Path Catalog
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正在从 AllPath API 加载 Path Catalog…
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。
共 7800 条 Path · 第 94 / 780 页
This learning path equips computational neuroscience and informatics students with the core concepts and practical skills for managing, analyzing, and sharing neural data. It covers data representation, databases, tools, standards, and sharing practices, emphasizing the FAIR principles and real-world workflows.
This advanced learning path equips computational neuroscience and computer science students with the knowledge and skills to apply machine learning to neuroscience data. It covers essential prerequisites in programming, mathematics, and neuroscience, followed by core ML techniques and their applications to neural data analysis.
A comprehensive learning path for computational neuroscience students to understand and model neural circuits, covering from single-neuron dynamics to network-level population models and circuit dynamics.
This learning path guides students through the essential physics and mathematics needed to understand and construct biophysical models of neurons. It covers the thermodynamics of ion channels, membrane biophysics, and the electrical circuit models that form the basis of computational neuroscience.
This path equips computational neuroscience students with the tools of information theory to analyze neural coding. It covers foundational probability and information measures, then applies them to quantify neural coding efficiency, redundancy, and population coding. The path emphasizes practical estimation and model-based approaches.
A structured learning path for computational neuroscience students to understand and apply dynamical systems concepts—phase plane analysis, stability, bifurcations, oscillations, and attractors—to neural models. The path builds from calculus foundations through core dynamical systems theory to practical applications in neural modeling.
This learning path guides computational neuroscience students through essential concepts and skills for analyzing neural data, from signal processing and spike sorting to tuning curves, dimensionality reduction, and statistical inference. It emphasizes practical computational methods and their theoretical foundations, preparing learners to work with neural recordings.
A systematic learning path for computational neuroscience students to understand models of synaptic plasticity. It covers the biological foundations, core Hebbian and STDP rules, their mathematical formulations, and extensions to reinforcement learning, culminating in practical modeling exercises.
This learning path introduces computational neuroscience students to the fundamental principles of neural coding. It covers how neurons represent information through spike rates and spike timing, how populations of neurons encode information collectively, and how decoding algorithms can extract that information. The path builds from basic neurophysiology and statistics to advanced population coding and decoding methods.
This learning path guides computational neuroscience students through the essential concepts and models of neural networks, from foundational mathematics to advanced recurrent and attractor networks. It covers feedforward networks, recurrent dynamics, and learning rules, providing a systematic understanding of how neural computations emerge from network architectures.