Add missing _index.org files for 7 sections: stages, ai-agents-scoping, compliance, impact, social-protocol, verification, resources — rebuild clean after file reorganization

projects/passepartout/architecture/neuro-neuromorphic-symbolic-comparison.org

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+:PROPERTIES:
+:CREATED:  [2026-06-01 Mon]
+:ID:       a7b8c9d0-1e2f-3a4b-5c6d-7e8f90abcdef
+:END:
+#+title: ANN vs Neuromorphic vs Symbolic — When Each Mathematics Fits
+#+filetags: :passepartout:architecture:neurosymbolic:math:
+
+**ANN vs Neuromorphic vs Symbolic**
+
+**Core insight**
+
+The gap between ANNs and biology is not substrate (binary vs analog). Both are continuous mathematics running on discrete hardware. The real differences are architectural:
+
+1. Memory-binding: ANNs store weights separately from compute (von Neumann bottleneck). Biology co-locates weight and signal at the synapse.
+2. Local vs global learning: ANNs need a global error signal backpropagated through every layer. Biology uses purely local plasticity (STDP) — each synapse adjusts based on its own pre/post partners.
+3. Time: Biology is asynchronous, continuous, with rich temporal dynamics. ANNs are synchronous — everything computed in lockstep. Recurrence is an awkward addition.
+4. One substrate, many functions: A biological synapse does memory, signal propagation, temporal integration, and plasticity in one structure. ANNs separate these across different passes and optimizers.
+
+**When each mathematics is appropriate**
+
+| Mathematics | Naturally good at | Awkward at |
++------------+-------------------+------------+
+| ANN / gradient descent | Smooth function approximation, interpolation, pattern completion from dense data | Symbolic reasoning, exact constraints, sparse data, multi-step verification |
+| Neuromorphic / spiking dynamics | Temporal pattern recognition, event-driven control, low-power always-on sensing | Complex multi-step planning, precise arithmetic, storing large lookup tables |
+| Symbolic / deduction | Exact reasoning, proof, constraint satisfaction, verifiable behavior | Learning from raw data, generalization, handling noisy inputs |
+
+**How Passepartout uses all three**
+
+- LLM (ANN on GPU) handles the noisy real world — language, vision, imperfect input
+- Screamer (symbolic constraint search on CPU) handles combinatorial reasoning — "find valid configuration"
+- ACL2 (deductive proof) handles the verifiable kernel — "prove this decision follows from rules"
+- P150 (RISC-V parallel accelerator, in-between arch) handles ambient awareness, parallel dispatch, anomaly detection
+
+Each mathematics where it belongs. The failure mode of both pure ANN and pure symbolic approaches is forcing one mathematics to do what the other is better at.
+
+**The neuromorphic opportunity**
+
+A neuromorphic chip (Loihi-level) would add unsupervised temporal learning — learning daily rhythms, behavioral patterns, and detecting deviations without training, labels, or LLM involvement. This is the difference between responding to commands and anticipating needs.
+
+But the P150 gets 80% there with programmable cores controlled directly, without waiting for neuromorphic hardware to mature.