Brian simulator

By Dan F. M. Goodman, Romain Brette. Scholarpedia, 2013

501 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/f126b127b581921663bf4f5da366d512875fc2e1

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In Artificial Intelligence

In Experimental and Cognitive Psychology

Citations in the corpus

Neuronal Synchrony in Complex-Valued Deep Networks (2013, methodology): “Consider Figure 1a for an example of how a more dynamic code could be transmitted between neurons (simulated with the Brian simulator, Goodman & Brette, 2009).”
An efficient automated parameter tuning framework for spiking neural networks (2014, methodology): “The popular SNN simulator, Brian (Goodman and Brette, 2009), also has support for parameter tuning in the form of a parallelized CPU/GPU tuning toolkit.”
Nengo: a Python tool for building large-scale functional brain models (2014, methodology): “There are many other neural simulators dedicated to building large-scale neural models [e.g., (Goodman and Brette, 2008; Eppler et al., 2009; Hines et al., 2009)], and many tools for simulating cognitive phenomena with various levels of biologically plausibility [e.g., (Cooper and Fox, 1998; Sun,…”
Brian 2: an intuitive and efficient neural simulator (2019, methodology): “Popular tools for simulating spiking neurons and networks of such neurons are NEURON (Carnevale & Hines, 2006), GENESIS (Bower & Beeman, 1998), NEST (Gewaltig & Diesmann, 2007), and Brian (Goodman & Brette, 2009).”
Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python (2011, methodology): “Python has already been embraced by the neuroscientific community and is rapidly gaining popularity (Bednar, 2009; Goodman and Brette, 2009).”
The Virtual Brain: a simulator of primate brain network dynamics (2013, background): “It does not intend to build brain models at the level of neurons (Goodman and Brette, 2009; Cornelis et al., 2012), however, it does leverage information from microscopic models to add detail and enhance the performance of the neural population models, which act as building blocks and functional…”
Dendrites of dentate gyrus granule cells contribute to pattern separation by controlling sparsity (2016, background): “3) network simulator (Goodman and Brette, 2009; Brette and Goodman, 2011) running on a high-performance computing cluster (HPCC) with 312 cores under 64-bit CentOS Linux.”
Python in neuroscience (2015, background): “…of the articles were chosen to “climb the tier” in the Frontiers system, and were followed up by Focused Review articles in Frontiers in Neuroscience (Davison et al., 2009b; Goodman and Brette, 2009; Hanke et al., 2010; Ince et al., 2010), another was the subject of a commentary (Einevoll, 2009).”