logo

Neuromatch Academy: Computational Neuroscience
  • Introduction
  • Schedule
    • General schedule
    • Shared calendars
    • Timezone widget
  • Technical Help
    • Using jupyterbook
      • Using Google Colab
      • Using Kaggle
    • Using discord
  • Quick links and policies
  • Prerequisites and preparatory materials for NMA Computational Neuroscience

Pre-reqs Refresher

  • Neuro Video Series (W0D0)
    • Intro
    • Human Psychophysics
    • Behavioral Readout
    • Live in Lab
    • Brain Signals: Spiking Activity
    • Brain Signals: LFP
    • Brain Signals: EEG & MEG
    • Brain Signals: fMRI
    • Brain Signals: Calcium Imaging
    • Stimulus Representation
    • Neurotransmitters
    • Neurons to Consciousness
  • Python Workshop 1 (W0D1)
    • Tutorial: LIF Neuron Part I
  • Python Workshop 2 (W0D2)
    • Tutorial 1: LIF Neuron Part II
  • Linear Algebra (W0D3)
    • Tutorial 1: Vectors
    • Tutorial 2: Matrices
    • Bonus Tutorial: Discrete Dynamical Systems
    • Outro
    • Day Summary
  • Calculus (W0D4)
    • Tutorial 1: Differentiation and Integration
    • Tutorial 2: Differential Equations
    • Tutorial 3: Numerical Methods
    • Day Summary
  • Statistics (W0D5)
    • Tutorial 1: Probability Distributions
    • Tutorial 2: Statistical Inference
    • Outro
    • Day Summary

Intro to Modeling

  • Model Types (W1D1)
    • Intro
    • Tutorial 1: “What” models
    • Tutorial 2: “How” models
    • Tutorial 3: “Why” models
    • Tutorial 4: Model Discussions
    • Outro
    • Suggested further readings
    • Day Summary
  • Modeling Practice (W2D1)
    • Intro
    • Tutorial 1: Framing the Question
    • Outro
    • Day Summary

Machine Learning

  • Model Fitting (W1D2)
    • Intro
    • Tutorial 1: Linear regression with MSE
    • Tutorial 2: Linear regression with MLE
    • Tutorial 3: Confidence intervals and bootstrapping
    • Tutorial 4: Multiple linear regression and polynomial regression
    • Tutorial 5: Model Selection: Bias-variance trade-off
    • Tutorial 6: Model Selection: Cross-validation
    • Outro
    • Suggested further readings
    • Day Summary
  • Generalized Linear Models (W1D3)
    • Intro
    • Tutorial 1: GLMs for Encoding
    • Tutorial 2: Classifiers and regularizers
    • Outro
    • Suggested further readings
    • Day Summary
  • Dimensionality Reduction (W1D4)
    • Intro
    • Tutorial 1: Geometric view of data
    • Tutorial 2: Principal Component Analysis
    • Tutorial 3: Dimensionality Reduction & Reconstruction
    • Tutorial 4: Nonlinear Dimensionality Reduction
    • Outro
    • Suggested further readings
    • Day Summary
  • Deep Learning (W1D5)
    • Intro
    • Tutorial 1: Decoding Neural Responses
    • Tutorial 2: Convolutional Neural Networks
    • Tutorial 3: Building and Evaluating Normative Encoding Models
    • Bonus Tutorial: Diving Deeper into Decoding & Encoding
    • Outro
    • Suggested further readings
    • Day Summary
  • Autoencoders (Bonus)
    • Intro
    • Tutorial 1: Intro to Autoencoders
    • Tutorial 2: Autoencoder extensions
    • Tutorial 3: Autoencoders applications
    • Outro
  • Machine Learning Wrap-Up

Dynamical Systems

  • Linear Systems (W2D2)
    • Intro
    • Tutorial 1: Linear dynamical systems
    • Tutorial 2: Markov Processes
    • Tutorial 3: Combining determinism and stochasticity
    • Tutorial 4: Autoregressive models
    • Outro
    • Suggested further readings
    • Day Summary
  • Biological Neuron Models (W2D3)
    • Intro
    • Tutorial 1: The Leaky Integrate-and-Fire (LIF) Neuron Model
    • Tutorial 2: Effects of Input Correlation
    • Tutorial 3: Synaptic transmission - Models of static and dynamic synapses
    • Bonus Tutorial: Spike-timing dependent plasticity (STDP)
    • Outro
    • Suggested further readings
    • Day Summary
  • Dynamic Networks (W2D4)
    • Intro
    • Tutorial 1: Neural Rate Models
    • Tutorial 2: Wilson-Cowan Model
    • Bonus Tutorial: Extending the Wilson-Cowan Model
    • Outro
    • Suggested further readings
    • Day Summary
  • Dynamical Systems Wrap-Up

Stochastic Processes

  • Bayesian Decisions (W3D1)
    • Intro
    • Tutorial 1: Bayes with a binary hidden state
    • Tutorial 2: Bayesian inference and decisions with continuous hidden state
    • Bonus Tutorial : Fitting to data
    • Outro
    • Suggested further readings
    • Day Summary
  • Hidden Dynamics (W3D2)
    • Intro
    • Tutorial 1: Sequential Probability Ratio Test
    • Tutorial 2: Hidden Markov Model
    • Tutorial 3: The Kalman Filter
    • Bonus Tutorial 4: The Kalman Filter, part 2
    • Bonus Tutorial 5: Expectation Maximization for spiking neurons
    • Outro
    • Suggested further readings
    • Day Summary
  • Optimal Control (W3D3)
    • Intro
    • Tutorial 1: Optimal Control for Discrete States
    • Tutorial 2: Optimal Control for Continuous State
    • Outro
    • Suggested further readings
    • Day Summary
  • Reinforcement Learning (W3D4)
    • Intro
    • Tutorial 1: Learning to Predict
    • Tutorial 2: Learning to Act: Multi-Armed Bandits
    • Tutorial 3: Learning to Act: Q-Learning
    • Tutorial 4: Model-Based Reinforcement Learning
    • Outro
    • Suggested further readings
    • Day Summary
  • Network Causality (W3D5)
    • Intro
    • Tutorial 1: Interventions
    • Tutorial 2: Correlations
    • Tutorial 3: Simultaneous fitting/regression
    • Tutorial 4: Instrumental Variables
    • Outro
    • Suggested further readings
    • Day Summary
  • Stochastic Processes Wrap-Up

Project Booklet

  • Introduction
  • Daily guide for projects
  • Modeling Step-by-Step Guide
    • Modeling Steps 1 - 4
    • Modeling Steps 5 - 10
    • Example Model Project: the Train Illusion
    • Example Data Project: the Train Illusion
  • Datasets
    • Neurons
      • Guide
      • Overview videos
    • fMRI
      • Guide
      • Overview videos
    • ECoG
      • Guide
      • Overview videos
    • Behavior
      • Guide
      • Overview videos
    • Theory
      • Guide
  • Project Templates
  • Projects 2020
    • Neurons
    • Theory
    • Behavior
    • fMRI
    • EEG
Powered by Jupyter Book
  • repository
  • open issue
  • .md

Using discord

Using discord¶

Please click here for Discord Guide

previous

Using Kaggle

next

Quick links and policies

By Neuromatch

The contents of this repository are shared under the Creative Commons Attribution 4.0 International License. Software elements are additionally licensed under the BSD (3-Clause) License.