Module neuromorphic 

Neuromorphic Computing for Spatial Data Processing

This module implements brain-inspired computing paradigms for spatial algorithms, leveraging spiking neural networks, memristive computing, and neuroplasticity for energy-efficient adaptive spatial processing. These algorithms mimic biological neural computation to achieve extreme energy efficiency and real-time adaptation.

Features

• Spiking Neural Networks (SNNs) for spatial pattern recognition
• Memristive crossbar arrays for in-memory spatial computations
• Spike-timing dependent plasticity (STDP) for adaptive learning
• Event-driven spatial processing for real-time applications
• Neuromorphic clustering using competitive learning
• Temporal coding for multi-dimensional spatial data
• Bio-inspired optimization using neural adaptation mechanisms
• Homeostatic plasticity for stable learning
• Neuromodulation for context-dependent adaptation

Module Organization

Core Components

• core::events - Spike event structures and utilities
• core::neurons - Spiking neuron models with various dynamics
• core::synapses - Synaptic models with STDP and metaplasticity

Algorithm Implementations

• algorithms::spiking_clustering - SNN-based clustering
• algorithms::competitive_learning - Winner-take-all and homeostatic clustering
• algorithms::memristive_learning - Advanced memristive learning systems
• algorithms::processing - General neuromorphic processing pipeline

Examples

Basic Spiking Neural Network Clustering

use scirs2_core::ndarray::Array2;
use scirs2_spatial::neuromorphic::SpikingNeuralClusterer;

let points = Array2::from_shape_vec((4, 2), vec![
    0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0
]).unwrap();

let mut clusterer = SpikingNeuralClusterer::new(2)
    .with_spike_threshold(0.8)
    .with_stdp_learning(true)
    .with_lateral_inhibition(true);

let (assignments, spike_events) = clusterer.fit(&points.view()).unwrap();
println!("Cluster assignments: {:?}", assignments);
println!("Recorded {} spike events", spike_events.len());

Competitive Learning with Homeostasis

use scirs2_core::ndarray::Array2;
use scirs2_spatial::neuromorphic::HomeostaticNeuralClusterer;

let points = Array2::from_shape_vec((4, 2), vec![
    0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0
]).unwrap();

let mut clusterer = HomeostaticNeuralClusterer::new(2, 2)
    .with_homeostatic_params(0.1, 1000.0);

let assignments = clusterer.fit(&points.view(), 50).unwrap();
println!("Homeostatic clustering results: {:?}", assignments);

Advanced Memristive Learning

use scirs2_core::ndarray::{Array1, Array2};
use scirs2_spatial::neuromorphic::{AdvancedMemristiveLearning, MemristiveDeviceType};

let mut learning_system = AdvancedMemristiveLearning::new(
    4, 2, MemristiveDeviceType::TitaniumDioxide
).with_forgetting_protection(true);

let spatial_data = Array2::from_shape_vec((4, 4), vec![
    0.0, 0.0, 1.0, 1.0,
    1.0, 0.0, 0.0, 1.0,
    0.0, 1.0, 1.0, 0.0,
    1.0, 1.0, 0.0, 0.0
]).unwrap();
let targets = Array1::from_vec(vec![0.0, 1.0, 1.0, 0.0]);

let result = learning_system.train_spatial_data(
    &spatial_data.view(), &targets.view(), 50
).await.unwrap();
println!("Training completed with final accuracy: {:.2}",
         result.training_metrics.last().unwrap().accuracy);

Event-driven Neuromorphic Processing

use scirs2_core::ndarray::Array2;
use scirs2_spatial::neuromorphic::NeuromorphicProcessor;

let points = Array2::from_shape_vec((3, 2), vec![
    0.0, 0.0, 1.0, 1.0, 2.0, 2.0
]).unwrap();

let mut processor = NeuromorphicProcessor::new()
    .with_memristive_crossbar(true)
    .with_temporal_coding(true)
    .with_crossbar_size(64, 64);

