Dr. Alex Johnson
Professor of Computational Neuroscience
Exploring the intersection of artificial intelligence and neural computation to understand the human brain.
Exploring the intersection of artificial intelligence and neural computation to understand the human brain.
I am a Professor of Computational Neuroscience at Stanford University, where I lead the Cognitive Computation Lab. My research focuses on developing computational models to understand neural processes in learning, memory, and decision-making.
With over 15 years of experience in both neuroscience and artificial intelligence, I've authored more than 80 peer-reviewed publications and have been recognized with multiple awards for my contributions to the field.
2020 - Present
2014 - 2020
Cambridge University, 2010
Exploring the frontiers of computational neuroscience through innovative research and cross-disciplinary collaboration.
Developing novel algorithms to interpret neural signals and understand how information is represented in the brain.
Learn MoreCreating biologically inspired neural network architectures that bridge neuroscience and artificial intelligence.
Learn MoreTranslating computational neuroscience research into diagnostic tools and therapeutic interventions.
Learn MoreDeveloping next-generation neural interface technologies to restore motor function in patients with paralysis.
Computational modeling of cognitive aging processes to identify early biomarkers of neurodegenerative diseases.
Developing AI systems that incorporate principles from neuroscience to create more efficient learning algorithms.
Computational investigation of memory modification mechanisms with potential for treating PTSD and anxiety disorders.
Johnson, A., et al. (2023). Uncovering the principles that allow neural circuits to learn complex tasks with minimal data.
Johnson, A., & Smith, R. (2022). Journal of Cognitive Neuroscience, 35(4), 678-694.
Johnson, A., et al. (2021). Trends in Neurosciences, 44(11), 890-905.
Johnson, A., Chen, L., & Williams, K. (2020). Neuron, 106(3), 495-508.
Sharing knowledge and fostering the next generation of computational neuroscientists.
Graduate course exploring neural computation, brain modeling, and neuro-AI interfaces.
Advanced methods for analyzing and interpreting complex neural datasets.
Research group for graduate students conducting cutting-edge computational neuroscience research.
Detailed course outline and reading list
Recorded lectures from previous semesters
Code examples and project templates
Requirements for graduate research projects
Interested in collaboration, research opportunities, or have questions? Feel free to reach out.