spykes
  • Getting Started
    • What is Spykes?
    • Installing
      • Vanilla
      • Bleeding-Edge
      • Local Version
    • Datasets
  • Tutorials
    • Fitting Tuning Curves with Gradient Descent
      • Special Case 1: Poisson Generalized Linear Model (GLM)
      • Special Case 2: Generalized von Mises Model (GVM)
    • Minimizing Negative Log Likelihood with Gradient Descent
    • Decoding Feature from Population Activity
  • Examples Gallery
    • Neuropop Example
      • Create a NeuroPop object
      • Simulate a population of neurons
      • Split into training and testing sets
      • Fit the tuning curves with gradient descent
      • Predict the population activity with the fit tuning curves
      • Score the prediction
      • Plot the simulated and fit tuning curves
      • Decode feature from the population activity
      • Visualize ground truth vs. decoded estimates
      • Score decoding performance
    • PopVis Example
      • 0 Initialization
        • 0.1 Download Data
        • 0.2 Read In Data
        • 0.3 Initialize Variables
      • 1 PopVis
        • 1.1 Initiate all Neurons
        • 1.2 Get Event Times
        • 1.3 Create PopVis Object
    • CRCNS DataSet Example
      • 0 Overview: Reproduce Figure
        • 0.1 Article
        • 0.2 Dataset
      • 1 Data
        • 1.1 Download Data
        • 1.2 Load Data
      • 2 Get Spike Times
      • 3 Get Event Times
      • 4 Get Features
      • 5 Define Features
      • 6 Plots
        • 6.1 Rasters
        • 6.2 PSTH
        • 6.3 Reproduce Figure
        • 6.4 ggplot
    • Neuropixels Example
      • Neuropixels
      • 0 Download Data
      • 1 Read In Data
      • 2 Create Data Frame
      • 3 Start Plotting
        • 3.1 Striatum
        • 3.2 Frontal
        • 3.3 All Neurons
        • 3.4 Striatum vs. Motor Cortex
    • Neural Coding Reward Example
      • 0 Overview: Reproduce Figure
        • 0.1 Article
        • 0.2 Dataset
        • 0.3 Initialization
      • 1 First Graph of Panel A
        • 1.1 Initiate all Neurons
        • 1.2 Get Event Times
        • 1.3 Match Peak Velocities
        • 1.4 Plot PSTHs
      • 2 First Graph of Panel C
        • 2.1 Normalize PSTHs
        • 2.2 Find Population Average
        • 2.3 Plot PSTH
    • Reaching Dataset Example
      • Initialization
        • Download Reaching Dataset
      • Part I: NeuroVis
        • Instantiate Example PMd Neuron
        • Raster plot and PSTH aligned to target onset
        • Events
        • Features
        • Example 1: Reward vs No Reward
        • Example 2: according to quadrant of reaching direction
        • Example 3: Same as Example 2 but for an M1 neuron and aligned at goCueTime
        • Example 4: sorted by direction only for the trials with reward
      • Part II: NeuroPop
        • Extract reach direction x
        • Extract M1 spike counts Y
        • Split into train and test sets
        • Create an instance of NeuroPop
        • Predict firing rates
        • Score the prediction
        • Visualize tuning curves
        • Decode reach direction from population vector
        • Visualize decoded reach direction
        • Score decoding performance
  • Contributing
    • Guidelines
    • Testing
    • Building Documentation

API

  • Plotting
    • NeuroVis
    • PopVis
  • Machine Learning
    • NeuroPop
    • STRF
    • Sparse Filtering
    • Poisson Layers
  • Input / Output
    • Datasets
  • Config
  • Utils
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