Topics and Areas:

Track 1: Investigations of Human Information Processing Systems (HIPS) and Computational Foundations of Brain Science Adaptation and self-organization;

• Bayesian models of the brain, and causal modeling of behaviour for neurology;

• Brain dynamics and functional/resting/structural networks;

• Cognitive architectures and their relations to fMRI/EEG/MEG;

• Computational mechanisms of learning and memory;

• Computational models of sensory-motor control;

• Conscious mental functions and subconscious information processing;

• Emotion, heuristic search, information granularity, and autonomy related issues in reasoning and problem solving;

• Higher-order cognitive functions and their relationships;

• HIPS complex systems;

• Investigating spatiotemporal characteristics and flow in HIPS and the related neural structures and neurobiological process;

• Learning mechanisms (e.g., stability, personalized user/student models);

• Methodologies for systematic design of cognitive experiments;

• Models of executive function & prefrontal cortex;

• Modeling brain information processing mechanisms (e.g., information organization, neuromechanism, mathematical, cognitive and computational models of HIPS);

• Multi-perception mechanisms and visual, auditory, and tactile information processing;

• Neural basis of decision-making;

• Neural foundations of intelligent behavior;

• Reasoning mechanisms (e.g., principles of deductive/inductive reasoning, common-sense reasoning, decision making, and problem solving);

• Social brain communication.

Track 2: Information Technologies for Curating, Mining and Using Brain Big Data

• Brain data collection, pre-processing, management, and analysis methodologies;

• Brain connectome, functional connectivity, and multi-level brain networks;

• Brain data grids and brain research support services;

• Brain informatics provenances;

• Brain mapping and visualization;

• Cyber-individuals and individual differences;

• Data brain modeling and formal conceptual models of brain data;

• Databasing the brain, curating big data, and constructing brain data centers;

• Development of data-driven markers of diseases, and behavioural biomarkers of neurological diseases;

• fMRI and PET imaging registration and analysis;

• Information technologies for simulating brain data;

• Integrating multiple forms of brain big data obtained from atomic and molecular levels to the entire brain;

• Knowledge representation and discovery in neuroimaging;

• Large scale models and simulation of brains;

• Machine learning algorithms for brain data analysis;

• Measuring scale thresholds of brain big data;

• Multi-aspect analysis in fMRI/DTI/EEG/ERP/MEG/PET/Eye-tracking data;

• Multimedia brain data mining and reasoning;

• Multimodal and combinatorial fusion for brain informatics;

• Optogenetics and in-vivo cell imaging analytics;

• Real-time fMRI and neurofeedback;

• Remote neurological assessment;

• Semantic technology for brain data integration;

• Simulating and analysing spatiotemporal structure, characteristics and flows in HIPS and neural data;

• Statistical analysis and pattern recognition in neuroimaging.

Track 3: Brain-Inspired Technologies, Systems and Applications

• Affective computing and applications;

• Brain/Cognition inspired computing and artificial systems;

• Brain-computer interfaces and brain-robot interfaces;

• Brains connecting to the Internet of Things;

• Brain repair models and simulations;

• Clinical diagnosis and pathology of brain and mind/mental-related diseases (e.g., mild cognitive impairment, alzheimers, dementia & neuro-degeneration, depression, epilepsy, autism, Parkinson's disease, and cerebral palsy);

• Cloud and semantic brain data services;

• Cognitive and decision support;

• Computational approaches to rehabilitation;

• Computational intelligence methodologies for mental healthcare;

• Computational psychiatry;

• Digital, data, and computational brain;

• e-Science, e-Health, and e-Medicine;

• Mental healthcare knowledge abstraction, classification, representation, and summarization;

• Mental healthcare knowledge computerization, execution, inference, and management;

• Mental health risk evaluation and modeling;

• Neuro/Emotional robotics, computer vision and intelligent robotic networks;

• Neuroeconomics and neuromarketing;

• Neuroeducation, neurolinguistics, and neuroinstrumentation;

• New cognitive and computational models of intelligent systems;

• Non-verbal communication;

• Personal, wearable, ubiquitous, micro and nano devices for mental healthcare;

• Remote neurological assessment;

• Social networks, social media, and e-learning for spreading mental health awareness;

• WaaS (Wisdom as a Service) and active services for mental healthcare.