Merge Physical Neighborhood Mapping into Section 2 with relative image paths

README.md

@@ -33,7 +33,7 @@ Before this was a formal dataset, it was an attempt to solve a bare-metal proble
 
 The breakthrough came entirely by accident. I was running heavy mods—DLSS 4.0 and path tracing—on Cyberpunk 2077 and the Resident Evil 4 Remake. My workstation PC was screaming, pushing harder and louder than it ever did during crypto mining. That sparked the realization: *What if I used raw gaming telemetry data for neuromorphic spike data conversion?* What if I could use this intense hardware stress to create an artificial heartbeat for AI?
 
-When I pitched this idea, most people didn't believe the spike data conversion would work. But after refining the early thermal equations using that Resident Evil 4 telemetry, **Metis** was born—a MoE-based model and dataset architecture for exploring SNN quantization. The true origin of this project is deeply personal—it stems from my switch from Computer Science to Electrical Engineering, the struggle to learn on my own terms, and the drive to build an architecture that adapts dynamically.
+When I pitched this idea, most people didn't believe the spike data conversion would work. But after refining the early thermal equations using that Resident Evil 4 telemetry, **Metis** was born—a MoE-based SNN quantizaton model and dataset architecture for exploring SNN quantization. 
 
 ### Relationship to Spikenaut
 
@@ -41,9 +41,22 @@ When I pitched this idea, most people didn't believe the spike data conversion w
 
 ## 2. The Science: Semantic Attractor Clustering
 
-This dataset contains the raw bare-metal telemetry logs and latent space visualizations generated by the **Metis** architecture—a MoE model quantized for SNN research. The objective is to map the physical routing of LLM embeddings (specifically from the allenai/OLMoE-1B-7B-0125-Instruct-GGUF Mixture of Experts model) as they are processed by biologically-inspired neuronal fatigue mechanics. These insights directly inform the training of **Spikenaut**, my pure native SNN.
+This dataset contains the raw bare-metal telemetry logs and latent space visualizations generated by the routing encoder. The objective is to map the physical routing of LLM embeddings (specifically from the allenai/OLMoE-1B-7B-0125-Instruct-GGUF Mixture of Experts model) as they are processed by biologically-inspired neuronal fatigue mechanics. These insights directly inform the training of **Spikenaut**, my pure native SNN.
 
-The primary discovery documented here is **Semantic Attractor Clustering**. By applying L2 Normalization to F16-to-F32 casted embeddings, the SNN bounds the semantic pressure to a unit sphere. This prevents "Winner-Take-All" routing collapse and forces the network to organically balance the load. The resulting telemetry proves that the SNN physically routes different semantic concepts (e.g., abstract philosophy vs. rigid code syntax) into distinct, repeatable biological pathways.
+### The Discovery: Physical Neighborhood Mapping
+
+The primary breakthrough documented in this dataset is the organic, physical separation of semantic concepts into distinct routing bands. By applying L2 Normalization to the embeddings, the network bounds semantic pressure, forcing tokens to follow the biological **path of least resistance**.
+
+Telemetry visualizations prove that the Spike-based routing physically routes different cognitive tasks into isolated biological neighborhoods:
+
+#### Abstract Language Routing (The 2000-Route)
+When fed abstract English logic, the network distributes energy across multiple nodes, establishing a dominant attractor basin at the **2000-index walker route**, with secondary echoes in Walkers 700 and 1450.
+![English Logic Routing](first-day-testing-real-weights/second-test/map_olmoe_english_logic.png)
+
+#### Structured Logic Routing (The 600-800 Band)
+When fed rigid mathematical statements or raw Rust syntax, the network completely abandons the 2000-route. The tokens experience mathematical pushback in abstract centers and organically collapse into the exact same **600-800 frequency band**. This demonstrates that the network physically maps highly structured logic tasks to adjacent biological neighborhoods to conserve energy.
+![Math Logic Routing](first-day-testing-real-weights/fourth-test/map_olmoe_math_logic.png)
+![Rust Syntax Routing](first-day-testing-real-weights/third-test/map_olmoe_rust_syntax_logic.png)
 
 ## 3. Experiment Progression
 The dataset documents the chronological progression from synthetic baselines to actual semantic routing: