SP_NN: Building a “Living” Autonomous Neural System

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Project Overview: Building a “Living” Autonomous Neural System

This project focuses on developing a fully autonomous neural network system designed to transition from evolutionary design to self-regulated learning and ultimately to task-specific functionality. By adhering to first principles, this system aims to blur the line between artificial constructs and living systems, creating a neural network capable of sensing, adapting to, and thriving within its operational constraints.

You can get the project files Here. Note Everything as always is under development and this project is far from fully realized.

Core Objectives

  1. Autonomous Network Construction: The system evolves an initial neural network topology using a genetic algorithm (GA), balancing structural efficiency with adaptability.
  2. Resource-Aware Self-Regulation: The network learns to operate within an allotted resource budget, developing intrinsic mechanisms for energy conservation and task prioritization.
  3. Reinforced Task Learning: The system transitions to task-specific reinforcement learning, using its intrinsic resource-awareness to guide adaptive behaviors.

Phases of Development

Phase 1: Initial Network Wiring

  • Objective: Define the neural network’s initial topology and configuration through evolutionary design.
  • Mechanics:
    • The GA operates within the network’s 3D spatial environment, wiring neurons (input, hidden, output) based on proximity and structural constraints.
    • Parameters:
      • Volume size, neuron counts, radii ranges, and activation thresholds.
      • Fitness is based on network health metrics:
        • Structural Connectivity: Ensuring robust connections between neurons.
        • Connection Density: Avoiding sparse or overly dense regions.
        • Proximity Score: Encouraging clustering without redundancy.
    • Result: An initial network topology optimized for adaptability and efficiency.

Phase 2: Activation Bank and Self-Regulation Training

  • Objective: Train the network to sense and regulate its own resources (activations) while responding to dynamic inputs.
  • Mechanics:
    • Introduce an Activation Bank:
      • Allotted a fixed number of activations per time window.
      • Serves as both a constraint and a feedback mechanism for fitness evaluation.
    • Connect the Activation Bank as an input node to the network:
      • Allows the network to sense its current resource balance.
    • Apply random inputs over a sliding time window:
      • The network processes inputs while maintaining resource balance.
      • Rewards are proportional to performance within constraints, incentivizing efficient and adaptive behavior.
    • Fitness Feedback:
      • Networks that effectively manage activations and maintain task performance are rewarded in subsequent generations.

Phase 3: Reinforced Learning for Task-Specific Behaviors

  • Objective: Transition from general self-regulation to task-specific learning, leveraging the network’s learned reward mechanisms.
  • Mechanics:
    • The Activation Bank remains central to the reward system:
      • Performance on specific tasks (e.g., pattern recognition, decision-making) increases activation rewards.
      • Poor performance results in resource scarcity, encouraging behavioral adjustments.
    • Reinforcement Learning:
      • Use task-specific rewards to guide network adaptation.
      • Encourage the emergence of efficient, goal-oriented behaviors while maintaining resource-awareness.
    • Long-Term Adaptability:
      • The system retains the ability to adjust its topology and behavior to meet evolving task demands.

System Components

1. Genetic Algorithm (GA)

  • Operates as the architect of the neural network’s initial topology.
  • Encodes neurons and connections as spatial entities within a bounded 3D environment.
  • Fitness metrics guide the evolution of:
    • Connection patterns.
    • Neuron radii and positions.
    • Network efficiency and adaptability.

2. Spatial Neural Network

  • Neurons:
    • Categorized into input, hidden, and output types, each with distinct properties and roles.
    • Dynamically adjust radii based on activation history, mimicking neuroplasticity.
  • Connections:
    • Formed based on spatial proximity and neuron compatibility.
    • Updated iteratively to reflect evolving network states.
  • Resource Constraints:
    • Governed by the Activation Bank, limiting activations per time window to enforce efficiency.

3. Activation Bank

  • Acts as both a resource constraint and a feedback mechanism:
    • Limits neuron activations, simulating energy usage.
    • Rewards efficient and effective network behaviors.
    • Connects directly to the network, enabling resource sensing and adaptation.

4. Task Environment

  • Provides dynamic inputs for the network to process.
  • Randomized patterns ensure robustness and generalization during Phase 2.
  • Specific task objectives shape learning and adaptation during Phase 3.

Key Innovations

1. Resource Awareness and Self-Regulation

  • The Activation Bank introduces resource-awareness, enabling the network to:
    • Sense and manage its own operational state.
    • Develop intrinsic mechanisms for energy conservation and behavioral optimization.

2. Pavlovian Reward Conditioning

  • By linking rewards to task performance, the network learns to:
    • Associate success with resource abundance.
    • Adapt behaviors to maximize long-term rewards.

3. Bridging Evolution and Learning

  • Seamless transition from evolutionary design (GA) to adaptive learning (reinforcement) enables:
    • Open-ended exploration during Phase 1.
    • Task-specific refinement during Phase 3.

4. Open-Ended Adaptability

  • The system’s design supports continuous learning and adaptation, fostering emergence and resilience in unpredictable environments.

Experimental Goals and Metrics

1. Network Health Metrics

  • Structural Connectivity: Proportion of connected components.
  • Connection Density: Ratio of existing to potential connections.
  • Proximity Score: Clustering of neurons in 3D space.
  • Combined Health: Weighted aggregate of the above metrics.

2. Activation Efficiency

  • Activation Budget Utilization: Ratio of activations to budget.
  • Recovery from Debt: Time taken to return from negative to positive balance.

3. Task Performance

  • Accuracy: Success rate on task objectives.
  • Resource Efficiency: Performance per activation unit.

4. Emergent Behaviors

  • Self-Regulation: Ability to maintain activation balance over varying inputs.
  • Novelty: Emergence of unique or unexpected behaviors.
  • Adaptability: Capacity to generalize across tasks or environments.

Future Directions

1. Multi-Agent Dynamics

  • Introduce competing or cooperating networks to study emergent interactions and resource-sharing strategies.

2. Dynamic Reward Scaling

  • Adjust reward mechanisms based on task complexity or network state to encourage continuous improvement.

3. Real-World Applications

  • Explore applications in robotics, adaptive control systems, and energy-efficient infrastructure.

Conclusion

This project represents a novel paradigm in neural network design, focusing on creating systems that are more alive than artificial. By integrating evolutionary design, resource-aware self-regulation, and task-specific learning, it aims to redefine how we build autonomous, adaptive systems that thrive in dynamic environments.

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