| name | distributed-hierarchical-temporal-memory-dhtm |
| description | Distributed Hierarchical Temporal Memory (D-HTM) neuromorphic framework enabling cross-entity preemptive warning via Shared Associative Memory (SAM) — extends HTM beyond reactive detection to distributed predictive reasoning (arXiv: 2606.31789) |
| version | 1.0.0 |
| created | 2026-07-01T00:00:00.000Z |
| author | Hermes Agent |
| category | neuroscience |
| tags | ["neuromorphic","hierarchical-temporal-memory","anomaly-detection","sparse-distributed-representations","predictive-warning","time-series"] |
| activation | D-HTM, distributed HTM, shared associative memory, SAM, cross-entity warning, neuromorphic anomaly detection, SDR, temporal memory, preemptive warning |
| arxiv_id | 2606.31789 |
| arxiv_url | https://arxiv.org/abs/2606.31789 |
Distributed Hierarchical Temporal Memory (D-HTM) for Cross-Entity Preemptive Warning
Overview
D-HTM is a neuromorphic framework that extends Hierarchical Temporal Memory (HTM) by introducing a Shared Associative Memory (SAM) for cross-entity knowledge transfer. While traditional HTM operates independently on each data stream (reactive), D-HTM enables preemptive warning — issuing alerts before local anomaly onset by reusing precursor signatures learned from related entities.
Key Innovation: Demonstrates that transferable precursor structure can emerge within shared Sparse Distributed Representation (SDR) space and be reused for predictive reasoning across distributed systems.
Paper: arXiv:2606.31789 (2026-06-30)
Authors: Pavia Bera, Jennifer Adorno, Sanjukta Bhanja
Core Architecture
D-HTM consists of three integrated components:
1. Spatial Pooler (SP) — Shared SDR Projection
- Projects observations from multiple entities into a common SDR space
- Uses HTM's canonical Spatial Pooling algorithm: overlap → inhibition → activation
- Creates a shared representation vocabulary enabling cross-entity comparison
- Key property: Similar precursors across entities map to overlapping SDRs
2. Temporal Memory (TM) — Entity-Specific Dynamics
- Each entity has its own TM module learning sequence statistics online
- TM maintains context via active dendrite segments with permanence values
- Learns transitions: given current SDR context, predict next SDR
- Detects anomalies when predictions fail (low overlap with actual input)
- Online learning: No offline training required; adapts continuously
3. Shared Associative Memory (SAM) — Cross-Entity Knowledge Transfer
- Novel component: Stores recurring pre-anomaly signatures shared across entities
- When TM detects precursor pattern in one entity, SAM can retrieve and check against other entities
- Enables preemptive warning: entity B gets alert before local anomaly based on entity A's precursor
- Maintains online learning capability of individual HTM modules
Methodology
Step-by-Step Pipeline
- Input encoding: Convert multivariate time series observations to binary SDRs via SP
- Temporal learning: Each entity's TM learns sequence transitions online
