Nengo: a Python tool for building large-scale functional brain models

By Trevor Bekolay, James Bergstra, Eric Hunsberger, Travis DeWolf, T. Stewart, Daniel Rasmussen, Feng-Xuan Choo, Aaron R. Voelker, Chris Eliasmith. Front. Neuroinform., 2014

507 citations recorded by the source corpus.

Neuroscience currently lacks a comprehensive theory of how cognitive processes can be implemented in a biological substrate. The Neural Engineering Framework (NEF) proposes one such theory, but has not yet gathered significant empirical support, partly due to the technical challenge of building and simulating large-scale models with the NEF. Nengo is a software tool that can be used to build and simulate large-scale models based on the NEF; currently, it is the primary resource for both teaching how the NEF is used, and for doing research that generates specific NEF models to explain experimental data. Nengo 1.4, which was implemented in Java, was used to create Spaun, the world's largest functional brain model (Eliasmith et al., 2012). Simulating Spaun highlighted limitations in Nengo 1.4's ability to support model construction with simple syntax, to simulate large models quickly, and to collect large amounts of data for subsequent analysis. This paper describes Nengo 2.0, which is implemented in Python and overcomes these limitations. It uses simple and extendable syntax, simulates a benchmark model on the scale of Spaun 50 times faster than Nengo 1.4, and has a flexible mechanism for collecting simulation results.

Subfields: Artificial Intelligence, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/939be55e4c179b8f306445e85656d437282ec1d9

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Citations in the corpus

Neuromorphic computing at scale (2025, unknown): “…specific to each task, as opposed to the The neuromorphic ecosystem, as described here, consists of hierarchies of hardware 123–125 and software 66,67,72,77,126,127 technology, hardware/software co-design approaches, workflows that culminate in flexible neuromorphic ecosystems 4,128,129 ,…”
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence (2023, unknown): “Nengo ( 80 ), SpykeTorch ( 81 ), and BindsNET ( 82 ) use simplified neurons with smaller numbers of ODEs than detailed neurons.”
Opportunities for neuromorphic computing algorithms and applications (2022, background): “For example, NEST targets primarily computational neuroscience workloads, whereas Nengo implements computation as framed by the Neural Engineering Framework 107 .”
Spiking Neural Networks and Their Applications: A Review (2022, methodology): “Nengo [111] Neural simulator for large-scale neural networks based on the neural engineering framework (NEF), which is a large-scale modeling approach that can leverage single neuron models to build neural networks.”
Event-Based Vision: A Survey (2019, methodology): “It is programmed using Nengo [238] and implements the Neural Engineering Framework [239].”
Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook (2021, methodology): “The neural engineering framework (NEF) [19] and other related capabilities are supported through the Nengo toolchain [20] from Applied Brain Research.”