Module neural_processing 

Neural Processing Module for Advanced Fusion Algorithms

This module implements self-organizing neural processing capabilities that enable neural networks to reorganize their own structure based on input patterns and processing requirements. The implementation is inspired by biological neural plasticity and includes various activation functions ranging from classical to quantum-inspired variants.

Key Features

• Self-Organizing Networks: Neural networks that adapt their topology dynamically
• Multiple Activation Functions: Classical (Sigmoid, Tanh, ReLU) and advanced (Quantum, Biological, Consciousness-inspired)
• Real-time Adaptation: Networks that learn and reorganize during processing
• Quantum-Classical Hybrid: Seamless integration of quantum and classical processing paradigms
• Biological Inspiration: Leaky integrate-and-fire neurons and spike-based processing
• Consciousness Modeling: Attention and awareness mechanisms for intelligent processing

Processing Flow

1. Network Reorganization: Structure adapts based on input patterns
2. Connection Processing: Calculate inputs from connected nodes
3. Activation: Apply appropriate activation function
4. State Update: Update node internal states
5. Learning: Apply self-organization learning rules
6. Global Update: Update network-wide properties

Functions

self_organizing_neural_processing

Self-Organizing Neural Processing