Path Catalog
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Path Catalog
列表数据实时取自 GET /api/v1/paths，仅包含存在已发布版本的 Path。
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共 7800 条 Path · 第 93 / 780 页
This path equips computational neuroscience students with the skills to analyze spike train data, covering essential neurophysiology, Python programming, and statistical methods. Learners progress from understanding action potentials to visualizing and quantifying spike patterns using raster plots, ISIs, and correlations.
This learning path equips neuroscience students with essential computational skills, covering programming, data analysis, modeling, and simulation. Starting with Python basics, it progresses through data handling, statistical analysis, and neural modeling, culminating in a capstone project.
This graduate-level path equips students to computationally model neural development, from molecular morphogenesis to network-level maturation. It builds foundations in neuroscience and computational methods, then progresses through gene regulation, morphogen gradients, axon guidance, synaptogenesis, activity-dependent plasticity, and network development. The path culminates in integrative projects modeling experience-dependent maturation.
This advanced graduate-level path equips computational neuroscience students with the skills to construct and analyze models of neuromodulatory systems. It covers foundational neurobiology, computational modeling techniques, and the dynamic effects of neuromodulators on neural circuits, culminating in the construction of a comprehensive model.
A graduate-level learning path for computational neuroscience students covering the mathematical and conceptual foundations of neural computation. It progresses from essential mathematical tools and single-neuron models to population dynamics, learning rules, and cognitive functions, emphasizing theoretical frameworks and their interconnections.
A comprehensive graduate-level learning path for applying computational methods to neuroimaging. It covers the core modalities (fMRI, EEG, MEG), signal processing, statistical analysis, connectivity estimation, and advanced modeling, with a strong emphasis on the underlying prerequisites in mathematics, programming, and neuroscience.
This graduate-level path guides computational neuroscience students through the essential concepts and methods for modeling motor systems. It covers foundational neuroscience and computational tools, progresses through motor control and learning theories, and culminates in building and evaluating computational models of movement.
A graduate-level learning path for computational neuroscience students to model sensory systems computationally. It progresses from foundational neuroscience and computational methods through detailed modeling of visual, auditory, and somatosensory systems, culminating in integrative and coding projects.
A comprehensive learning path for computational neuroscience students aiming to understand and build large-scale neural simulations. It covers essential computational neuroscience concepts, numerical methods, high-performance computing, and systems integration, culminating in the design and analysis of brain-scale models.
This learning path equips students with the knowledge and skills to apply computational models to cognitive processes, focusing on decision making, memory, and attention. It covers foundational concepts in cognitive science and computational modeling, then progresses to advanced modeling frameworks and practical applications.