI) has for many years drawn inspiration from the brain to produce data and signal processing techniques and systems which are capable of learning, evolving, adapting, self-organizing, communicating effectively with humans and machines and controlling complex systems. Brain-inspired methods are now widely used to process data produced by the brain with the aim of improving our understanding of how the brain functions and produces the remarkable intelligence exhibited by humans, which is yet elusive for computational systems.
Topics
This Symposium focuses on several core topics associated with cognitive algorithms, mind and brain, which are deemed to be of critical importance as we progress into the 21st century.
Brain-Computer Interfaces (BCIs): BCI technology enables communication which does not rely on neuromuscular control thereby offering assistance to those who require alternative communicatory and control mechanisms because of neuromuscular deficiencies due to disease, or spinal/brain damage. BCI have advanced significantly in the past few years but there remain a significant number of problems and issues to be resolved due to the high level of noise, and the non- stationary nature of the neural signals used in BCI.
Computational models of functional and dysfunctional brain circuitry: Mathematical and neurocomputational models are contributing to the understanding of structural, functional, and behavioural aspects of brain regions. They can play an instrumental role in bridging the gap between the brain and behavior and help to unravel the underlying mechanisms of therapy. It is desirable that a computational model be a supplement to behavioural and experimental neuroscientists in understanding functional anomalies in the brain corresponding to certain medical conditions.
Cognitive Robotics: Controlling robots for assistive devices and industrial applications is a key area of Computational Intelligence research. It is critical to consider the intentional action-perception cycle, when the robot develops its own understanding of the world through interactions and hypothesis testing. Brain inspired approaches to robotic control and cognitive robotics become increasingly popular and lead to various breakthroughs in the field of autonomous systems designs and implementations.
In general, we are interested in dynamic brain models and brain inspired methods which are used to differentiate between signals from different domains, uniquely fingerprint signals for identification, feature extraction, classification, modelling, prediction and more but the main topics of interest include studies of
Cortical dynamics, theory & experiments
Brain-machine interfaces & neuroprostheses
Motor circuitry of the brain
Autonomous robot control
Brain rhythms and their cognitive relevance
Embodied cognition modeling
Neural Modelling Fields (NMF)
Cognitive robotics
Perceptual processing
Evolutionary and multi-agent modeling
Psychological and neurological disorders
Language learning and evolution
Cognitive and emotional processing
Socio-cultural modelling