Simulation of networks of spiking neurons: A review of tools and strategies

By Romain Brette, Michelle Rudolph-Lilith, Nicholas T. Carnevale, Michael L. Hines, D. Beeman, James M. Bower, Markus Diesmann, Abigail Morrison, P. Goodman, F. Harris and 12 more. 2006

884 citations recorded by the source corpus.

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin–Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

Deep learning in neural networks: An overview (2014, neutral): “Many computational models of such spiking neurons have been proposed and analyzed (e.g., Amit & Brunel, 1997; Bohte, Kok, & La Poutre, 2002; Brea, Senn, & Pfister, 2013; Brette et al., 2007; Brunel, 2000; Deco & Rolls, 2005; Gerstner & Kistler, 2002; Gerstner & van Hemmen, 1992; Hoerzer, Legenstein,”
Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks (2017, background): “The brain processes input information in the form of spatiotemporal binary events called spikes [16]–[18].”
Brian 2: an intuitive and efficient neural simulator (2019, background): “Neural simulators are increasingly used to develop models of the nervous system, at different scales and in a variety of contexts (Brette et al., 2007).”
Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE) (2018, methodology): “This strategy is commonly used in synapse circuits in neuromorphic hardware to reduce circuit area (Bartolozzi and Indiveri, 2006), and in software simulations of spiking neurons to improve memory consumption and computation time (Brette et al., 2007).”
Neuron tracing in perspective (2010, background): “A much more complete discussion of these, and six other simulation systems and underlying strategies can be found in (120), while the issue of interoperability between simulators is discussed in (121).”
Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype (2018, background): “It dominates the required chip area (Benjamin et al., 2014; Schmitt et al., 2017) and power consumption (Schemmel et al., 2008; Akopyan et al., 2015) in special purpose hardware and the required memory and computation time ( Brette et al., 2007) in software simulations.”
How advances in neural recording affect data analysis (2011, methodology): “As the number of simultaneously recorded neurons grows, models that have traditionally only been tested using large-scale neural simulations will be able to access large, comparable dataset”
Memory and Information Processing in Neuromorphic Systems (2015, methodology): “Neural network simulation frameworks based on C or Python [37], [153], [154] which can run on conventional CPU- and GPU-based systems, offer the best flexibility and quickest development times for simulating large-scale spiking networks.”
NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail (2010, methodology): “A number of dedicated software packages are available for creating and simulating neuronal and network models [24] including NEURON [25], GENESIS [26], MOOSE [27], NEST [28] and PSICS (http://www.”
Minitaur, an Event-Driven FPGA-Based Spiking Network Accelerator (2014, background): “A comprehensive review of the performance of both event-driven and timestepped software algorithms and implementations of spiking neural networks can be found in [11].”
A review of learning in biologically plausible spiking neural networks (2019, neutral): “…and phase, SNNs offer a promising computing paradigm and are potentially capable of modelling complex information processing in the brain (Brette, R. et al., 2007; Gerstner, Wulfram & Kistler, 2002; Hodgkin & Huxley, 1952; Izhikevich, 2004; Izhikevich, 2006; Kasabov, Dhoble, Nuntalid, &…”
A Novel Multiple Objective Optimization Framework for Constraining Conductance-Based Neuron Models by Experimental Data (2007, methodology): “Conductance-based compartmental models are increasingly used in the simulation of neuronal circuits (Brette et al., 2007; Herz et al., 2006; Traub et al., 2005).”