Brian

Free, open-source simulator for SNNs, written in Python, focusing on ease of use and flexibility.

Brian

Overview

Brian2 is an open-source Python library for the simulation of spiking neural networks (SNNs), notable for its user-friendly syntax and flexible approach to the design and simulation of neural models. Brian2 has been continually maintained by Romain Brette, Marcel Stimberg, and Dan Goodman since 2012. They heavily encourage and support community contributions and involvement. Having been publicly available for 12 years, Brian2 positions itself as the pillar of the neuromorphic community.

It was one of the first to provide a user-friendly and flexible library for researchers and practitioners interested in understanding and advancing the field of SNNs. Brian2 has a robust community, comprehensive documentation, and stays competitive with the latest advancements in neural network simulations, making it a powerful tool in both teaching and research.

The framework emphasizes simplicity, efficiency, and extensibility, making it a popular choice for both teaching and research. The neural models in Brian2 are defined using equations directly, streamlining the transition from theoretical models to simulation code. This feature significantly lowers the barriers to entry for those who are new to computational modeling.

With neuron models defined by equations and the ability to specify synaptic connections, the network’s topology and construction have a very low level of abstraction. This design allows users to have creative freedom in designing their networks and serves as an effective way to learn the principles of both spiking and non-spiking neural networks.

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Get Involved with ONM

Digital Neuromorphic Hardware Read List

Digital Neuromorphic Hardware Read List

  • Fabrizio Ottati

Stay up-to-date with cutting-edge digital hardware designs for neuromorphic applications. Explore recent research on power-efficient event-driven spiking neural networks and state-of-the-art processors like TrueNorth and Loihi.

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.

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.