Sparse Distributed Memory¶
Retrieve binary data through overlapping Hamming neighborhoods of sparsely realized hard locations whose counters hold distributed traces of stored words.
Core Idea¶
Sparse distributed memory (SDM) samples a small set of physical hard locations from an enormous binary address space. An address activates locations within a Hamming-distance radius. Writing adjusts per-bit counters across those locations; reading from a similar cue sums and thresholds activated counters to reconstruct a data word. The same data trace is distributed across many locations, enabling conditional retrieval from a noisy cue.[^ref-3d8be6e3539b]
Scope of Application¶
Kanerva introduced SDM as a mathematical model of long-term associative recall, not proof of its neural implementation. The architecture can be realized as a computational memory; a later robot-navigation study used encoded visual sequences and examined limits from noise, saturation and encoding.[ref-3d8be6e3539b][ref-39f7484a84f6]
Clarity¶
The virtual address need not be a physical hard location, and the data word need not equal its address. Sparse hard locations and overlapping cue neighborhoods distinguish SDM from exact-address RAM. Recovery is not assured for distant cues or excessive interfering writes.[^ref-3d8be6e3539b]
Manages Complexity¶
An enormous possible address space is served by relatively few realized locations. In a constructed four-bit illustration, writing \(1010\) at address \(0000\) updates hard locations \(0000,0001\) at radius one; cue \(0010\) reads \(0000,0011\), and their counter sum recovers \(1010\). A second write of \(0101\) at \(0011\) makes the same cue's two selected counter vectors cancel, so the stated nonpositive-to-zero rule returns \(0000\). This tiny calculation illustrates both overlap and interference, not a general success rate or high-dimensional guarantee.[^ref-3d8be6e3539b]
Abstract Reasoning¶
State binary dimension, hard-location sample, Hamming radius and counter rule. Compare the locations activated by a stored address and a retrieval cue; then assess overlap and interference before claiming successful recall. Repeated reading is not a universal convergence guarantee.[ref-3d8be6e3539b][ref-39f7484a84f6]
Knowledge Transfer¶
The same write/read roles apply in cognitive simulation and robot view-sequence memory, while the encoded data and performance requirements differ. A strict edge to the live Associative Memory prime was rejected: its stored-item content geometry is narrower than SDM's separable Hamming-addressed hard locations and data words. SDM is an unparented node in the current DAG pending an exact genus.[ref-3d8be6e3539b][ref-39f7484a84f6]
[^ref-3d8be6e3539b]: Kanerva, original-author SDM chapter (1993). [^ref-39f7484a84f6]: Mendes and colleagues, original robot-navigation study, abstract.
Neighborhood in Abstraction Space¶
Sparse Distributed Memory sits in a sparse region of the domain-specific corpus (97th percentile for distinctiveness): few abstractions share its structure, so a faithful description tends to retrieve it precisely.
Family — Unclustered & Miscellaneous (2551 abstractions)
Nearest neighbors
- Flat memory model — 0.79
- Classless Inter-Domain Routing — 0.77
- Hash Table — 0.77
- Quotient Filter — 0.76
- Hamming Scheme — 0.76
Computed from structural-signature embeddings · 2026-10-08