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canns-toolkit-attractor-networks CANNs toolkit for continuous attractor neural network research - unified Python/Rust/PySide6 framework for modeling spatial navigation, grid cells, head-direction cells, and attractor dynamics analysis. Activation: CANNs toolkit, continuous attractor neural networks, BrainPy JAX, grid cell simulation, head-direction cells, place cells, path integration, persistent homology attractor, Rust neural simulation, PySide6 neuroscience, anticipative tracking, theta sweep
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2 fichiers canns-toolkit-attractor-networks name canns-toolkit-attractor-networks description CANNs toolkit for continuous attractor neural network research - unified Python/Rust/PySide6 framework for modeling spatial navigation, grid cells, head-direction cells, and attractor dynamics analysis. Activation: CANNs toolkit, continuous attractor neural networks, BrainPy JAX, grid cell simulation, head-direction cells, place cells, path integration, persistent homology attractor, Rust neural simulation, PySide6 neuroscience, anticipative tracking, theta sweep metadata {"arxiv_id":"2606.27783","authors":["Sichao He","Aiersi Tuerhong","Shangjun She","Tianhao Chu","Yuling Wu","Junfeng Zuo","Si Wu"],"published":"2026-06-26","tags":["continuous attractor neural networks","BrainPy","JAX","Rust acceleration","PySide6","persistent homology","grid cells","place cells","head-direction cells","path integration","spatial navigation"]}
CANNs Toolkit: Continuous Attractor Neural Networks
Comprehensive open-source toolkit for continuous attractor neural network (CANN) research, integrating simulation, acceleration, and analysis in a unified workflow.
Overview
CANNs are the canonical computational framework for how the brain encodes continuous variables (spatial position, head direction, movement direction) and explain hippocampal place cells, entorhinal grid cells, and head-direction cells. This toolkit addresses fragmentation in CANN research by providing standardized implementations, acceleration, and analysis tools.
Core Components
1. canns (Python Library)
Built on BrainPy/JAX framework, provides:
Standardized CANNs : 1D and 2D continuous attractor networksSpike-frequency adaptation (SFA) variants : For anticipative trackingGrid cell networks : Hexagonal pattern formation and maintenance
Hierarchical path integration models : Multi-scale spatial navigation
Brain-inspired attractor architectures : Biologically plausible implementations
Curated datasets : Standard benchmarks for CANN evaluation
Task generators : Spatial navigation and memory tasks
Analyzer module : Attractor geometry and dynamics analysis
Trainer modules : Biologically plausible plasticity rules (STDP, Hebbian, homeostatic)
GPU acceleration via JAX
Differentiable programming for gradient-based optimization
Integration with Brain ecosystem (BrainPy, BrainState, BrainUnit)
2. canns-lib (Rust Backend) High-performance acceleration layer:
Spatial navigation workloads : Hundreds-of-times speedups over pure PythonPersistent homology computation : Modest gains for Ripser-based topological analysisMemory-efficient implementations : Optimized data structures for large-scale simulationsParallel computation : Multi-threaded attractor dynamics
Large-scale grid cell network simulations (>10,000 neurons)
Long-timescale path integration (hours of simulated time)
Topological analysis of attractor manifolds
3. ASA (Attractor Structure Analyzer) PySide6-based GUI pipeline for experimental data analysis:
Persistent homology : Detect ring-like and toroidal attractor signaturesCohomology analysis : Higher-order topological featuresSpike train processing : Convert experimental recordings to attractor geometryInteractive visualization : 3D attractor manifold explorationExport capabilities : Publication-ready figures and data
Load spike train data (NWBI, HDF5, or custom formats)
Preprocess (binning, smoothing, rate estimation)
Compute topological features (persistence diagrams, barcodes)
Identify attractor type (ring, torus, sphere, etc.)
