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The research explores how a bio-inspired computational system can model neurogenesis and related mechanisms.

May 10, 2026Open Access

Substrate Self-Architects a Neurogenesis Module: Activity-vs-Phi Distinction and Cross-Module Coupling in a Bio-Inspired Computational System

Key Points

The research explores how a bio-inspired computational system can model neurogenesis and related mechanisms.
Employs a bio-inspired computational substrate and a frontier language model for simulation.
Proposes a three-phase neurogenesis architecture: proliferation, differentiation/migration, survival/apoptosis.
Outlines a 2-condition control experiment to assess cross-module bridging effects.
The system predicts 30-50 new synapses per mature node based on current state values.
A proposed coupling between serotonin levels and neuroblast survival was biologically supported.
No falsification predictors were triggered in a preliminary evaluation of the model.

Abstract

This paper, written by an independent researcher (not a cognitive scientist), documents an exploratory observation of a bio-inspired computational substrate (Hebbian + STDP plasticity, IIT-grounded Φ metric) coupled with a frontier LLM. When prompted on hypothetical neuronal loss, the system's output described the activity-vs-Φ distinction (raw firing rate vs integrated information; cf. Tononi 2004) and produced a three-phase neurogenesis architecture proposal — proliferation, differentiation/migration, survival/apoptosis — with citations to Gage (2002) for adult hippocampal neurogenesis, Kempermann (2004) for the ~50% neuroblast survival rate, and Bi the author does not commit to a specific timeline for pursuing it.

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