Rank Order N Of M Codes For Sparse Distributed Mem

# Rank-Order N-of-M Codes for Sparse Distributed Memory: Disentangling Representation and Learning Effects in Noise Robustness Against Contemporary Neuromorphic Architectures

Derived from arXiv:2607.02967 - Rank-Order N-of-M Codes for Sparse Distributed Memory: Disentangling Representation and Learning Effects in Noise Robustness Against Contemporary Neuromorphic Architectures

## Core Concept
Large language models remain limited as continual learning systems, motivating renewed interest in Sparse Distributed Memory (SDM) as an explicit online episodic memory. CALM (Nechesov and Ruponen, 2025) identifies its threshold-binary encoder as an open design question. This paper evaluates rank-order N-of-M encoding (Furber et al., 2007) as an alternative. We make three contributions. First, a faithful reimplementation validates the published architecture by confirming exact equivalence betwee...

## Key Insights
- Derived from arXiv:2607.02967
- Published: 2026-07-03
- Utility Score: 1.00
- Authors: Joy Bose

## Activation
rank-order-n-of-m-codes-for-sparse-distributed-mem, 2607.02967

## References
- arXiv: https://arxiv.org/abs/2607.02967