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Graph-native Python reimplementation of the Information Dynamics of Music (IDyOM) model that represents predictive memories as explicit graph objects for musical expectation modeling and network analysis.
Physics-aware end-to-end deep reinforcement learning methodology for quadcopter control with actuator dynamics modeling.
Reinforced Dreamer methodology for asymmetric reinforcement learning using latent guidance to improve world model representations and behaviors in model-based RL.
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| name | neural-receptive-fields-hyperbolic-geometry |
| description | Neural Receptive Fields via Hyperbolic Geometry |
Source: arXiv:2509.25453v2 (January 2026) Utility: 0.90 Authors: Yuri A. Dabaghian
This skill implements a physiologically grounded framework where neural receptive fields arise naturally from the effective hyperbolic geometry of scale-free networks - without synaptic fine-tuning. By embedding stimulus space at the boundary of hyperbolic geometry, localized activity patterns reflect stimulus structure.
Core Insight: Receptive field size depends on neuron's connectivity degree, following experimentally observed statistics. Generalizes across modalities (orientation, place selectivity).
scale_free_network - Network structure modelinghyperbolic_embedding - Geometric representationrate_based_model - Neural dynamics simulationspiking_model - Spiking neuron dynamicsstimulus_space - Boundary embeddingplace_field_data - Hippocampal recordingsUser: 如何理解方向选择性感受野的形成？
Agent: 双曲几何框架：
优势： 无需精细调整突触连接！
User: 海马位置场如何从网络几何产生？
Agent: 实验验证支持：
| 发现 | 描述 |
|---|---|
| 感受野大小 | 依赖神经元连接度 |
| 统计特性 | 符合实验观察 |
| 线性轨道 | 海马位置场验证 |
| 模态推广 | 方向 + 位置选择性 |
核心原理： 刺激空间边界 → 局部活动模式 → 感受野形成
Structure: Power-law degree distribution
Effective geometry: Hyperbolic space naturally represents scale-free networks
Key property: High-degree neurons → smaller receptive fields
Purpose: Map network to hyperbolic space
Stimulus space: Associated with hyperbolic boundary
Result: Localized activity patterns reflect stimulus structure
| Property | Observation |
|---|---|
| Size vs degree | Degree-dependent (high degree = small RF) |
| Statistics | Match experimental data |
| Modality | Orientation, place selectivity |
| Fine-tuning | Not required |
Scale-Free Network → Hyperbolic Geometry
↓
Stimulus Space Boundary → Neural Dynamics
↓
Localized Activity → Receptive Fields
Novel insight: Network structure → Stimulus encoding → Neural dynamics linkage without fine-tuning
Scale-Free Network → Hyperbolic Embedding
↓
Stimulus Space (Boundary)
↓
Neural Dynamics (Rate/Spiking) → Receptive Fields
↓
Experimental Validation (Place Fields)
| Metric | Result |
|---|---|
| RF formation | Natural emergence ✅ |
| Synaptic fine-tuning | Not required ✅ |
| RF statistics | Match experiments ✅ |
| Degree-dependence | Validated ✅ |
| Modality generalization | Orientation + Place ✅ |
| Hippocampal validation | Linear track place fields ✅ |
| Fine-Tuning | This Framework |
|---|---|
| Synaptic adjustment required | ✅ No fine-tuning |
| Limited biological plausibility | ✅ Physiologically grounded |
| RF statistics artificial | ✅ Match experiments |
| Single modality | ✅ Generalizes across modalities |
Why no fine-tuning needed?
brain-network-joint-embedding - Network embedding methodshyperbolic-brain-network-neurodegeneration - Hyperbolic brain networksmesoscale-brain-organization - Brain organization principlesneutral-theory-neural-dynamics - Neural dynamics theory