Dr. Pamela Douglas

Professor Computational Neuroscience in Los Angeles, CA

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Dr. Pamela Douglas's expertise has been featured in workshops and presentations, several on YouTube. She has collaborated with esteemed institutions and organizations such as the Biomedical Science Research and Training Centre, Cosyne Talks, the Institute for Pure & Applied Mathematics, and the National Institutes of Health Center for Multimodal Neuroimaging. Through these partnerships, she has contributed to the dissemination of cutting-edge research and the advancement of knowledge in neuroscience. Dr. Douglas's work expands our understanding of the brain and serves as a catalyst for progress in artificial intelligence, generative models, and functional MRI. Her speaking engagements and collaborations demonstrate her commitment to sharing insights, fostering interdisciplinary dialogue, and inspiring advancements in the scientific community. Pamela Douglas's interests lie in the fascinating realm of computational neuroscience, where she explores the brain's remarkable ability to organize itself and generate 1/f spectral rhythms that mirror the surrounding environment. Her research suggests that this mirroring function optimises learning capabilities while reducing the brain's energy requirements for modelling the environment.

Douglas has employed various empirical data collection methods throughout her career, including transcranial ultrasound, functional magnetic resonance imaging (fMRI), and electroencephalography (EEG). Combining these datasets, she creates cognitive computational models that enable a deeper understanding of brain function. Additionally, she applies pattern classification tools to evaluate representational patterns in fMRI data, utilizing decoding frameworks as parameters for analysis. In addition to her groundbreaking research, Dr. Pamela actively shares knowledge and advancements with her peers through speaking engagements and lectures. She has dedicated significant time to educating others on topics such as artificial intelligence, generative models, functional MRI, and other areas of neuroscience. Some notable speaking engagements she has participated in include "Explainable AI in Neuro-Imaging: Challenges and Future Directions" and "Beyond Linear Decoding: Introduction to Deep Learning Methods."