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  • Schedule
  • Technical Help
  • Quick links and policies
  • Prerequisites and preparatory materials for NMA Computational Neuroscience
  • Neuro Video Series (W0D0)
  • Python Workshop 1 (W0D1)
  • Python Workshop 2 (W0D2)
  • Linear Algebra (W0D3)
  • Calculus (W0D4)
  • Statistics (W0D5)
  • Model Types (W1D1)
  • Modeling Practice (W2D1)
  • Model Fitting (W1D2)
  • Generalized Linear Models (W1D3)
  • Dimensionality Reduction (W1D4)
  • Deep Learning (W1D5)
  • Autoencoders (Bonus)
  • Machine Learning Wrap-Up
  • Linear Systems (W2D2)
  • Biological Neuron Models (W2D3)
  • Dynamic Networks (W2D4)
  • Dynamical Systems Wrap-Up
  • Bayesian Decisions (W3D1)
  • Hidden Dynamics (W3D2)
  • Optimal Control (W3D3)
  • Reinforcement Learning (W3D4)
  • Network Causality (W3D5)
  • Stochastic Processes Wrap-Up
  • Introduction
  • Daily guide for projects
  • Modeling Step-by-Step Guide
  • Datasets
  • Project Templates
  • Projects 2020

Site Navigation

  • Schedule
  • Technical Help
  • Quick links and policies
  • Prerequisites and preparatory materials for NMA Computational Neuroscience
  • Neuro Video Series (W0D0)
  • Python Workshop 1 (W0D1)
  • Python Workshop 2 (W0D2)
  • Linear Algebra (W0D3)
  • Calculus (W0D4)
  • Statistics (W0D5)
  • Model Types (W1D1)
  • Modeling Practice (W2D1)
  • Model Fitting (W1D2)
  • Generalized Linear Models (W1D3)
  • Dimensionality Reduction (W1D4)
  • Deep Learning (W1D5)
  • Autoencoders (Bonus)
  • Machine Learning Wrap-Up
  • Linear Systems (W2D2)
  • Biological Neuron Models (W2D3)
  • Dynamic Networks (W2D4)
  • Dynamical Systems Wrap-Up
  • Bayesian Decisions (W3D1)
  • Hidden Dynamics (W3D2)
  • Optimal Control (W3D3)
  • Reinforcement Learning (W3D4)
  • Network Causality (W3D5)
  • Stochastic Processes Wrap-Up
  • Introduction
  • Daily guide for projects
  • Modeling Step-by-Step Guide
  • Datasets
  • Project Templates
  • Projects 2020
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  • Introduction
  • Schedule
    • General schedule
    • Shared calendars
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  • 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
  • repository
  • open issue
  • .md

Quick links and policies

Contents

  • Quick links
  • Policies

Quick links and policies#

Quick links#

Course materials: https://compneuro.neuromatch.io/

Portal: https://portal.neuromatchacademy.org/

Website: https://neuromatch.io/computational-neuroscience-course/

Code of Conduct Violations Form: NeuromatchAcademy/precourse

Project Exemption Form: https://airtable.com/shrubhlgsWJ8DuA7E

Attendance Policy & Waiver: https://docs.neuromatch.io/p/BI_ssrrHYrfg_E/Academy-Student-Attendance-Policy-and-Waivers

Policies#

See full course attendance policy here.

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