// Encode spatial data as neuromorphic events
let events = processor.encode_spatial_events(&points.view()).unwrap();

// Process events through neuromorphic pipeline
let processed_events = processor.process_events(&events).unwrap();
println!("Processed {} events", processed_events.len());

Performance Considerations

Neuromorphic algorithms are designed for:

• Energy efficiency: Event-driven processing reduces computation
• Real-time adaptation: Online learning without full retraining
• Noise tolerance: Biological inspiration provides robustness
• Scalability: Distributed processing capabilities

Biological Inspiration

These algorithms draw inspiration from:

• Synaptic plasticity: Adaptive connection strengths
• Homeostatic regulation: Maintaining stable activity levels
• Neuromodulation: Context-dependent learning control
• Memory consolidation: Strengthening important patterns
• Competitive dynamics: Winner-take-all neural competition

Re-exports

pub use core::events::SpikeEvent;

pub use core::events::SpikeSequence;

pub use core::neurons::AdaptiveSpikingNeuron;

pub use core::neurons::SpikingNeuron;

pub use core::synapses::HomeostaticSynapse;

pub use core::synapses::MetaplasticSynapse;

pub use core::synapses::Synapse;

pub use algorithms::competitive_learning::AdaptationScale;

pub use algorithms::competitive_learning::CompetitiveNeuralClusterer;

pub use algorithms::competitive_learning::HomeostaticNeuralClusterer;

pub use algorithms::competitive_learning::HomeostaticNeuron;

pub use algorithms::competitive_learning::LearningRateAdaptation;

pub use algorithms::competitive_learning::MetaplasticityController;

pub use algorithms::competitive_learning::MultiTimescaleAdaptation;

pub use algorithms::memristive_learning::AdvancedMemristiveLearning;

pub use algorithms::memristive_learning::ConsolidationEvent;

pub use algorithms::memristive_learning::ConsolidationRules;

pub use algorithms::memristive_learning::ConsolidationType;

pub use algorithms::memristive_learning::ForgettingProtectionRules;

pub use algorithms::memristive_learning::HomeostaticMechanism;

pub use algorithms::memristive_learning::HomeostaticSystem;

pub use algorithms::memristive_learning::LearningHistory;

pub use algorithms::memristive_learning::LearningRateAdaptation as MemristiveLearningRateAdaptation;

pub use algorithms::memristive_learning::MemristiveCrossbar;

pub use algorithms::memristive_learning::MemristiveDeviceType;

pub use algorithms::memristive_learning::MetaplasticityRules;

pub use algorithms::memristive_learning::NeuromodulationEffects;

pub use algorithms::memristive_learning::NeuromodulationSystem;

pub use algorithms::memristive_learning::NeuromodulatorReleasePatterns;

pub use algorithms::memristive_learning::PerformanceMetrics;

pub use algorithms::memristive_learning::PlasticityEvent;

pub use algorithms::memristive_learning::PlasticityEventType;

pub use algorithms::memristive_learning::PlasticityLearningRates;

pub use algorithms::memristive_learning::PlasticityMechanism;

pub use algorithms::memristive_learning::PlasticityThresholds;

pub use algorithms::memristive_learning::PlasticityTimeConstants;

pub use algorithms::memristive_learning::PlasticityType;

pub use algorithms::memristive_learning::ThresholdAdaptation;

pub use algorithms::memristive_learning::TrainingResult;

pub use algorithms::processing::NeuromorphicProcessor;

pub use algorithms::spiking_clustering::NetworkStats;

pub use algorithms::spiking_clustering::SpikingNeuralClusterer;

Modules

algorithms

Neuromorphic Computing Algorithms

core

Core Neuromorphic Computing Components

utils

Neuromorphic utilities

Structs

NeuromorphicConfig

Neuromorphic system configuration

NeuromorphicFactory

Neuromorphic system factory

Enums

NeuromorphicCapability

Neuromorphic processing capabilities

Traits

NeuromorphicAlgorithm

Neuromorphic algorithm trait for unified interface