Neuroscience Dataset Recommendation

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Related lectures (32)

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Neuroscience Data Analysis

Explores neuroscience data analysis, focusing on K+ and Na+ currents, Ca²+ currents, and data reproducibility.

In Silico Neuroscience: Data Reproducibility and Reusability

Emphasizes data reproducibility and reusability in in silico neuroscience, focusing on neuroinformatics tools and methods.

Neuroscience Data Analysis

Explores neuroscience data analysis, emphasizing structured data, computational tools, and the trend of computational neuroscience as a service.

The Microcircuits of Striatum in Silico

Explores the reconstruction of a full-scale mouse striatal cellular level model to integrate and interpret striatal data.

Computational Neuroscience: Biophysics & Modeling

Covers the fundamentals of computational neuroscience, focusing on biophysics and modeling.

Scientific Computing in Neuroscience

Explores the history and tools of scientific computing in neuroscience, emphasizing the simulation of neurons and networks.

Neurosciences I: Molecular Neuroscience and Neurodegeneration

Covers molecular neuroscience, neurodegeneration, cell types, neuron structure, and history of neuroscience breakthroughs.

Approaches and Rationale of Simulation Neuroscience

Explores the overview, rationale, and strategies of simulation neuroscience, emphasizing the challenges of reconstructing and simulating the brain.

Introduction to Neuroinformatics

Covers the heterogeneous neuroscience data, techniques like microarrays and gene sequencing, data integration, and the importance of metadata in organizing and sharing data.

Models and data

Covers the optimization of neuron models for coding and decoding in computational neuroscience.