CANNs: A Toolkit for Research on Continuous Attractor Neural Networks

2026-06-26

AI Summary

This work addresses the long-standing lack of a unified toolchain in continuous attractor neural network (CANN) research, where existing simulators suffer from insufficient abstractions and no standardized methods exist for extracting attractor structures from neural data. To bridge this gap, we present the first end-to-end open-source toolkit that integrates a Python modeling library (built on BrainPy/JAX), a high-performance Rust-based simulation engine, and an attractor structure analysis (ASA) pipeline grounded in persistent homology. The framework supports 1D/2D CANNs, grid cell networks, and hierarchical path integration models, unifying modeling, training, simulation, and empirical analysis within a single workflow. It enables faithful reproduction of cutting-edge results—such as slow feature analysis–based trajectory prediction and theta sweep phenomena—while achieving over two orders of magnitude speedup in simulation, thereby facilitating closed-loop validation between theoretical models and neural experiments.

Abstract

Continuous attractor neural networks (CANNs) are the canonical computational framework for how the brain encodes continuous variables such as spatial position, head direction, and movement direction, and explain the activity of hippocampal place cells, entorhinal grid cells, and head-direction cells. CANN research, however, is fragmented: most results rest on lab-specific implementations, general-purpose simulators lack CANN-specific abstractions, and the path from spike trains to attractor geometry in real recordings lacks a standardized toolkit. Here, we present a comprehensive open-source toolkit that unifies the full CANN research workflow. It combines three tightly integrated components: 1) canns, a Python library on BrainPy/JAX that provides standardized 1D/2D CANNs, spike-frequency-adaptation variants, grid cell networks, hierarchical path-integration models, and brain-inspired attractor architectures, together with curated datasets, task generators, an analyzer module and trainer modules for biologically plausible plasticity; 2) canns-lib, a Rust acceleration backend delivering hundreds-of-times speedups for spatial-navigation workloads and modest gains for Ripser-based persistent homology; 3) ASA (Attractor Structure Analyzer), a PySide6 pipeline applying persistent homology and cohomology to experimental neural recordings to detect ring-like and toroidal attractor signatures in real data. The toolkit ships with full-detail reproducible pipelines that recover recent CANN results including SFA-driven anticipative tracking, theta sweeps in head-direction/place/grid systems, and hierarchical path integration.

Problem

• Continuous Attractor Neural Networks
• standardized toolkit
• neural recordings
• attractor geometry
• research fragmentation

Innovation

• Continuous Attractor Neural Networks
• Persistent Homology
• Open-Source Toolkit
• Spike-Frequency Adaptation
• Hierarchical Path Integration