# Feasibility of a Personal Neuromorphic Emulation

## TL;DR

The paper explores whether a person's mind can be emulated using neuromorphic computing by modeling brain connectivity and self-organizing information.

## Contribution

It proposes that computational emulation can reconstruct individual brain/mind dynamics based on active inference and self-evidencing principles.

## Key findings

- Brain connectivity patterns and self-organizing information systems may be emulated computationally.
- Consciousness could emerge from hierarchical, reentrant self-evidencing in complex systems.
- Neuromorphic emulation may capture individual human brain dynamics at sufficient complexity.

## Abstract

The representation of intelligence is achieved by patterns of connections among neurons in brains and machines. Brains grow continuously, such that their patterns of connections develop through activity-dependent specification, with the continuing ontogenesis of individual experience. The theory of active inference proposes that the developmental organization of sentient systems reflects general processes of informatic self-evidencing, through the minimization of free energy. We interpret this theory to imply that the mind may be described in information terms that are not dependent on a specific physical substrate. At a certain level of complexity, self-evidencing of living (self-organizing) information systems becomes hierarchical and reentrant, such that effective consciousness emerges as the consequence of a good regulator. We propose that these principles imply that an adequate reconstruction of the computational dynamics of an individual human brain/mind is possible with sufficient neuromorphic computational emulation.