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A graduate-level learning path bridging physics and computational neuroscience. It covers the mathematical and physical foundations, neural modeling, network dynamics, and advanced topics in statistical physics, nonlinear dynamics, and quantum biology, culminating in a capstone project.
This advanced graduate-level path systematically explores the intersection of machine learning and neuroscience. It begins with foundational concepts in both fields, then builds toward understanding how neural systems inspire learning algorithms and how ML models serve as tools for understanding cognition and brain function.
This advanced graduate-level path equips computational neuroscience students with the conceptual and mathematical toolkit to apply predictive models—ranging from predictive coding to Bayesian and active inference—to neural data and models of brain function. It builds from foundational probability and neurobiology through formal frameworks to practical applications, culminating in a project that integrates these concepts.
This advanced graduate-level learning path equips computational neuroscience students with the theoretical and practical knowledge to model and analyze brain networks. It covers graph theory foundations, neuroimaging and tractography data, structural and functional connectivity, dynamic network models, and applications to brain disorders and cognition.
This advanced graduate-level path equips computational neuroscience and computer science students with the knowledge to apply deep learning to neuroscience questions. It covers foundational machine learning, neural network architectures, and their applications to neural data analysis and brain modeling, emphasizing rigorous evaluation and interpretation.
This graduate-level path equips computational neuroscience and engineering students with the knowledge to design and evaluate computational BCI systems. It covers neural signal acquisition, preprocessing, feature extraction, classification/decoding, and real-time processing, grounded in the necessary neurophysiology and signal processing fundamentals.
This advanced graduate learning path guides computational neuroscience and engineering students through the foundational principles and practical implementations of neuromorphic computing. It covers neural dynamics, spiking neural networks, learning algorithms, and the hardware substrates that emulate them, culminating in the analysis of contemporary neuromorphic chips and systems.
This advanced graduate-level learning path equips computational neuroscience students with the skills to fit and validate computational models. It covers statistical foundations, parameter estimation, cross-validation, and model selection, with a focus on neural data.
This learning path equips computational neuroscience students with the knowledge and skills to analyze neuroimaging data, covering MRI physics, preprocessing, statistical analysis, connectivity, and visualization. It progresses from foundational concepts to advanced application, ensuring a solid understanding of the entire analysis pipeline.
This learning path equips computational neuroscience students with the knowledge and skills to simulate neural systems at scale. It covers the mathematical foundations of neuron models, the use of simulators like NEURON and NEST, and the parallel computing techniques required for large-scale simulations. The path progresses from single-neuron dynamics to network-level simulations, culminating in hands-on projects that address real-world computational challenges.