Quantum spin models for numerosity perception
Jorge Yago Malo, Guido Marco Cicchini, Maria Concetta Morrone, Maria, Luisa Chiofalo

TL;DR
This paper introduces a simple quantum spin model that encodes numerosity through spectral analysis, successfully reproducing key perceptual features like Weber's law, offering a novel approach to understanding number sense in neural systems.
Contribution
The authors propose a minimal quantum spin model that captures numerosity perception and reproduces Weber's law, contrasting with complex neural network models.
Findings
Spectral components increase with number of stimuli
Model follows Weber's law in numerosity perception
Contrasts with linear system or accumulator models
Abstract
Humans share with animals, both vertebrates and invertebrates, the capacity to sense the number of items in their environment already at birth. The pervasiveness of this skill across the animal kingdom suggests that it should emerge in very simple populations of neurons. Current modelling literature, however, has struggled to suggest a simple architecture carrying out this task, with most proposals suggesting the emergence of number sense in multi-layered complex neural networks, and typically requiring supervised learning. We present a simple quantum spin model with all-to-all connectivity, where numerosity is encoded in the spectrum after stimulation with a number of transient signals occurring in a random or orderly temporal sequence. We use a paradigmatic simulational approach borrowed from the theory and methods of open quantum systems out of equilibrium, as a possible way to…
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Taxonomy
TopicsNeural Networks and Applications · Cognitive and developmental aspects of mathematical skills
