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doi: 10.5281/zenodo.18402021 , 10.5281/zenodo.18371737 , 10.5281/zenodo.18449524 , 10.5281/zenodo.17618153 , 10.5281/zenodo.18373568 , 10.5281/zenodo.18068182 , 10.5281/zenodo.17422980 , 10.5281/zenodo.18449596 , 10.5281/zenodo.18295465 , 10.5281/zenodo.18376808 , 10.5281/zenodo.18397532 , 10.5281/zenodo.18371740 , 10.5281/zenodo.17896915 , 10.5281/zenodo.18449556 , 10.5281/zenodo.18113672 , 10.5281/zenodo.17919224 , 10.5281/zenodo.18401900 , 10.5281/zenodo.18054272 , 10.5281/zenodo.18205024 , 10.5281/zenodo.18243971 , 10.5281/zenodo.18058207 , 10.5281/zenodo.18376763 , 10.5281/zenodo.18399391 , 10.5281/zenodo.18091501 , 10.5281/zenodo.18449591 , 10.5281/zenodo.18398569 , 10.5281/zenodo.17474272 , 10.5281/zenodo.18193333 , 10.5281/zenodo.17445780
A Python library built on top of the Brain Simulation Ecosystem (brainstate, brainunit) that streamlines experimentation with continuous attractor neural networks and related brain-inspired models. It delivers ready-to-use models, task generators, analysis tools, and pipelines so neuroscience and AI researchers can move from ideas to reproducible simulations quickly.
If you use this software, please cite it as below.
CANNs, neural dynamics, spatial cognition, continuous attractor neural networks, JAX, brain-inspired computing, computational neuroscience
CANNs, neural dynamics, spatial cognition, continuous attractor neural networks, JAX, brain-inspired computing, computational neuroscience
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