canns-lib

High-performance computational acceleration library for CANNs (Continuous Attractor Neural Networks), providing optimized Rust implementations for computationally intensive tasks in neuroscience and topological data analysis.

Overview

canns-lib is a modular library designed to provide high-performance computational backends for the CANNS Python package. It currently includes the Ripser module for topological data analysis, with plans for additional modules covering approximate nearest neighbors, dynamics computation, and other performance-critical operations.

Modules

🔬 Ripser - Topological Data Analysis

High-performance implementation of the Ripser algorithm for computing Vietoris-Rips persistence barcodes. Drop-in replacement for ripser.py with identical output (verified at the level of bar counts and per- dimension birth/death values on dense and sparse inputs).

Performance vs ripser.py (v0.9.0)

Measured by benchmarks/ripser/comprehensive_benchmark.py --fast (24 dense point-cloud tests, n ∈ {50, 100, 150, 300}, maxdim ∈ {1, 2}, categories: circle, sphere, torus, random, clusters, grid, swiss_roll, moons, circles). Cross-validated on macOS arm64 and Linux x86_64 (16-core A100 server, RAYON_NUM_THREADS=16).

Headline Linux maxdim=2 result: peak 1.91× on torus n=300. Per-dimension persistence values match ripser.py exactly on both platforms (counts_match=True | birth/death values match=True).

Performance vs ripser.py (v0.8.0 baseline, before this release)

Same harness, same matrices:

The dominant end-user win in this release is the shuffle null-model FFI shipped for downstream consumers — see the next section.

Shuffle null-model acceleration (v0.9.0 vs canns<1.2.1 legacy multiprocessing.Pool)

canns_lib._ripser_core.shuffle_null_model is a single Rust+rayon call that replaces the per-shuffle Python multiprocessing.Pool.imap loop in canns.analyzer.data.asa.tda._run_shuffle_analysis (used when TDAConfig.do_shuffle=True).

Measured with canns/scripts/bench_shuffle.py (macOS arm64, maxdim=1, 24-cell matrix T∈{60,300}×N∈{20,40,80}×n_shuffles∈{10,50,200,1000}):

Aggregate across all 24 cells: FFI 4.5 s vs legacy 2 175 s ≈ 36 min, a 484× total wall-clock ratio. From canns 1.2.1 onwards this is the default behaviour; older canns releases pick up the speedup as soon as they upgrade canns-lib to 0.9.0 (the FFI falls back to the legacy path automatically if missing).

Semantic difference: the FFI computes Euclidean distances on the raw (T, N) spike-train matrix; the legacy multiprocessing.Pool path applies timepoint downsampling, PCA, UMAP denoising, and an nbs distance threshold before ripser. The resulting null-distribution shape will differ even at the same random seed — opt out with use_ffi_shuffle=False if you specifically need the legacy pipeline.

Features

• Algorithmic improvements: Row-by-row edge generation, cleared coboundary enumeration
• Memory optimization: Structure-of-Arrays reduction matrix, k-major binomial coefficient table, packed (index, coefficient) EntryT (24 → 16 bytes), GAT-based static-dispatch cofacet enumeration with inline simplex-vertex stack array
• Parallel processing: Multi-threading with Rayon (enabled by default)
• Full Compatibility: Drop-in replacement for ripser.py with identical API
• Multiple Metrics: Support for Euclidean, Manhattan, Cosine, and custom distance metrics
• Sparse Matrices: Efficient handling of sparse distance matrices
• Cocycle Computation: Optional computation of representative cocycles
• Shuffle null-model FFI: single-call parallel Rust path for per-shuffle persistence (when used by canns)

Two experimental paths are kept under env-flag opt-in only: CANNS_RIPSER_USE_LOCKFREE=1 and CANNS_RIPSER_APPARENT=1. Both are correctness-fixed and match ripser.py outputs but are currently net- slower than sequential on this codebase.

🧭 Spatial Navigation (RatInABox parity)

Accelerated reimplementation of RatInABox environments and agents with PyO3/ Rust. Supports solid and periodic boundaries, arbitrary polygons, holes, and thigmotaxis wall-following.

Performance Snapshot

The spatial backend delivers ~700× runtime speedups vs. the pure-Python reference when integrating long trajectories. Benchmarked with benchmarks/spatial/step_scaling_benchmark.py (dt=0.02, repeats=1).

Plots and CSV summaries are emitted to benchmarks/spatial/outputs/.

Highlights

• Full parity with RatInABox API (Environment, Agent, trajectory import/export)
• Polygon & hole support with adaptive projection and wall vectors
• Parity comparison tools in example/trajectory_comparison.py
• Visualization utilities: drop-in replacements for RatInABox's plotting helpers (trajectory, heatmaps, histograms)
• Benchmark scripts for long-step drift and speedup under benchmarks/spatial/

Visualization Helpers

from canns_lib import spatial

env = spatial.Environment(dimensionality="2D", boundary_conditions="solid")
agent = spatial.Agent(env, rng_seed=2025)

for _ in range(2_000):
    agent.update(dt=0.02)

# Trajectory with RatInABox-style colour fading and agent marker
agent.plot_trajectory(color="changing", colorbar=True)

# Other helpers mirror RatInABox naming
agent.plot_position_heatmap()
agent.plot_histogram_of_speeds()
agent.plot_histogram_of_rotational_velocities()

See example/spatial_plotting_demo.py for a full script that produces the trajectory, heatmap, and histogram figures showcased above.

