Python for Neuroscience PhDs
Empower your neuroscience research with Python: Master data analysis, modeling, and machine learning techniques for neural data.
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Python Fundamentals for Neuroscience
Unit 1: Python Basics for Neuroscientists
Why Python for Neurons?
Variables & Data Types
Lists: Data Swiss Army
Dictionaries: Key Insights
Tuples & Sets
Unit 2: Control Flow & Functions
If/Else: Branching Logic
Loops: Repeating Tasks
Functions: Code Recipes
Lambda Functions
Error Handling
Unit 3: NumPy for Numerical Computation
NumPy Arrays: The Core
Array Operations
Linear Algebra
Random Number Generation
SciPy Intro
Unit 4: Pandas for Data Analysis
DataFrames: Tabular Data
Indexing & Selection
Data Cleaning
Data Aggregation
Merging & Joining
Unit 5: Visualization with Matplotlib & Seaborn
Matplotlib Basics
Plotting Neural Data
Seaborn: Stats Viz
Customizing Seaborn
Interactive Plots
Analyzing Neural Data with Python
Unit 1: Electrophysiology Data Analysis
Intro to Electrophysiology
Loading Electrophys Data
Filtering Neural Signals
Spike Sorting Basics
LFP Analysis
Unit 2: Analyzing Imaging Data
Intro to Imaging Data
Loading Imaging Data
Calcium Imaging Analysis
fMRI Data Preprocessing
fMRI GLM Analysis
Unit 3: Behavioral Data Analysis and Integration
Intro to Behavioral Data
Loading Behavioral Data
Analyzing Behavior
Integrating Neural & Behav
Regression Analysis
Unit 4: Statistical Analysis for Neuroscience
Stats Review
SciPy for Stats
Pandas for Stats
Bayesian Stats Intro
Non-parametric Tests
Computational Neuroscience Modeling in Python
Unit 1: Hodgkin-Huxley Model
HH Model Intro
HH Model: Equations
HH Model in Python
HH Model: Parameters
HH Model: Dynamics
Unit 2: Basic Neural Networks
NNs: The Basics
NNs: NumPy Time
NNs: Training
NNs: Loss Functions
NNs: TensorFlow/PyTorch
Unit 3: Bayesian Decoding
Bayesian Decoding Intro
Bayes: Prior
Bayes: Likelihood
Bayes: Posterior
Bayes: Prediction
Unit 4: Neural Circuit Simulation
Circuit Sim: Intro
Circuit Sim: Models
Circuit Sim: Connectivity
Circuit Sim: Simulation
Circuit Sim: Analysis
Machine Learning for Neuroscience
Unit 1: Decoding Neural Activity with Scikit-learn
Intro to Neural Decoding
Decoding with Linear Models
Decoding with SVMs
Cross-Validation
Decoding Performance
Unit 2: Predicting Behavior from Neural Data
Feature Engineering
Time Series Prediction
Causal Inference
Encoding Models
Closed-Loop Control
Unit 3: Dimensionality Reduction for Neural Data
PCA for Neuroscience
t-SNE and UMAP
Factor Analysis
ICA for Neuroscience
Autoencoders
Unit 4: Evaluating and Interpreting ML Models
Model Selection
Overfitting and Underfitting
Feature Importance
Model Explainability
Reproducibility
