CARLsim 3: A user-friendly and highly optimized library for the creation of neurobiologically detailed spiking neural networks


Spiking neural network (SNN) models describe key aspects of neural function in a computationally efficient manner and have been used to construct large-scale brain models. Large-scale SNNs are challenging to implement, as they demand high-bandwidth communication, a large amount of memory, and are computationally intensive. Additionally, tuning parameters of these models becomes more difficult and time-consuming with the addition of biologically accurate descriptions. To meet these challenges, we have developed CARLsim 3, a user-friendly, GPU-accelerated SNN library written in C/C++ that is capable of simulating biologically detailed neural models. The present release of CARLsim provides a number of improvements over our prior SNN library to allow the user to easily analyze simulation data, explore synaptic plasticity rules, and automate parameter tuning. In the present paper, we provide examples and performance benchmarks highlighting the library’s features.

Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN)
Michael Beyeler
Assistant Professor

Michael Beyeler directs the Bionic Vision Lab at UC Santa Barbara, which is developing novel methods and algorithms to interface sight recovery technologies with the human visual system, with the ultimate goal of restoring useful vision to the blind.