View source for BIH 2016

==Topics==

'''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.