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CARLsim specifications


Unique identifier OMICS_15870
Name CARLsim
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages C, C++, Java
Computer skills Advanced
Version 3.1
Stability Stable
Maintained Yes




No version available


  • person_outline Michael Beyeler <>

Publications for CARLsim

CARLsim citations


Evolving Simple Models of Diverse Intrinsic Dynamics in Hippocampal Neuron Types

PMCID: 5859109
PMID: 29593519
DOI: 10.3389/fninf.2018.00008

[…] im parameters. single compartment models were simulated using the apache commons mathematics library. the mc models with up to 39 open parameters were tuned using the parameter tuning interface of carlsim, an open-source high performance gpu-based spiking neural network simulator (beyeler et al., ). the ea and the single compartment model simulations were run on distributed cpu nodes, […]


A FPGA Based, Granularity Variable Neuromorphic Processor and Its Application in a MIMO Real Time Control System

PMCID: 5620544
PMID: 28832522
DOI: 10.3390/s17091941

[…] circuits (asics) [], and field programmable gate arrays (fpgas) []., cpus and gpus are parts of general purpose processors (gpps). the classic platforms based on cpu and gpu are spinnaker and carlsim, correspondingly. the spinnaker machine is a specifically designed computer for supporting the sorts of communication found in the brain. it is based on the connection of processing nodes, […]


Learning touch preferences with a tactile robot using dopamine modulated STDP in a model of insular cortex

PMCID: 4510776
PMID: 26257639
DOI: 10.3389/fnbot.2015.00006

[…] 4 ~ 7 trackballs are touched simultaneously., to support the present mutual reinforcement learning experiments, we built a spiking neural network (snn) model using the large scale snn simulator carlsim to recognize tactile sensory input, and to control carl-sjr's behavior (nageswaran et al., ; richert et al., ; carlson et al., ). carlsim was written in c/c++/cuda and designed to leverage […]


Improper activation of D1 and D2 receptors leads to excess noise in prefrontal cortex

PMCID: 4356073
PMID: 25814948
DOI: 10.3389/fncom.2015.00031

[…] and the maintenance of working memory is mediated by d2-expressing neurons (“no-go pathway”)., to construct our model, we used a publicly available simulator (, which has been shown to simulate large-scale spiking neural networks efficiently and flexibly (richert et al., ). the model contained a total of 57,212 neurons and approximately 30 million […]


Synthetic neuronal datasets for benchmarking directed functional connectivity metrics

PMCID: 4435472
PMID: 26019993
DOI: 10.7717/peerj.923

[…] with an intel® core™ i7-2600k cpu @3.4 ghz, 8 gb of ram and an nvidia® geforce® gtx 580 gpu with 3 gb graphics memory. simulations of izhikevich columns ran on modified cuda/c + + routines from carlsim 2.0 () and used the parallel processing capabilities of the gpu. the remaining generative and forward models were implemented and ran in matlab® and used only the cpu without any explicit […]


An efficient automated parameter tuning framework for spiking neural networks

PMCID: 3912986
PMID: 24550771
DOI: 10.3389/fnins.2014.00010

[…] automated parameter tuning framework is the ability to run multiple snns in parallel on the gpu, allowing significant acceleration of the ea evaluation phase. we first briefly review the approaches carlsim uses to run snns in parallel before describing the general layout of the automated parameter tuning framework and describe how a researcher would use the tool to tune snns. figure shows […]

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CARLsim institution(s)
Department of Cognitive Sciences, University of California, Irvine, CA, USA; Department of Computer Science, University of California, Irvine, CA, USA
CARLsim funding source(s)
This work was supported in part by the Defense Advanced Research Projects Agency (DARPA) subcontract 801888-BS.

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