Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.22083

Beyond the Hamming Cliff: Compact Spatial Encodings Preserving Semantic Distance in Spiking Neural Networks

Sergio Davies·Muhammad Aitsam·Morgan-Tyler Stone·Alessandro Di Nuovo·Liangxiu Han
View original source ↗Read source document ↗Export metadata ↓

Abstract

By pre-processing input signals with Gray, Thermometer, Johnson and hybrid encodings before passing them to spiking neural networks running on SpiNNaker hardware, Davies et al.(2025) address the semantic-spatial disparity that causes the ``Hamming cliff'' in standard binary representations. Continuous semantic patterns are presented as a set of simultaneous spikes at the input layer, preserving ultra-low latency processing. Training the network on multiple continuous values under each scheme, and measuring accuracy, required homeostatic scaling and false-positive rates, shows that removing the cliff is necessary but not sufficient: Gray encoding removes it yet retains little global distance structure, whereas Thermometer and Johnson preserve semantic distance at a code width that grows with the value range. The proposed hybrid encodings contain all three as limiting cases of a single parameter, recovering most of that structure at a fraction of the width. The homeostatic factor is shown to set a radius of tolerance around each trained pattern, so that the choice of encoding alone decides which values the network treats as similar.

Explore related subjects

cs.NE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sergio Davies, Muhammad Aitsam, Morgan-Tyler Stone, Alessandro Di Nuovo, Liangxiu Han. 2026-10-15. Beyond the Hamming Cliff: Compact Spatial Encodings Preserving Semantic Distance in Spiking Neural Networks. https://arxiv.org/abs/2610.22083

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

First Mathematical Runtime Analyses of Evolutionary Multitask Multi-Objective Optimization

To facilitate the solving of complex problems, evolutionary multitask optimization is regarded as a promising technique due to its capacity for cross-task knowledge transfer. In contrast to empirical evidences, no mathematical runtime support exists for this technique within the domain of multi-objective optimization. In this work, we aim to fill this important gap. We analyze how the representative multiobjective multifactorial evolutionary algorithm (MO-MFEA) covers the Pareto fronts of two bi-objective problems simultaneously. For the two-task problem of maximizing the classic bi-objective OMM and the multimodal OJZJ benchmarks, the MO-MFEA requires only an expected runtime of $O(n^2 \log n)$---the same as optimizing the simple OMM alone---yet achieves a speedup by a factor of $Θ(n^{k-1}/\log n)$ compared to optimizing OJZJ alone (with gap size $k$). Similarly, for the task pair comprising the classic LOTZ and the multimodal DLTB benchmarks, a speedup of $Θ(n)$ is attained over optimizing DLTB alone, while the runtime matches that of optimizing LOTZ alone.

cs.NE↗
arXiv2026

A restart rule for genetic algorithms based on Schnabel census

A new adaptive restart rule for Genetic Algorithms (GAs) is considered. The rule is based on the Schnabel Census method, originally developed for statistical estimation of a size of animal population. In this paper, the Schnabel Census method is applied as a heuristic to estimate the number of different solutions that may be visited with positive probability, given the current distribution of offspring. The rule consists in restarting a GA as soon as the maximum likelihood estimate reaches the number of different solutions observed at the recent iterations. We demonstrate how the new restart rule can be applied in a GA with steady-state replacement scheme, using three different combinatorial optimization problems as examples. Computational experiments on well-known benchmarks show a statistically significant advantage of the GAs with the new restarting rule over the original versions of GAs. The new restart rule also tends to be superior to the well-known rule, which restarts an algorithm when the current iteration number is twice the number of iterations till the current best incumbent was found.

cs.NE↗
arXiv2026

Design and Analysis of Temporal Representations in Neural Networks

The ability to represent temporal information is often hypothesized to underlie the efficiency and learning capabilities of biological neural networks. This motivates the development of spiking neural networks. However, it remains unclear how temporal information is actually represented in neural networks and how different forms of temporal representation affect performance and computational efficiency. We introduce the notion of the degree of temporal representation of a neural network, defined by whether its constituent functions (activation, learning, encoding, and decoding) explicitly manipulate time as a computational variable. This perspective yields a continuum ranging from non-temporal representations, where time does not explicitly appear as a computational variable in the network functions, to fully temporal representations, where computations explicitly operate on temporal variables. Using a fixed deep architecture, we compare different encoding, activation mechanisms and decoding with increasing temporal expressiveness: non-temporal weighted-sum activations used in usual artificial neural networks, non-temporal binary spiking activations used in spiking neural networks, and spike-time-based activations used in delay neural networks. Experiments on event-based datasets show that increasing the degree of temporal representation improves both performance and computational efficiency, allowing state-of-the-art performances on the Google Speech Commands dataset. We further analyze how temporal representations induce distinct forward and backward computational trajectories, leading to gradients that depend explicitly on the history of event times.

cs.NE↗