[Comp-neuro] New release of the CARLsim Spiking Neural Network Simulator

Dear colleagues,

Many of you may be interested in our latest software release of the CARLsim simulator. CARLsim is a publicly available, efficient C/C++-based Spiking Neural Network (SNN) simulator that is optimized to run on both generic, x86 CPUs and standard off-the-shelf GPUs. The simulator provides a PYNN-like programming interface, which allows for details and parameters to be specified at the synapse, neuron, and network level. Software and documentation can be found at:

http://www.socsci.uci.edu/~jkrichma/CARLsim/index.html

This release is in conjunction with our latest publications, which highlight CARLsim's latest features.

Beyeler, M., Richert, M., Dutt, N.D., and Krichmar, J.L. (2014). Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex. Neuroinformatics.

Carlson, K.D., Nageswaran, J.M., Dutt, N., and Krichmar, J.L. (2014). An efficient automated parameter tuning framework for spiking neural networks. Frontiers in Neuroscience 8.

Carlson, K.D., Richert, M., Dutt, N., and Krichmar, J.L. (2013). Biologically Plausible Models of Homeostasis and STDP: Stability and Learning in Spiking Neural Networks. Paper presented at: International Joint Conference on Neural Networks (Dallas, TX: IEEE Explore).

CARLsim Release 2.2.0 Features
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1. Improved and expanded real-time SNN vision models.
2. Included support for a parameter tuning interface library that uses evolutionary algorithms and GPUs for automated SNN parameter tuning.
3. Implemented a model for homeostatic synaptic scaling.
4. Added CUDA 5.0 support.

Best regards from the CARLsim team,

Michael Beyeler
Kris Carlson
Nikil Dutt
Jeff Krichmar

-----------------
Jeff Krichmar
Department of Cognitive Sciences
2328 Social & Behavioral Sciences Gateway
University of California, Irvine
Irvine, CA 92697-5100
jkrichma@uci.edu
http://www.socsci.uci.edu/~jkrichma

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