The 2018 International Conference on Brain Informatics (BI 2018)

Keynotes

Arthur Toga

Arthur Toga

Provost Professor of Ophthalmology, Neurology, Psychiatry and Behavioral Sciences, Radiology and Engineering
Director of the Mark and Mary Stevens Neuroimaging and Informatics Institute
Director of USC Laboratory of Neuro Imaging (LONI)
University of Southern California, Los Angeles, California, USA
Title: Neuroimaging and Informatics in Alzheimer's Disease Research

Abstract: The complexity of neurodegenerative and psychiatric diseases often requires the collection of numerous data types from multiple modalities. These can be genetic, imaging, clinical and biosample data. In combination, they can provide biomarkers critical to chart the progression of the disease and to measure the efficacy of therapeutic intervention.

Mapping the human brain, and the brains of other species, has long been hampered by the fact that there is substantial variance in both the structure and function of this organ among individuals within a species. There are numerous probabilistic atlases that describe specific subpopulations, measure their variability and characterize the structural differences between them. Utilizing data from structural, functional, diffusion MRI, along with gwas studies and clinical measures we have built atlases with defined coordinate systems creating a framework for mapping and relating diverse data across studies. A specific and important example of mapping multimodal data is the study of Alzheimer's. The dynamic changes that occur in brain structure and function throughout life make the study of degenerative disorders of the aged difficult. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a large national consortia established to collect, longitudinally, distributed and well described cohorts of age matched normals, mci's and Alzheimer's patients. The consequences of this disease can be seen using a variety of imaging (including structural, diffusion and amyloid) and other data analyzed from the ADNI database.

Essential elements in performing this type of population based research are the informatics infrastructure to assemble, describe, disseminate and mine data collections along with computational resources necessary for large scale processing of big data such as whole genome sequence data and imaging data. This talk also describes the methods we have employed to address these challenges.

Biography: Arthur W. Toga, Ph.D., is Provost Professor of Ophthalmology, Neurology, Psychiatry and the Behavioral Sciences, Radiology and Biomedical Engineering, and Director of the USC Institute of Neuroimaging and Informatics. One of the world''s leading authorities on neuroimaging, informatics, mapping brain structure and function, and brain atlasing, Dr. Toga came to USC September 1, 2013, from UCLA, where he was Distinguished Professor of Neurology, David Geffen Chair in Informatics and University Professor. His interdisciplinary work led to the creation of the Laboratory of Neuro Imaging (LONI), which is one of the most advanced multidisciplinary neurological research centers in the world serving numerous multisite neuroscience projects from around the world. Funded by National Institutes of Health (NIH) grants as well as industry partners, LONI houses one of the largest computing facilities and largest brain image repository in the world.

He is an author or co-author of more than 750 peer-reviewed papers, 1000 abstracts and 80 book chapters or books, among them Brain Mapping: The Methods. He is the founding editor of the journal NeuroImage. Dr. Toga has received numerous awards for his research and teaching, including the Pioneer in Medicine Award, Smithsonian Award for Scientific Innovation and Giovanni DiChiro Award for Outstanding Scientific Research. He holds the Ghada Irani chair in Neuroscience and has been on the Thomson Reuters' Highly Cited Researchers for many years.

Guoming Luan

Guoming Luan

Professor and Chief Physician in Neurosurgery
President of Chinese Neuromodulation Society
Director of Beijing Key Laboratory of Epilepsy
Director of Beijing Institute for Brain Disorders,
Capital Medical University, Beijing, China
Title: How to Understand Our Brain and Intelligence: From Epileptic Network to Brain-Computer Interface

Abstract: As more and more attention has been paid to understanding protecting cognitive functions in neurological disease, traditional neural imaging technologies can no longer meet the needs of individual decision of neurosurgery. Multimodal brain imaging methods in epilepsy diagnose has made it to be the best model to understand our perception, cognition and intelligence. By using the techniques of intracranial recording the single neuron and neuronal population of the cerebral cortex directly, and combining with MEG/EEG/MRI to construct the connection network, we have tried to establish the functional network of the human brain while elucidating the characteristics of the epileptic network. Our findings not only carry out the precise neuromodulation technique to realize the accurate diagnosis and treatment, but also inspire the algorithm of brain-computer interface.

