Bio-inspired Predictive Intelligence

Current AI systems excel at statistical pattern recognition but struggle with continuously changing environments that require temporal reasoning, adaptation and efficient decision making. RebuildAI is developing a new generation of artificial intelligence that combines Spiking Neural Networks, predictive self-supervised learning based on Yann LeCun’s Joint Embedding Predictive Architecture (JEPA), and biological validation using human neuro-organoids.

Rather than competing directly with deep learning on image classification or large language models, our goal is to develop AI for problems where biological intelligence demonstrates clear advantages: adaptive robotics, real-world navigation, autonomous decision making and dynamic environments. We validate our mathematical models against living neuro-organoid systems, enabling a unique closed-loop research methodology that continuously improves the computational architecture.

Research Objective

Biologically Inspired Predictive Temporal Learning for Spiking Neural Networks

  • Integrate JEPA into event-driven SNNs
  • Learn temporal predictive representations
  • Validate with neuro-organoid recordings
  • Demonstrate adaptive performance

Methodology

Computational Neuroscience Meets Modern Machine Learning

  • Develop hierarchical SNNs with temporal dynamics
  • Train JEPA-based predictive representations
  • Evaluate on dynamic navigation tasks
  • Record neuro-organoid activity (MEA, LFP, calcium imaging)
  • Compare biological and computational dynamics
  • Refine the model iteratively

Expected Impact

Toward a new generation of AI for adaptive intelligence.

  • Predictive SNN with temporal world models
  • Dynamic navigation benchmark
  • Biologically validated computational framework
  • Energy-efficient, adaptive AI
  • Scalable bio-inspired AI platform