Tonic

Tonic is a Python package for managing and transforming neuromorphic datasets.

Tonic

Overview

Tonic is a specialized Python package designed to facilitate the downloading and manipulation of neuromorphic datasets, particularly focusing on event-based vision and audio data. It is fully compatible with PyTorch Vision/Audio and offers a range of event transformations, making it a flexible tool for working with neuromorphic data. The package includes a variety of publicly available datasets and provides efficient ways to manage and transform these datasets for various applications.

The documentation for Tonic includes tutorials on loading and augmenting images alongside events, caching data for faster loading, batching for event data, and slicing datasets for manageable processing. Additionally, it provides API references for its neuromorphic datasets and event transformations, supporting file parsers for various formats. For those new to the field or seeking to deepen their understanding, Tonic offers reading material on neuromorphic cameras, spiking neural networks, and associated simulators.

Tonic is an open-source project and encourages community involvement, offering guidelines for contributions and communication channels for collaboration. The project outlines its journey and updates in its release notes, giving insight into its evolution and the community behind it.

Can you contribute tutorial guides or case studies?

Get Involved with ONM

Spiking Neurons: A Digital Hardware Implementation

Spiking Neurons: A Digital Hardware Implementation

  • Fabrizio Ottati

Learn how to model Leaky Integrate and Fire (LIF) neurons in digital hardware. Understand spike communication, synapse integration, and more for hardware implementation.

NorthPole, IBM's latest Neuromorphic AI Hardware

NorthPole, IBM's latest Neuromorphic AI Hardware

  • Fabrizio Ottati

Translating the NorthPole paper from IBM to human language.

Efficient Compression for Event-Based Data in Neuromorphic Applications

Efficient Compression for Event-Based Data in Neuromorphic Applications

  • Gregor Lenz, Fabrizio Ottati, Alexandre Marcireau

Discover methods to efficiently encode and store event-based data from high-resolution event cameras, striking a balance between file size and fast retrieval for spiking neural network training.