Dear Colleagues,

We are pleased to announce the 6.1 release of CARLsim. CARLsim is an efficient, easy-to-use, GPU-accelerated library for simulating large-scale spiking neural network (SNN) models with a high degree of biological detail. CARLsim allows execution of networks of Izhikevich spiking neurons with realistic synaptic dynamics on both generic x86 CPUs and standard off-the-shelf GPUs. The simulator provides a PyNN-like programming interface in C/C++, which allows for details and parameters to be specified at the synapse, neuron, and network level. CARLsim, which is in its 6th major iteration, is open-source and publicly available.

With the release of CARLsim 6.1, we introduce axonal plasticity to simulate the biological phenomenon of experience dependent plasticity to the conductance velocity of axonal signaling. Axonal plasticity appears to be important for synchronization, skill learning, and many other potential applications.  More information about the implementation and an example application can be found in our recent IJCNN paper, "Experience-Dependent Axonal Plasticity in Large-Scale Spiking Neural Network Simulations", which can be found at:

https://www.socsci.uci.edu/~jkrichma/CARLsim61-IJCNN2023.pdf

CARLsim 6.1 is available on GitHub: https://github.com/UCI-CARL/CARLsim6

Best regards,