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npx versuz@latest install hiyenwong-ai-collection-collection-skills-learning-hippo-biologically-detailed-ca3git clone https://github.com/hiyenwong/ai_collection.gitcp ai_collection/SKILL.MD ~/.claude/skills/hiyenwong-ai-collection-collection-skills-learning-hippo-biologically-detailed-ca3/SKILL.md--- name: learning-hippo-biologically-detailed-ca3 version: 1.0.0 description: Biologically detailed CA3 auto-associative memory model extending Hopfield/Marr with 10 populations, 47 compartments, 5 plasticity rules, and cholinergic modulation. Demonstrates multi-attractor dynamics absent from minimal baselines. date: 2026-04-23 source: arXiv:2604.20679 authors: Daniele Corradetti, Renato Corradetti tags: [hopfield, hippocampus, CA3, auto-associative-memory, attractor-dynamics, multi-attractor, biological-plausibility, Hebbian-learning, BCM, cholinergic-modulation, inhibitory-interneurons, pattern-completion] activation: hippocampus, CA3, Hopfield, attractor memory, auto-associative, pattern completion, multi-attractor, biological detail, Marr --- # Learning Hippo: Biologically Detailed CA3 Auto-Associative Memory ## Overview Biologically detailed extension of the classical Hopfield/Marr auto-associative memory model for hippocampal region CA3. Implements ten neural populations with 47 compartments and multi-rule plasticity, demonstrating three qualitative signatures absent from minimal Hopfield baselines. **arXiv:** 2604.20679v1 [cs.NE] | **Submitted:** 22 April 2026 ## Core Architecture ### Neural Populations (10 total) - **2 pyramidal subtypes** (asymmetric) - **8 GABAergic interneuron classes** — matching biological CA3 diversity ### Compartments - **47 compartments** per neuron model — enabling dendritic computation ### Multi-Rule Plasticity (5 mechanisms) 1. **Recurrent Hebb** — standard associative learning at recurrent synapses 2. **BCM anti-saturation** — Bienenstock-Cooper-Munro rule preventing runaway excitation 3. **Mossy-fiber short-term plasticity** — DG→CA3 pathway dynamics 4. **Endocannabinoid iLTD** — inhibitory long-term depression via retrograde signaling 5. **Burst-gated Hebb** — Hebbian learning gated by burst patterns ### Cholinergic Modulation - **Bimodal cholinergic cycle**: encoding mode (high ACh) vs. consolidation mode (low ACh) - Implements theta/gamma rhythm-dependent memory processing ## Key Results — Three Qualitative Signatures ### 1. Multi-Attractor Cross-Seed Behavior - At K=5 stored patterns with biologically realistic inhibitory proportions - 2 of 5 seeds converge to positive attractors with margin +0.10–0.22 - Effect size: Cohen's d=0.71, one-sided p=0.08 - **Absent** from minimal Hopfield baseline ### 2. Target-Selective Associative Recall - Paired (A, B) memory at K≥5 stored patterns - Full model retrieves B from partial cue of A (true associative recall) - Minimal model echoes A (pattern completion, not association) - Pearson margin Δ=+0.163 at K=5 ### 3. Reduced Cross-Seed Variance - Full model variance below minimal baseline under clean upstream input - Variance ratios: 1.0–3.0 (full model lower) - Indicates more robust attractor basins ## Evaluation Protocols - **Auto-associative** — pattern completion from partial cues - **Associative** — paired (A,B) recall across patterns - **Temporal** — sequential pattern recall - **Inhibitory-proportion manipulation** at N=256 neurons — systematic sweep of inhibition levels ## Methodological Contributions ### Biological Plausibility Checklist - [x] Multiple interneuron types (8 GABAergic classes) - [x] Asymmetric pyramidal subtypes - [x] Dendritic compartments (47 per neuron) - [x] Multiple plasticity mechanisms (5 rules) - [x] Cholinergic neuromodulation - [x] Realistic inhibitory proportions ### Architecture-Specific Signatures All three signatures appear consistently across independent evaluation regimes and are absent from minimal control — demonstrating that biological detail is *functionally necessary*, not merely cosmetic. ## Implementation Guide ### Model Parameters ``` N = 256 neurons (evaluated) K = 5+ stored patterns Populations = 10 (2 pyramidal + 8 interneuron) Compartments = 47 per neuron Plasticity rules = 5 (Hebb, BCM, STP, iLTD, burst-gated) Cholinergic modes = 2 (encode, consolidate) ``` ### Key Design Decisions 1. **Inhibitory proportion** is a critical parameter — multi-attractor behavior emerges only at biologically realistic ratios 2. **BCM rule** prevents saturation while maintaining selectivity 3. **Endocannabinoid iLTD** enables dynamic inhibitory balance adjustment 4. **Burst-gating** implements behavioral relevance filtering ## Comparison: Full vs. Minimal Hopfield | Feature | Minimal Hopfield | Learning Hippo | |---------|-----------------|----------------| | Populations | 1 (binary units) | 10 (2 pyr + 8 GABA) | | Compartments | 0 | 47 | | Plasticity rules | 1 (Hebb) | 5 | | Neuromodulation | None | Cholinergic (bimodal) | | Multi-attractor cross-seed | No | Yes (K=5) | | Target-selective recall | No | Yes (Δ=+0.163) | | Variance robustness | Baseline | Below baseline | ## Relevance and Applications ### Computational Neuroscience - Demonstrates *minimum biological complexity* needed for multi-attractor dynamics - Provides ground truth for simplified models — which biological details are essential? ### Neuromorphic Engineering - Multi-compartment, multi-population design is implementable in hardware - Cholinergic gating translates to mode-switching circuits - BCM rule provides inherent homeostatic regulation ### Brain-Inspired AI - Associative memory beyond simple pattern completion - Multi-attractor landscapes enable richer memory representations - Burst-gated learning translates to attention-gated learning in artificial systems ## Pitfalls and Limitations - N=256 is small — scaling behavior unknown - Single-region model (CA3 only, no EC/DG/subiculum) - No spike-timing dependent plasticity (STDP) in current version - Cholinergic modulation is simplified (binary, not continuous) - p=0.08 for cross-seed effect is marginal significance ## Key Takeaway > Biological detail matters. Multi-attractor dynamics, target-selective recall, and robust variance are emergent properties that require the full architecture — they cannot be recovered by tuning a minimal Hopfield network. ## References - Corradetti and Corradetti (2026). "Learning Hippo: Multi-attractor Dynamics and Stability Effects in a Biologically Detailed CA3 Extension of Hopfield Networks." arXiv:2604.20679 - Hopfield (1982). "Neural networks and physical systems with emergent collective computational abilities." - Marr (1971). "Simple memory: a theory for archicortex." - Bienenstock, Cooper, Munro (1982). "Theory for the development of neuron selectivity."