Biography: Professor Guoming Luan is the Chief Expert and Director of Functional Neurosurgery and Epilepsy Center of Sanbo Brain Hospital, Capital Medical University. He achieved the MD in 1982 and Ph. D in Capital Medical University in 1989. He gained professional fellow experience for two years in the University of California, Los Angeles (UCLA), Afterwards he devotes himself to clinical and research work about Functional Neurosurgery and mainly focuses on the therapy of Epilepsy. He had operated over 8 thousands of patients over 30 years.

He is currently chief-editor and editor for more than 9 medical journals including 'Neuromodulation: Technology at the Neural Interface', 'The Chinese Journal of Stereotactic and Functional Neurosurgery' and 'The Chinese Journal of Minimal Invasive Neurosurgery'. More than 200 articles that correspond to him have been published both in national and international journals. Furthermore, he has taken charge for more than 30 national scientific research programs.

Chris Eliasmith

Chris Eliasmith

Professor of Philosophy & Systems Design Engineering
Director, Centre for Theoretical Neuroscience
University of Waterloo, Ontario, Canada
Canada Research Chair in Theoretical Neuroscience
Royal Society of Canada, College of New Scholars
Title: Next Generation Hardware for Large-Scale Brain Modeling and AI Applications

Abstract: Large-scale brain modelling requires significant computational resources. New neuromorphic hardware is ideally suited to providing those resources at low power. I present recent advances in building the world's largest functional brain model, Spaun. I demonstrate how elements of that model have been mapped to newly available neuromorphic hardware, specifically the Loihi chip from Intel, and demonstrate orders of magnitude improvement in efficiency compared to CPU or GPU performance. I also describe how the algorithms exhibiting such efficiencies can be applied to practical AI problems.

Biography: Dr. Chris Eliasmith is the Director of the Centre for Theoretical Neuroscience (CTN) at the University of Waterloo. Dr Eliasmith holds the Canada Research Chair in Theoretical Neuroscience. His book, 'How to build a brain' (Oxford, 2013), describes the Semantic Pointer Architecture for constructing large-scale brain models. His team built what is currently the world's largest functional brain model, 'Spaun', which performs a variety of tasks while respecting anatomical and physiological data. This ground-breaking work was published in Science (November, 2012) and has been featured by CNN, BBC, Der Spiegel, Popular Science, National Geographic and CBC among many other media outlets, and was awarded the NSERC Polayni Prize for 2015 for the most outstanding advance in the natural sciences or engineering in Canada.

Andreas Tolias

Andreas Tolias

Professor of Neuroscience
Brown Foundation Endowed Chair of Neuroscience
Director, Center of Neuroscience and Artificial Intelligence (CNAI)
Baylor College of Medicine, Texas, USA
Title: The Fabric Of The Neocortex: A Less-Artificial Intelligence

Abstract: Over several decades, the quest to advance artificial intelligence (AI) has used different approaches such as symbolic reasoning, expert systems, statistics and neural networks. Recently, deep learning stirred a renaissance in AI by reaching human - or even superhuman - performance on several tasks. From the media and the gaming industry to the internet, mobile devices, autonomous machines, security and defense, deep learning is transforming industries at an accelerating rate. Deep learning networks have notable similarities to the brain, involving many layers, many neurons, and many plastic synapses that change with experience. Yet they differ significantly in important respects, lacking cell types, complex nonlinearities, pervasive feedback, structured connectivity, and local learning rules. The fact that the most successful artificial networks share such important features with the brain, yet have so many differences in their details, suggests that there are enormous opportunities to revolutionize machine learning and build next-generation AI systems by understanding and incorporating features derived from neuroscience into artificial neural networks. I will describe our ongoing experimental and computational efforts to decipher the algorithms of cortical microcircuits and how we are beginning to transfer these algorithms to advance machine learning.

Biography: Dr. Tolias studies how microcircuits in the cerebral cortex of mice and non-human primates are functionally organized and how they process information. Research in his lab combines electrophysiological (whole- cell and multi-electrode extracellular), multi-photon imaging, molecular, behavioral and computational methods. His goal is to dissect and understand the functional organization of neocortical microcircuits and decipher their structure and the canonical computations they perform with an emphasis on the role top-down influences in visual processing. In parallel his research team is also trying to apply these canonical computation principles in machine learning tasks in order to advance the field of artificial intelligence.

