What does this research mean for the field?

A new framework based on the Conscious Fractal Processor (CFP) architecture enables artificial resilience and scale-invariant recovery dynamics in Sparse Recurrent Neural Networks (SR-RNNs) following structural shocks or sensory deprivation. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to develop a resilient artificial neural network framework that mimics biological adaptability.

May 16, 2026Open Access

Universal time constants in allostatic SR-RNN : Scale invariance and AUNI and SCO dual kinetic laws

Key Points

This research aims to develop a resilient artificial neural network framework that mimics biological adaptability.
Conducted large-scale experiments with Sparse Recurrent Neural Networks (N=32 to 512 neurons)
Employed allostatic plasticity for recovery dynamics testing
Analyzed system responses after synaptic resets and sensory deprivation.
Demonstrated effective recovery dynamics after total synaptic resets.
Highlighted resilience across varying network sizes during sensory deprivation.
Found evidence for scale-invariance in recovery processes.

Abstract

While modern Recurrent Neural Networks (RNNs) excel at pattern recognition, they often lack the biological-like resilience required to maintain internal coherence under extreme sensory deprivation or structural shocks. This paper introduces a new framework for artificial resilience based on the Conscious Fractal Processor (CFP) architectureWe conducted a series of large-scale experiments using Sparse Recurrent Neural Networks (SR-RNN) equipped with allostatic plasticity. We tested the system’s recovery dynamics after total synaptic resets and prolonged sensory deprivation across various network sizes (N=32 to 512 neurons). 10. 5281/zenodo. 20129339

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