Drift Velocity Control

Guide agent movement toward target directions while maintaining natural stochastic motion (matches RatInABox API):

# Basic drift usage
agent.update(
    dt=0.02,
    drift_velocity=[0.05, 0.02],  # Target velocity vector
    drift_to_random_strength_ratio=5.0  # Drift strength relative to random motion
)

Parameters:

• drift_velocity: Target velocity vector (must match environment dimensionality)
• drift_to_random_strength_ratio: Controls balance between drift and randomness
  • 0.0 = pure random motion (no drift)
  • 1.0 = equal weighting (default)
  • > 1.0 = stronger drift toward target

Use cases: Goal-directed navigation, reinforcement learning, biased exploration.

See example/drift_velocity_demo.py for detailed examples with visualizations.

🚀 Coming Soon

• Dynamics: High-performance dynamics computation for neural networks
• And more...

Installation

From PyPI (Recommended)

pip install canns-lib

From Source

git clone https://github.com/Routhleck/canns-lib.git
cd canns-lib
pip install maturin
maturin develop --release

Quick Start

Using the Ripser Module

import numpy as np
from canns_lib.ripser import ripser

# Generate sample data
data = np.random.rand(100, 3)

# Compute persistence diagrams
result = ripser(data, maxdim=2)
diagrams = result['dgms']

print(f"H0: {len(diagrams[0])} features")
print(f"H1: {len(diagrams[1])} features")
print(f"H2: {len(diagrams[2])} features")

Advanced Options

# High-performance computation with progress tracking
result = ripser(
    data,
    maxdim=2,
    thresh=1.0,                    # Distance threshold
    coeff=2,                       # Coefficient field Z/2Z
    do_cocycles=True,              # Compute representative cycles
    verbose=True,                  # Detailed output
    progress_bar=True,             # Show progress
    progress_update_interval=1.0   # Update every second
)

# Access results
diagrams = result['dgms']          # Persistence diagrams
cocycles = result['cocycles']      # Representative cocycles
num_edges = result['num_edges']    # Number of edges in complex

Sparse Matrix Support

from scipy import sparse

# Create sparse distance matrix
row = [0, 1, 2]
col = [1, 2, 0]
data = [1.0, 1.5, 2.0]
sparse_dm = sparse.coo_matrix((data, (row, col)), shape=(3, 3))

# Compute with sparse matrix (automatically detected)
result = ripser(sparse_dm, distance_matrix=True, maxdim=1)

Compatibility

The ripser module maintains 100% API compatibility with ripser.py:

# These work identically
import ripser as original_ripser
from canns_lib.ripser import ripser

result1 = original_ripser.ripser(data, maxdim=2)
result2 = ripser(data, maxdim=2)

# Results are numerically identical
assert np.allclose(result1['dgms'][0], result2['dgms'][0])

Development

Building from Source

# Prerequisites
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
pip install maturin

# Build and install
git clone https://github.com/Routhleck/canns-lib.git
cd canns-lib
maturin develop --release --features parallel

# Run tests
python -m pytest tests/ -v

Running Benchmarks

cd benchmarks
python compare_ripser.py --n-points 100 --maxdim 2 --trials 5

Technical Details

Ripser Module Architecture

• Dual API paths: High-performance versions and full-featured versions with progress tracking
• Memory optimization: Structure-of-Arrays layout, intelligent buffer reuse
• Sparse matrix support: Efficient handling via neighbor intersection algorithms
• Progress tracking: Built-in progress bars using tqdm when available
• Parallel processing: Multi-threading with Rayon

Algorithmic Optimizations

• Dense edge enumeration: O(n²) row-by-row generation vs O(n³) vertex decoding
• Sparse queries: O(log k) binary search vs O(k) linear scan
• Cache-friendly data structures: SoA matrix layout, k-major binomial tables
• Packed simplex entries: (index, coefficient) packed into one 64-bit word, halving DiameterEntryT size from 24 → 16 bytes

License

Licensed under the Apache License, Version 2.0. See LICENSE for details.

Citation

If you use canns-lib in your research, please cite the toolkit paper (which describes both canns and canns-lib):

@misc{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},
  year         = 2026,
  eprint       = {2606.27783},
  archivePrefix = {arXiv},
  primaryClass  = {q-bio.NC},
  doi          = {10.48550/arXiv.2606.27783},
  url          = {https://arxiv.org/abs/2606.27783}
}

Plain text:

He, S., Tuerhong, A., She, S., Chu, T., Wu, Y., Zuo, J., & Wu, S. (2026). CANNs: A Toolkit for Research on Continuous Attractor Neural Networks. arXiv:2606.27783. https://arxiv.org/abs/2606.27783

If you want to cite this Rust backend specifically, you can additionally reference the Zenodo archive:

@software{canns_lib,
  title={canns-lib: High-Performance Computational Acceleration Library for CANNS},
  author={He, Sichao},
  url={https://github.com/Routhleck/canns-lib},
  year={2025}
}

Acknowledgments

Ripser Module

• Ulrich Bauer: Original Ripser algorithm and C++ implementation
• Christopher Tralie & Nathaniel Saul: ripser.py Python implementation
• Rust community: Amazing ecosystem of high-performance libraries

Related Projects

• Ripser: Original C++ implementation
• ripser.py: Python bindings for Ripser
• CANNS: Continuous Attractor Neural Networks
• scikit-tda: Topological Data Analysis in Python