征集投稿

脑信息学 (Brain Informatica，BI) 作为一个新的研究领域，在信息学的视角之下开启了对大脑的研究。首先，脑信息学结合了认知科学，神经科学，机器学习，大数据分析，人工智能 （AI），信息通信技术 （ICT）等领域的知识和技术，将大脑视为通用信息加工系统并进行研究。其次，新的测量手段、设备、工具以及平台的出现正在促成一场认知大脑的新革命。再次，从脑信息学作为新的研究领域被提出之初，其目标就被设定为启发未来的人工智能，尤其是网络智能 （Web Intelligence，WI）。

BI’17将以计算、认知、生理、仿真、物理、生态和社会等脑信息学视角解读大脑，并关注与精神健康以及幸福有关的主题。大会也欢迎从事于与脑研究相关的新兴信息技术领域的专家参会，这些领域包括 （但不仅限于）物联网（IoT/WoT）、云计算、大数据分析以及交互式知识发现。BI’17也同样鼓励那些探索如何将高级计算技术用于多样性大尺度环境下脑研究与应用的专家学者把他们具有创造性的研究成果总结成论文 （或摘要）向大会投稿。

BI'17欢迎论文全文与摘要两种形式的投稿。稿件需用标准英文写作，研究型与应用型均可。所有的投稿都将经过专家审阅。专家将对稿件的技术性、关联性、重要性和清晰度进行评价，并决定是否接收稿件。被接收的论文全文将被收录入大会论文集，并由Springer LNCS/LNAI正式出版（支持EI检索）。

大会同时欢迎专家学者提交申请，组织自己的Tutorial，Workshop或Special Session，并征集Industry/Demo-Track的论文。

会议投稿：目前投稿系统已开放，投稿请请点击 此处.

下载论文征集宣传页请点击 此处.

主题与领域

Track 1: 脑科学的认知计算基础

• 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: 人脑信息加工系统 (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: 脑大数据的分析，策展，与管理

• 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: 脑与精神健康研究的信息学范式

• 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: 类脑智能与计算

• 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.

会议投稿与出版

论文全文投稿

论文篇幅不得超过10页，并按照 LNCS格式来写作。

所有被接收的论文全文（以及被special sessions接收的论文全文）将作为LNCS/LNAI系列的一卷被Springer出版

摘要投稿

摘要投稿字数不得超过500字，最好为实验研究。被接收的摘要投稿将被收录入大会程序册以及单独刊印的大会摘要集。

标题： 包含作为主题索引的所有关键词。把标题写为句子形式（第一个字母大写，其余全部小写）。不要把标题加粗或变为斜体。

作者： 列出所有对研究有贡献的作者。对摘要进行发表的作者必须排在首位。提供每一位作者的联系信息以及作者间是否存在利益冲突的信息。

摘要: 只能输入摘要的主体内容。不要重复输入标题、作者、基金支持、或者其他已在别的步骤填写过的信息。

发表的形式: 作者可从三个选项中选择自己喜好的发表形式：“只张贴海报”，“口头发表”，或“前两种任意”。选择“口头发表”意味着作者更愿意使用PPT和口头发表的形式对摘要中有关研究成果进行报告，但是如有必要也可接受张贴海报的形式。选择“只张贴海报”意味着作者不考虑在会议中口头报告研究成果。选择“前两种任意”意味着作者愿意配合会议的程序安排，并接受任意一种发表形式。

每篇投稿的论文或摘要都需要有至少一名注册（成功付费）并参会的作者。一位参会者不能注册超过两篇的摘要或论文。

口头发表者将从论文全文和摘要的作者中选出。