Spiking RNN

The spiking package provides leaky integrate-and-fire (LIF) spiking neural networks mapped from continuous rate RNNs.

Core Modules

• LIF Network Function
• Tasks
• Evaluation Tasks
• Scaling Factor Grid Search
• Utilities

Module Overview

LIF_network_fnc.py

Core function for converting rate RNNs to spiking networks and running LIF simulations.

tasks.py

Task-based architecture for spiking neural network evaluation with abstract base classes and concrete task implementations.

eval_tasks.py

Unified, extensible evaluation interface for spiking neural networks on cognitive tasks.

lambda_grid_search.py

Grid search optimization for finding optimal scaling factors in rate-to-spike conversion.

utils.py

Utility functions for model loading, network configuration, and spike data analysis.

Quick Reference

Main Functions:

• LIF_network_fnc(): Core rate-to-spike conversion and simulation

• evaluate_task(): Unified evaluation interface for all tasks

• lambda_grid_search(): Optimize scaling factors

Task Classes:

• AbstractSpikingTask: Base class for spiking task evaluation

• GoNogoSpikingTask: Go-NoGo task for spiking networks

• XORSpikingTask: XOR task for spiking networks

• ManteSpikingTask: Mante task for spiking networks

• SpikingTaskFactory: Factory for creating spiking task instances

Configuration:

• SpikingConfig: Configuration dataclass for spiking RNN parameters

• create_default_spiking_config(): Create default configuration

Utility Functions:

• load_rate_model(): Load rate model from .mat file

• format_spike_data(): Format spike data for analysis

• SpikingTaskFactory.register_task(): Register custom tasks