API reference

What is this page?

This is the API (Application Programming Interface) reference for OpenRetina. It provides comprehensive documentation of all public classes, functions, and modules available in the package.

How to use this reference:

• Browse by module: Navigate through the sections below to find specific components
• Search for functionality: Use the search function to find specific classes or functions
• Understand parameters: Each entry includes detailed parameter descriptions, return types, and usage examples
• Link to source: Most entries link to the source code for deeper understanding

If you're new to OpenRetina, start with the Getting Started Guide or Tutorials before diving into the API reference.

Core Components

• Models: Pre-trained and customizable retina models
• Modules: Building blocks for constructing models
• Data I/O: Data loading and processing utilities
• In-silico: Tools for analyzing models with virtual experiments
• Utilities: Helper functions and visualization tools

API Documentation by Module

The OpenRetina package is organized in a modular structure where each module serves a specific purpose in the retina modeling workflow.

Models

The models module provides complete model implementations:

• openretina.models.core_readout: End-to-end convolutional models with spatial readouts
• openretina.models.linear_nonlinear: Classical linear-nonlinear cascade models
• openretina.models.sparse_autoencoder: Models for learning sparse representations

Modules

The modules package contains building blocks for model construction. See the modules overview for complete documentation.

Data I/O

The data_io package handles data loading and preprocessing:

• openretina.data_io.base_dataloader: Abstract interfaces for data handling
• openretina.data_io.hoefling_2024: Dataloaders for Höfling et al. 2024 dataset
• openretina.data_io.artificial_stimuli: Utilities for generating artificial stimuli
• openretina.data_io.cyclers: Utilities for cycling through datasets

See the data I/O overview for complete documentation.

In-silico

The insilico package provides tools for analyzing models:

• openretina.insilico.stimulus_optimization: Tools for finding optimal stimuli

See the in-silico overview for complete documentation.