Atika Syeda
Neural responses in the visual cortex are always active, even in the dark, and this variability was often treated as noise. In my project, I developed Facemap, which is a machine-learning tool that analyzes high-speed videos to precisely track mouse orofacial movements and uses the temporal patterns of these behaviors to predict neural activity. By directly measuring the animal’s ongoing behaviors, Facemap allows us to separate behavior-related neural activity from activity driven by visual stimuli. Orofacial behavioral signals explained a large portion of the variability in visual cortical activity, more than eye movements. This shows that much of the “noise” in neural recordings reflects the animal’s behavior, emphasizing the need to measure behavior to better understand sensory processing in the brain. This work was done in Stringer lab in collaboration with Pachitariu lab at Janelia Research Campus.
Questions & Answers
Why did you choose Johns Hopkins for your work? Hopkins provides a great opportunity to work in a lab at Janelia Research Campus while also being able to do coursework at Hopkins in the first year of the Ph.D. program. The coursework provides exposure to different areas of neuroscience, such as developmental and systems research. The wide variety of topics provides valuable training for addressing questions in neuroscience, which is highly interdisciplinary.
What does receiving this award mean to you personally and professionally? Do you have any connection with the particular award you received? The award recognizes my Ph.D. work, which involved addressing key questions about mouse visual systems by combining large-scale behavioral and neural data analyses using machine learning. Professionally, the award is valuable for recognizing work at the intersection of experimental neuroscience and machine learning, which has great potential to answer further questions in the field. Personally, I really appreciate the encouragement at this stage, which motivates me to pursue scientific research in the field of neuroscience and continue to develop my skills in machine learning. In addition, I am very grateful for the support from my parents, who are very happy about this accomplishment.
What contributed to your project’s success? (Special skills, interests, opportunities, guidance, etc.) My skill set in developing machine-learning models for neuroscience datasets was key for answering questions in the project. My lab’s emphasis on collecting good quality behavioral data and developing tools for analyzing it was also important for the project. My interest lies in working closely with experimentalists to understand all stages of data collection, which is useful for designing analysis pipelines.
What thoughts do you have about Young Investigators’ Day itself, as a celebration of the roles students and fellows play in research at Johns Hopkins? I’m excited to meet other awardees and learn about their research. I always enjoy connecting with people from different backgrounds and sharing my research. It will be a great platform to get feedback on my current and future research plans.
What has been your best/most memorable experience while at Johns Hopkins? Neuroscience department retreats where the whole department comes together to share their work and just hang out have been the most memorable.
What are your plans over the next year or so? Graduating, looking for faculty positions, etc.? I am looking toward graduation over the next year and looking for opportunities to do similar research as an independent fellow while collaborating with systems neuroscience labs.
Tell me something interesting about yourself that makes you unique. Do you have any special hobbies, interests or life experiences? My hobbies are running, cooking, hiking, plants and anything nature-related. I have lived in five different countries, mostly moving for education and research opportunities. Experiencing different cultures and academic environments has shaped my work and created a desire to continue to explore new places and cultures.