Pattern Recognition Units (PRUs)

Pattern Recognition Units are the "atoms" of the PRIMUS architecture. Each PRU is a specialized worker that looks for a specific pattern in the sea of incoming data.

Structure of a PRU

Each unit typically consists of:

1. Input Channels: Connections from lower-level units or raw sensors.
2. Recognition Engine: The algorithm used to find the pattern (could be a small neural net, a MeTTa pattern, or a statistical model).
3. Feedback Mechanism: Connections from higher-level units that provide "context" or "expectations."
4. Output Signal: A measure of how strongly the pattern was detected.

The Recognition Process

Bottom-Up Processing

Units at the bottom of the hierarchy process raw sensory data (e.g., pixels, audio samples). They signal the presence of simple features to the units above them.

Top-Down Influence

Higher-level units provide expectations to the levels below.

• Example: If a high-level unit has detected a "Face," it sends top-down signals to lower units to "look harder" for eyes and a nose, even if the image is noisy.

Learning in PRUs

PRUs are not static. They can learn to identify new patterns through:

• Hebbian Learning: "Neurons that fire together, wire together."
• Competitive Learning: Units compete to represent different features of the data.
• Supervised Fine-Tuning: Using high-level goals to refine low-level recognition.

Distributed Nature

In a large PRIMUS system, there might be thousands or millions of PRUs. This allows for massive parallel processing and high fault tolerance.

---

Next: Hierarchical Integration