Module neuromorphic 

Neuromorphic Optimization Module

This module implements optimization algorithms inspired by neuromorphic computing and neural network architectures. These methods leverage principles from biological neural networks and neuromorphic hardware for efficient optimization.

Key Features

• Spiking Neural Network Optimization: Event-driven optimization using spike trains
• Memristive Optimization: Algorithms that mimic memristor behavior for adaptive optimization
• Spike-Timing Dependent Plasticity (STDP): Learning rules based on spike timing
• Neuromorphic Gradient Descent: Event-driven gradient computation
• Liquid State Machines: Reservoir computing for optimization
• Neural ODE Optimization: Continuous-time neural network optimization

Applications

• Low-power optimization for edge devices
• Real-time adaptive control systems
• Bio-inspired machine learning
• Neuromorphic hardware optimization
• Event-driven optimization problems

Re-exports

pub use event_driven::*;

pub use liquid_state_machines::*;

pub use memristive_optimization::*;

pub use neural_ode_optimization::*;

pub use spiking_networks::*;

pub use stdp_learning::*;

Modules

event_driven

Event-Driven Optimization

liquid_state_machines

Liquid State Machines for Optimization

memristive_optimization

Memristive Optimization

neural_ode_optimization

Neural ODE Optimization

spiking_networks

Spiking Neural Network Optimization

stdp_learning

Advanced Spike-Timing Dependent Plasticity (STDP) Learning

Structs

BasicNeuromorphicOptimizer

Basic neuromorphic optimizer implementation

NeuromorphicConfig

Configuration for neuromorphic optimization algorithms

NeuromorphicNetwork

Neuromorphic optimization network

NeuronState

State of a neuromorphic neuron

SpikeEvent

Spike event in neuromorphic simulation

Traits

NeuromorphicOptimizer

Trait for neuromorphic optimization algorithms

Functions

neuromorphic_optimize

Convenience function to create and run neuromorphic optimization