The human brain is a complex information processing system and is currently the topic of multiple fascinating branches of research. Understanding how it works is a very challenging scientific task. In recent decades, multiple techniques for imaging the activity of the brain at work have been invented, which has allowed the field of cognitive neuroscience to flourish. Cognitive neuroscience is concerned with studying the neural mechanisms underlying various aspects of cognition, by relating the activity in the brain to the tasks being performed by it. This typically requires exciting collaborations with other disciplines (e.g. psychology, biology, physics, computer science).
Students who are interested in how the brain works and how cutting-edge brain-imaging and data-analysis tools are used to study it should take this course. During this course, students will learn tools based on the python programming language to understand, manipulate, and explore human brain recordings (such as ECoG, EEG, MEG and fMRI). Students will learn to formulate hypotheses about how the brain represents information and then test these hypotheses using real world data. Students will learn useful analysis methods to derive conclusions from brain recording data.
By giving first hand experience in data analysis of brain data, this course will provide an insight into the experiments and data used in the cognitive neuroscience field. It will allow the building of a better understanding of the current cutting edge research in cognitive neuroscience. Hence, students will be able to keep up with recent advances in this field and/or will be able to apply knowledge by doing research here at Berkeley. Additionally, the data analysis techniques and the investigation approaches that students will learn will be easily transferable to research in other disciplines.
Fatma Deniz (née Imamoglu) is a joint postdoctoral researcher at the Gallant Lab in UC Berkeley’s Helen Wills Neuroscience Institute and the International Computer Science Institute. She is interested in how sensory information is encoded in the brain and uses machine learning approaches to fit computational models to large-scale brain data acquired using functional magnetic resonance imaging (fMRI). Fatma works at the intersection between computer science, linguistics, music, and neuroscience. Her current focus is on the cross-modal representation of language in the human brain. In addition, she works on improving internet security applications using knowledge gained from cognitive neuroscience (MooneyAuth Project). She is an enthusiastic teacher for Berkeley's data-8 connector course Data Science for Cognitive Neuroscience (Fall2016 and Spring2017) and an instroctor in Software Carpentry, where she teaches scientific computing. As an advocate of reproducible research practices she is the co-editor of the book titled “The Practice of Reproducible Research”. As a data science fellow, she is interested in teaching and reproducible research and sees herself as a connector between diverse domains. She is a passionate coder, runner, baker, and cello player.
Chris Holdgraf is a Data Science Fellow at the Berkeley Institute for Data Science and a Community Architect at the Data Science Education Program at UC Berkeley. His background is in cognitive and computational neuroscience, where he used predictive models to understand the auditory system in the human brain. He's interested in the boundary between technology, open-source software, and scientific workflows, as well as creating new pathways for this kind of work in science and the academy. He's a core member of Project Jupyter, specifically working with JupyterHub and Binder, two open-source projects that make it easier for researchers and educators to do their work in the cloud. He works on these core tools, along with research and educational projects that use these tools at Berkeley and in the broader open science community.