BrainStem is an advanced neuro-symbolic cognitive architecture designed to facilitate lifelong learning. This innovative framework transforms text corpora into a probabilistic relational world model, all while operating on an SQLite backend. It introduces a unique homeostatic loop governed by 12 digital neurotransmitters, which autonomously adjust learning rates, track contextual hypotheses, and support memory consolidation through sub-1Hz slow-wave sleep and validation critic gates.

Core Features

• Biologically Inspired Learning: BrainStem operates based on key principles drawn from biological systems, focusing on how context, uncertainty, and contradictions interact over time rather than merely storing isolated facts.

• Dynamic Learning Mechanisms:

  • Active and Offline Processing: Switches between active data ingestion and optimization phases, enabling continuous self-evaluation and knowledge refinement.
  • Knowledge Distillation: Compares new data against established records to filter inconsistencies and solidify reliable information.
  • Equilibrium Control: Implements monitoring routines to maintain balanced performance levels, adjusting its learning variables according to detected states.

• Autonomous Architecture: It functions through a two-stage data pipeline, consisting of:

  1. Inference-Free Pre-Parsing: Structuring raw data inputs before the learning phases commence.
  2. Autonomous Learning: Continuously processes structured data while integrating neuromodulatory feedback to adapt learning strategies.

Digital Neuromodulators

The architecture utilizes 12 digital neuromodulators, akin to biological substances, each playing a specific role in regulating learning dynamics:

Neuromodulator	Function Description
Dopamine	Outcome and gap-closure signal
Serotonin	Consolidation and stability signal
Glutamate	Drives exploration and learning activity
GABA	Global inhibition and balance signal
Noradrenaline	Error and persistent-pressure signal
Acetylcholine	Novelty and attention signal
Adenosine	Sleep-pressure regulator
Endocannabinoids	Gain control mechanism
Cortisol	Stability regulation
Histamine	Wakefulness and arousal signal
Orexin	Curiosity and endurance drive
BDNF	Growth and consolidation substrate

Architectural Principles

BrainStem's design hinges on several foundational philosophies:

• Emphasizes learning before rule establishment, keeping the paths adaptable to new insights.
• Preserves errors as valuable evidence for future revisions and consolidations.
• Implements protective mechanisms that delay the promotion of facts until rigorous validation occurs.

Experimental Development

As a highly experimental and continuously evolving project, BrainStem's current status is focused on rigorous validation of its foundational architecture, with a commitment to engineering discipline and scalability. The project introduces advanced methodologies and AI-assisted engineering techniques to explore cognitive architectures more effectively.

Conclusion

BrainStem is positioned as a pioneering effort in neuro-symbolic modeling, providing a robust platform for exploring autonomous learning strategies that echo the complexities of human cognition. This architecture seeks not only to advance theoretical understanding but also to enhance practical implementations in artificial intelligence.