Leon Iasemidis

Leon Iasemidis

PI, NeuroNEM, NSFs B.R.A.I.N. Initiative
Professor and the Rhodes Eminent Scholar Chair, Biomedical Engineering Program
Director, Center for Biomedical Engineering and Rehabilitation Science (CBERS)
Founder and Director, The Brain Dynamics Lab
Founder and Director, The EEG Lab
Louisiana Tech University, Ruston, Louisiana, USA
Title: Epilepsy as a Brain Dynamical Disorder

Abstract: Epilepsy is a dynamical disorder of the brain characterized by intermittent recurrence of seizures that have debilitating effects on brain's operation and are the result of acquired or hereditary insults to the brain. Despite the existence of a broad spectrum of anti-epilepict drugs (AEDs), fully 1/3 of the world's 60 million patients with epilepsy have uncontrollable seizures and life-threatening emergencies such as status epilepticus (SE), as well as sudden unexpected death in epilepsy (SUDEP), indicative of the need for better understanding of the disorder and subsequent development of new treatments for it. We will discuss novel information concepts and multi-modal spatio-temporal signal processing technologies that hold great promise for better understanding of the mechanisms and diagnosis of epilepsy, and the development and evaluation of the efficacy of new anti-epileptic treatments. Examples include improved identification of the epileptogenic focus from seizure-free periods, differential diagnosis (e.g. epilepsy vs. metabolic encephalopathy), real-time seizure prediction and closed-loop seizure control via neuromodulation, real-time evaluation of AEDs in emergency situations (e.g. SE), as well as susceptibility to SUDEP by shedding light on the impairment of the communication of the epileptic brain with other vital organs (e.g. the heart). Results from analysis of electrochemical recordings, long-term electroencephalographic (EEG), and short-term magnetoencephalographic (MEG) recordings, in simulation models, animal models and patients with epilepsy, will be presented.

Biography: Leon D. Iasemidis, Ph.D. (leonidas@latech.edu), is an endowed Professor and the Rhodes Eminent Scholar Chair of Biomedical Engineering, the Director of the Center for Biomedical Engineering and Rehabilitation Science (CBERS), the Founder and Director of the Brain Dynamics Laboratory and leads the NSF sponsored NeuroNEM (Neuronal Networks in Epilepsy and Memory) consortium (http://www.neuronem.latech.edu/) at Louisiana Tech University, Ruston, Louisiana, USA. Dr. Iasemidis is considered the founder of the modern field of seizure prediction together with Dr. James Chris Sackellares, MD, in 1990s, and has worked on many engineering aspects of epilepsy for diagnostic and treatment purposes including seizure prediction and susceptibility to seizures, seizure control and epileptogenic focus localization, the extreme conditions of status epilepticus (SE) and sudden unexpected death in epilepsy (SUDEP). He has collaborated with multiple university medical centers and industry, and his research has been funded over the years by major US state and national sponsors, including NIH, NSF, EFA and CURE foundations. Dr. Iasemidis has served on the editorial boards of Epilepsia, IEEE TBME and the Int. J. Neural Sys., and currently the Ann. Biomed. Eng. and Epilepsy Research. He co-founded NeuroVista Inc., is the founder of EpiFocus LLC and the co-inventor on more than 20 patents. His research has been highlighted on multiple forums, including the NY Times, Discover magazine, the Teaching Company, and AAAS. He has published more than 200 peer reviewed articles and has more than 6,500 citations to his work. Dr. Iasemidis is a Fellow of IEEE, the American Institute of Medical and Biological Engineers (AIMBE) and the National Academy of Inventors (NAI).

Daniel Levine

Daniel Levine

Professor of Psychology
Fellow and former President of International Neural Network Society
Department of Psychology
The University of Texas at Arlington, USA
Title: Neural Network Modeling and Brain-Inspired Cognitive Systems

Abstract: This talk will focus on neural network modeling as it is used to understand how the brain gives rise to mental processes. This intersects with, but is not identical to, the use of neural networks for engineering applications. I will summarize the history of the neural modeling field as outlined in my book and focus on those methods that seem to hold the most promise for unlocking the mysteries of brain-mind interactions. These methods are different from, and older than, some currently popular techniques such as deep learning and Bayesian modeling. Rather, they start from network solutions of the paradoxical requirements of cognitive and behavioral tasks and then integrate the ensuing networks with data from cognitive neuroscience.

Biography: Daniel Levine is Professor of Psychology at the University of Texas at Arlington. He is a Fellow and former President of the International Neural Network Society. He is the author of Introduction to Neural and Cognitive Modeling (3rd edition), published by Routledge. His research involves computational modeling of brain processes in decision-making and cognitive-emotional interactions.