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April 1, 20260 citationsOpen Access

PHILIA-SAN: A Toy Model of a Single Artificial Neuron with Recursive Feedback Loop

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두신두섭 신

Key Points

  • The aim is to describe the PHILIA-SAN model and analyze its stability under disturbance ratios.
  • Constructed a model with linear normalization and a recursive neuron.
  • Utilized real physical data from CERN and ATLAS datasets.
  • Measured passive stability under various disturbance ratios from 10% to 50%.
  • The model displayed stability despite disturbances.
  • Stability measurements were based on extensive event datasets from CERN and ATLAS.

Abstract

This paper presents PHILIA-SAN (Single Artificial Neuron), a minimal toy model built on the PHILIA v9 engine. The model consists of a receptor layer (linear normalization) and a single recursive neuron with a closed SR->Phi feedback loop. It is not a biological model and does not claim homeostasis. Passive stability of the neuron was measured under disturbance ratios of 10% to 50% using real physical data from CERN dielectron (99,915 events) and ATLAS Higgs (693,636 events) datasets. All results are from local hardware execution (Intel N100). This is a description, not a proof.

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Cite This Study

두섭 신 (2026) studied this question.

synapsesocial.com/papers/69ccb72e16edfba7beb8902ehttps://doi.org/10.5281/zenodo.19335128
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