https://github.com/rexionmars/bicnet

BICNet is a biomimetic neural network architecture that integrates multiple systems inspired by biological brain processes to create a holistic simulation of consciousness and cognition.
https://github.com/rexionmars/bicnet

bioinformatics brain-computer-interface neural-network

Last synced: about 1 month ago
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BICNet is a biomimetic neural network architecture that integrates multiple systems inspired by biological brain processes to create a holistic simulation of consciousness and cognition.

• Host: GitHub
• URL: https://github.com/rexionmars/bicnet
• Owner: rexionmars
• License: mit
• Created: 2024-12-13T03:07:03.000Z (5 months ago)
• Default Branch: master
• Last Pushed: 2024-12-20T19:21:51.000Z (5 months ago)
• Last Synced: 2025-04-05T05:36:12.153Z (about 1 month ago)
• Topics: bioinformatics, brain-computer-interface, neural-network
• Language: Python
• Homepage:
• Size: 42.8 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
## BioInspired Consciousness Network (BICNet)

BICNet is a biomimetic neural network architecture that integrates multiple systems inspired by biological brain processes to create a holistic simulation of consciousness and cognition. The project implements a virtual rat model with complex neural processing capabilities, adaptive memory, and emergent conscious states.

Key Features:

1. Multi-System Architecture:

   * Biological Memory System

   * Complex Interaction Neural Network

   * Dense Gene Network

   * Advanced Neural Dynamics

   * Integrated Consciousness System

2. Biomimetic Components:

   * Dynamic Synaptic Plasticity

   * Neural Gene Regulation

   * Regional Neuromodulation

   * Multimodal Sensory Processing

   * Conscious Information Integration

3. Cognitive Capabilities:

   * Adaptive Learning

   * Episodic Memory

   * Emotional Processing

   * Emergent Consciousness

   * Metacognition

Technical Innovations:

1. Multi-Scale Integration:

   * Molecular Level (gene expression)

   * Cellular Level (neural dynamics)

   * Network Level (synaptic interactions)

   * System Level (emergent consciousness)

2. Processing Mechanisms:

   * STDP (Spike-Timing-Dependent Plasticity)

   * Neural Homeostasis

   * Epigenetic Regulation

   * Global Information Integration

   * Conscious State Dynamics

Applications:

1. Neuroscientific Research:

   * Brain Process Modeling

   * Conscious State Studies

   * Neurological Disease Investigation

2. Artificial Intelligence:

   * Advanced Cognitive Systems

   * Biologically Plausible Learning

   * Adaptive Decision Making

3. Robotics:

   * Biomimetic Behavioral Control

   * Adaptive Navigation

   * Natural Interaction

Differentiators:

1. Deep Biological Foundation:

   * Modeling of real molecular processes

   * Integration of neurobiological mechanisms

   * Simulation of conscious states

2. Integrated Architecture:

   * Multiple processing levels

   * System interaction

   * Complex behavior emergence

3. Flexibility and Adaptability:

   * Continuous learning

   * Adaptation to new environments

   * Emergent behavioral responses

This architecture represents a significant advancement toward more biologically plausible AI systems, combining aspects of neural processing, gene regulation, and conscious states in a single integrated and functional framework.