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

References in zbMATH (referenced in 11 articles )

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  1. Stöckel, Andreas; Eliasmith, Chris: Passive nonlinear dendritic interactions as a computational resource in spiking neural networks (2021)
  2. Voelker, Aaron R.; Blouw, Peter; Choo, Xuan; Dumont, Nicole Sandra-Yaffa; Stewart, Terrence C.; Eliasmith, Chris: Simulating and predicting dynamical systems with spatial semantic pointers (2021)
  3. Zixuan Zhao, Nathan Wycoff, Neil Getty, Rick Stevens, Fangfang Xia: Neko: a Library for Exploring Neuromorphic Learning Rules (2021) arXiv
  4. S. A., Malik; A. H., Mir: Synchronization of Hindmarsh Rose neurons (2020)
  5. Andalibi, Vafa; Hokkanen, Henri; Vanni, Simo: Controlling complexity of cerebral cortex simulations. I: CxSystem, a flexible cortical simulation framework (2019)
  6. Huang, Fuqiang; Ching, ShiNung: Spiking networks as efficient distributed controllers (2019)
  7. Hananel Hazan, Daniel J. Saunders, Hassaan Khan, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma: BindsNET: A machine learning-oriented spiking neural networks library in Python (2018) arXiv
  8. Voelker, Aaron R.; Eliasmith, Chris: Improving spiking dynamical networks: accurate delays, higher-order synapses, and time cells (2018)
  9. Agerskov, Claus: Vector symbolic spiking neural network model of hippocampal subarea CA1 novelty detection functionality (2016)
  10. Tripp, Bryan; Eliasmith, Chris: Function approximation in inhibitory networks (2016)
  11. Tripp, Bryan P.: Surrogate population models for large-scale neural simulations (2015)