CANNs: Continuous Attractor Neural Networks Toolkit
CANNs: Continuous Attractor Neural Networks Toolkit
CANNs (Continuous Attractor Neural Networks toolkit) is a research toolkit built on BrainPy and JAX, with optional Rust-accelerated canns-lib for selected performance-critical routines (e.g., TDA/Ripser and task generation). It bundles model collections, task generators, analyzers, and the ASA pipeline (GUI/TUI) so researchers can run simulations and analyze results in a consistent workflow. The API separates models, tasks, analyzers, and trainers to keep experiments modular and extensible.
If you use this software, please cite it as below.
- Peking University China (People's Republic of)
- Chongqing University China (People's Republic of)
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
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!