Visualize and export results
Key Methodologies
Spike-Frequency Adaptation (SFA) SFA enables anticipative tracking - the network predicts future states rather than representing current states. Critical for:
Moving object tracking
Smooth pursuit eye movements
Path integration with velocity input
Implementation : Adaptation current with timescale τ_adapt ~ 100-500ms
Theta Sweep Dynamics Theta oscillations (4-10 Hz) modulate attractor dynamics:
Head-direction cells : Theta-phase precessionPlace cells : Forward sweep of place field sequencesGrid cells : Theta-rhythmic grid pattern updates Modeling approach : Oscillatory input modulating synaptic weights or external drive
Hierarchical Path Integration Multi-scale integration from local cues to global map:
Local : Velocity integration (dead reckoning)Intermediate : Landmark-based correctionGlobal : Map alignment and drift correction Architecture : Stacked CANNs with different spatial scales and update rules
Persistent Homology for Attractor Detection Topological data analysis to identify attractor manifold structure:
Point cloud construction : Embed neural activity in state spaceFiltration : Build simplicial complex at multiple scalesPersistence computation : Track topological features across scalesBarcode interpretation :
Long bars = stable topological features
Ring attractor: 1 persistent H1 feature
Torus attractor: 2 persistent H1 features + 1 H2 feature
Tools : Ripser (C++ backend), Gudhi (Python wrapper)
Installation
pip install canns
pip install canns-lib
pip install canns-asa
Python 3.8+
JAX 0.4+ (with GPU support recommended)
BrainPy 2.0+
PySide6 (for ASA GUI)
Rust toolchain (for canns-lib compilation)
Usage Examples
Basic 1D Ring Attractor import canns
from canns.models import RingAttractor
from canns.tasks import HeadDirectionTask
net = RingAttractor(
n_neurons=256 ,
connectivity='mexican_hat' ,
adaptation=True
)
task = HeadDirectionTask(
duration=10000 ,
angular_velocity=10
)
activity = net.simulate(task.inputs)
analyzer = canns.Analyzer(activity)
analyzer.plot_attractor_manifold()
analyzer.compute_persistence_diagram()
Grid Cell Network with Rust Acceleration from canns.models import GridCellNetwork
from cannsLib import accelerate
net = GridCellNetwork(
grid_scales=[0.3 , 0.5 , 0.8 ],
grid_orientations=[0 , 60 , 120 ],
n_neurons_per_module=1000
)
fast_net = accelerate(net)
trajectory = canns.tasks.RandomWalk2D(duration=60000 )
activity = fast_net.simulate(trajectory.positions)
analyzer = canns.GridAnalyzer(activity)
grid_scores = analyzer.compute_gridness_scores()
Experimental Data Analysis with ASA from canns.asa import AttractorStructureAnalyzer
data = canns.load('neural_recording.nwb' )
asa = AttractorStructureAnalyzer()
rates = asa.estimate_firing_rates(data.spike_trains, bin_size=50 )
persistence = asa.compute_persistent_homology(
rates,
max_dimension=2 ,
filtration='rips'
)
attractor_type = asa.classify_attractor(persistence)
print (f"Detected: {attractor_type} " )
asa.plot_persistence_diagram(persistence)
asa.plot_3d_manifold(rates)
Reproducible Pipelines The toolkit ships with complete pipelines reproducing recent CANN results:
SFA-Driven Anticipative Tracking python -m canns.pipelines.sfa_tracking \
--config configs/anticipative_tracking.yaml \
--output results/sfa_tracking/
Recovers : Anticipative head-direction cell responses with ~100ms lookahead
Theta Sweeps in HD/Place/Grid Systems python -m canns.pipelines.theta_sweeps \
--system place_cells \
--theta_freq 8.0 \
--output results/theta_sweeps/
Recovers : Forward sweep of place field sequences during theta cycles
Hierarchical Path Integration python -m canns.pipelines.hierarchical_pi \
--n_scales 3 \
--landmark_correction True \
--output results/hierarchical_pi/
Recovers : Drift-resistant path integration with landmark anchoring
Advanced Topics
Custom Plasticity Rules from canns.plasticity import STDP, HomeostaticScaling
plasticity = [
STDP(tau_plus=20 , tau_minus=20 , A_plus=0.01 , A_minus=0.012 ),
HomeostaticScaling(target_rate=5.0 , timescale=1000 )
]
net = RingAttractor(plasticity_rules=plasticity)
Multi-Scale attractor Networks from canns.models import HierarchicalAttractor
hierarchy = HierarchicalAttractor([
{'scale' : 'local' , 'n_neurons' : 100 , 'update_rate' : 100 },
{'scale' : 'medium' , 'n_neurons' : 50 , 'update_rate' : 20 },
{'scale' : 'global' , 'n_neurons' : 20 , 'update_rate' : 5 }
])
GPU-Accelerated Training import jax
from canns.training import train_attractor
def loss_fn (params, inputs, targets ):
activity = net.apply(params, inputs)
return jax.numpy.mean((activity - targets)**2 )
trained_params = train_attractor(
net, loss_fn,
optimizer='adam' ,
learning_rate=1e-3 ,
n_epochs=1000
)
Pitfalls
JAX compilation time : First call to JAX functions triggers compilation (10-60s). Subsequent calls are fast. Use jax.jit to cache compiled functions.
Rust backend requires toolchain : canns-lib installation requires Rust compiler. Install via curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
Memory usage : Large CANNs (>10k neurons) require significant GPU memory. Use canns-lib acceleration to reduce memory footprint.
Persistent homology scaling : Ripser computation is O(n²) in number of points. Subsample neural activity if >1000 timepoints.
Theta phase interpretation : Theta-phase precession depends on reference frame. Ensure consistent alignment across neurons before analysis.
Grid cell orientation ambiguity : Grid orientation is defined up to 60° rotation. Use landmark cues to disambiguate.
Related Skills
spiking-neural-network-analysis - SNN analysis methodsbrain-network-connectivity - Brain network analysiscomputational-neuroscience-methods - General computational neuroscience
Resources
Citation @article{he2026canns,
title={CANNs: A Toolkit for Research on Continuous Attractor Neural Networks},
author={He, Sichao and Tuerhong, Aiersi and She, Shangjun and Chu, Tianhao and Wu, Yuling and Zuo, Junfeng and Wu, Si},
journal={arXiv preprint arXiv:2606.27783},
year={2026}
}
