Logo Dark Logo ANDA-NI Summer School
CTRL K
    • Light

    • Dark

    • System

    CTRL K
      • Course Catalog
        • Essential Computing Tools for Scientists
          • Environment Management
            • Environment Management with Pixi
            • Environment Management with Conda
          • Interactive Data Analysis with Jupyter Data Science Notebooks
            • Interactive Coding with Jupyter Notebooks
          • Version Control with Git and GitHub
            • Version Control with Git
            • Remote Repositories with GitHub
            • Exploring and Collaborating by Editing Your Git History
        • Crash Course on Python
          • From Adding Numbers to Analyzing Real Data in Python
            • Intro to Python and Numpy
            • Array Programming in Numpy
            • Analyzing Tabular Data With Pandas
            • Data Visualization with Matplotlib and Seaborn
            • Statistics with Pingouin
        • Advanced Neural Data Analysis (ANDA)
          • Spike Train Correlations: Measuring Neural Interactions in the Brain
            • Simulating Spike Trains as Poisson Processes
            • Cross-Correlation Histograms of Neuronal Spike Trains and Surrogate Methods for Null-Hypothesis Testing
            • Unitary Event Analysis (UAE) and SPADE
          • State Space Analysis
            • Binary Population Codes
            • The Ising Model of Neural Populations
            • Dynamic Correlations with State-Space Smoothing
          • Dimensionality Reduction
            • PCA for Feature Extraction in Spike Sorting
            • Dimensionality Reduction on Neural Population Activity
            • Probabilistic Dimensionality Reduction with PPCA, FA, and GPFA
          • Cortical Variability Dynamics
            • Simulating Neurons as Poisson Processes
            • Spike Train Rasters and Firing Irregularity
            • Trial-by-Trial Variability and Firing Irregularity
          • Spectral Analysis
            • Power Spectra and Time-Frequency Analysis
            • Spectral Analysis Practice
            • Spectral Coherence and Phase Locking
        • Neuroinformatics (NI)
          • Code and Data Repositories
            • Online Repositories for Code and Data
            • Version Control with Git
            • Working with a DataLad Dataset
            • Running Commands while Tracking Provenance
          • Data Models
            • Numpy and Xarray
            • Organizing Electrophysiology Data
            • Representing Electrophysiological Data with Neo
          • Data Formats
            • Structured Scientific Data with HDF5: Design, Access, and Compression
            • Working with NWB Files using h5py and pynwb
            • Create NWB files with pynwb
            • Extra 1: Understanding and Controlling Memory Usage in Numpy
            • Extra 2: Data Representation and Disk IO: Performance Beyond RAM
          • Data Pipelines
            • Storing and Extracting Metadata From Files: Converting Lists and Dicts into JSON and YAML
            • Static Workflows with Snakemake
            • Generalizing Workflows with Wildcards
      • Essential Computing Tools for Scientists
        • Environment Management
          • Environment Management with Pixi
          • Environment Management with Conda
        • Interactive Data Analysis with Jupyter Data Science Notebooks
          • Interactive Coding with Jupyter Notebooks
        • Version Control with Git and GitHub
          • Version Control with Git
          • Remote Repositories with GitHub
          • Exploring and Collaborating by Editing Your Git History
      • Crash Course on Python
        • From Adding Numbers to Analyzing Real Data in Python
          • Intro to Python and Numpy
          • Array Programming in Numpy
          • Analyzing Tabular Data With Pandas
          • Data Visualization with Matplotlib and Seaborn
          • Statistics with Pingouin
      • Advanced Neural Data Analysis (ANDA)
        • Spike Train Correlations: Measuring Neural Interactions in the Brain
          • Simulating Spike Trains as Poisson Processes
          • Cross-Correlation Histograms of Neuronal Spike Trains and Surrogate Methods for Null-Hypothesis Testing
          • Unitary Event Analysis (UAE) and SPADE
        • State Space Analysis
          • Binary Population Codes
          • The Ising Model of Neural Populations
          • Dynamic Correlations with State-Space Smoothing
        • Dimensionality Reduction
          • PCA for Feature Extraction in Spike Sorting
          • Dimensionality Reduction on Neural Population Activity
          • Probabilistic Dimensionality Reduction with PPCA, FA, and GPFA
        • Cortical Variability Dynamics
          • Simulating Neurons as Poisson Processes
          • Spike Train Rasters and Firing Irregularity
          • Trial-by-Trial Variability and Firing Irregularity
        • Spectral Analysis
          • Power Spectra and Time-Frequency Analysis
          • Spectral Analysis Practice
          • Spectral Coherence and Phase Locking
      • Neuroinformatics (NI)
        • Code and Data Repositories
          • Online Repositories for Code and Data
          • Version Control with Git
          • Working with a DataLad Dataset
          • Running Commands while Tracking Provenance
        • Data Models
          • Numpy and Xarray
          • Organizing Electrophysiology Data
          • Representing Electrophysiological Data with Neo
        • Data Formats
          • Structured Scientific Data with HDF5: Design, Access, and Compression
          • Working with NWB Files using h5py and pynwb
          • Create NWB files with pynwb
          • Extra 1: Understanding and Controlling Memory Usage in Numpy
          • Extra 2: Data Representation and Disk IO: Performance Beyond RAM
        • Data Pipelines
          • Storing and Extracting Metadata From Files: Converting Lists and Dicts into JSON and YAML
          • Static Workflows with Snakemake
          • Generalizing Workflows with Wildcards
      • Light

      • Dark

      • System

      Course Catalog

      Local Image Essential Computing Tools for Scientists
      Tools for reproducible computational research: VSCode and Jupyter for interactive coding, Conda and Pixi for environment management, and Git and GitHub for version control and collaboration.
      Local Image Crash Course on Python
      Compact one-day course covering data analysis with Numpy and Pandas, visualization with Matplotlib, and statistical tests using real neuroscience data.
      Local Image Advanced Neural Data Analysis (ANDA)
      Analysis techniques for high-dimensional electrophysiology data. Learn state-of-the-art methods to analyze recordings from hundreds of neurons during complex behaviors—essential for modern systems neuroscience.
      Local Image Neuroinformatics (NI)
      Tools and Techniques for Data Management and Processing in Neuroscience