Call for Papers

Brain Informatics (BI) started the exploration as a research field with the vision of investigating the brain from the informatics perspective. Firstly, Brain Informatics combines the efforts of Cognitive Science, Neuroscience, Machine Learning, Big Data Analytics, AI and ICT to study the brain as a general information processing system. Secondly, new measurements and informatics equipment, tools and platforms are causing impending revolution to understand the brain. Thirdly, staring from its proposal as a field, Brain Informatics is with the goal of inspiring future Artificial Intelligence, especially Web Intelligence.

BI’17 addresses the computational, cognitive, physiological, biological, physical, ecological and social perspectives of brain informatics, as well as topics relating to mental health and well-being. It also welcomes emerging information technologies, including but not limited to Internet/Web of Things (IoT/WoT), cloud computing, big data analytics and interactive knowledge discovery related to brain research. BI’17 also encourages submissions that explore how advanced computing technologies are applied to and make a difference in various large-scale brain studies and their applications.

BI'17 welcomes paper submissions (full paper and abstract submissions). Both research and application papers are solicited. All submitted papers will be reviewed on the basis of technical quality, relevance, significance and clarity. Accepted full papers will be included in the proceedings by Springer LNCS/LNAI.

Tutorial, Workshop and Special Session proposals and Industry/Demo-Track papers are also welcome.

Paper Submission:

Main Conference paper submission: HERE.

Workshop and Special Session papers submission: HERE.

Main conference papers, Workshop and Special Session papers will be published in the same proceedings at LNCS/LNAI Series, Springer.

Download the call for papers HERE.

Topics and Areas

Track 1: Cognitive and Computational Foundations of Brain Science

• Brain dynamics;
• Structural and functional connectome;
• Neural foundations of intelligent behavior;
• Learning mechanisms (e.g., stability, personalized user/student models);
• Multi-perception mechanisms and visual, auditory, and tactile information processing;
• Reasoning mechanisms (e.g., principles of deductive/inductive reasoning, common-sense reasoning, decision making, and problem solving);
• Neural basis of decision-making;
• Models of executive function & prefrontal cortex;
• Higher-order cognitive functions and their relationships;
• Adaptation and self-organization;
• Digital, data, and computational brain;
• Methodologies for systematic design of cognitive experiments;
• Neuroeconomics and neuromarketing;
• Cloud and semantic brain data services.

Track 2: Human Information Processing Systems (HIPS)

• Bayesian models of the brain, and causal modeling of behaviour for neurology;
• 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;
• HIPS complex systems;
• Investigating spatiotemporal characteristics and flow in HIPS and the related neural structures and neurobiological process;
• Modeling brain information processing mechanisms (e.g., information organization, neuro mechanism, mathematical, cognitive and computational models of HIPS);
• Social brain communication.

Track 3: Brain Big Data Analytics, Curation and Management

• Big-neuron, neuron morphology and neuron reconstruction
• 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 behavioral 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 analyzing spatiotemporal structure, characteristics and flows in HIPS and neural data;
• Statistical analysis and pattern recognition in neuroimaging.
• Cloud and semantic brain data services.

Track 4: Informatics Paradigms for Brain and Mental Health

• 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;
• 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.
• Computational approaches to rehabilitation;
• Computational intelligence methodologies for mental healthcare;
• Computational psychiatry;
• Brain repair models and stimulations;
• 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).

Track 5: Brain-Inspired Intelligence and Computing

• Brain-inspired Artificial Intelligence;
• Brain-inspired Cognitive Computation and Modeling;
• Brain-inspired Artificial Neural Networks<;/li>
• Brain-inspired Information Processing;
• Brain-inspired Evolutionary Systems;
• Brain-inspired Machine Learning;
• Brain-inspired / Cognitive Neuro Robotics;
• Brain-inspired / Neuromorphic Computing;
• Affective computing and applications;
• Brain-computer interaction and brain-robot interaction;
• Brains connecting to the Internet of Things.

Paper Submissions & Publications

Type I: Full Paper Submissions

Papers need to have up to 10 pages in LNCS format.

All full length papers accepted (and all special sessions' full length papers) will be published by Springer as a volume of the series of LNCS/LNAI.

Type II: Abstract Submissions

Abstracts have a word limit of 500 words. Experimental research is particularly welcome. Accepted abstract submissions will be included in the conference program, and will be published as a single, collective proceedings volume.

Title: Include in the title of the abstract all words critical for a subject index. Write your title in sentence case (first letter is capitalized; remaining letters are lower case). Do not bold or italicize your full title.

Author: List all authors who contributed to the work discussed in the abstract. The presenting author must be listed in the first author slot of the list. Be prepared to submit contact information as well as conflict of interest information for each author listed.

Abstract: Enter the body of the abstract and attach any applicable graphic files or tables here. Do not re-enter the title, author, support, or other information that is collected in other steps of the submission form.

Presentation Preference: Authors may select from three presentation formats when submitting an abstract: "poster only", "talk preferred" or "no preference." The "talk preferred" selection indicates that you would like to give a talk, but will accept a poster format if necessary. Marking "poster only" indicates that you would not like to be considered for an oral-presentation session. Selecting "no preference" indicates the author's willingness to be placed in the best format for the program.

Each paper or abstract requires one sponsoring attendee (i.e. someone who registered and is attending the conference). A single attendee can not sponsor more than two abstracts or papers.

Oral presentations will be selected from both full length papers and abstracts.