Information dynamics of our brains in dynamically driven disordered superconducting loop networks

TL;DR

This paper models brain-like information dynamics using disordered superconducting loop networks with Josephson junctions, demonstrating stable flux-based memory states and complex information flow patterns through simulations and experiments.

Contribution

It introduces a generic network model derived from superconducting loops to emulate brain-like information processing and memory, bridging physics and complex system behavior.

Findings

Stable flux states encode information in the network.

Experimental flux flow patterns demonstrate dynamic stability.

The model captures universal principles of information dynamics.

Abstract

Complex systems of many interacting components exhibit patterns of recurrence and emergent behaviors in their time evolution that can be understood from a new perspective of physics of information dynamics, modeled after one such system, our brains. A generic brain-like network model is derived from a system of disordered superconducting loops with Josephson junction oscillators to demonstrate these behaviors. The loops can trap multiples of fluxons that represent quantized information units in many distinct memory configurations populating a state space. The state can be updated by exciting the junctions to allow the movement of fluxons through the network as the current through them surpasses their thresholds. Numerical simulations performed with a lumped circuit model of a 4-loop network show that information written through excitations is translated into stable states of trapped flux and their time evolution. Experimental implementation on the 4-loop network shows dynamically stable flux flow in each pathway characterized by the junction firing statistics. The network separates information from multiple excitations into state categories with large energy barriers observed in simulations that correspond to different flux (information) flow patterns observed across junctions in experiments. Strong evidence for associative and time-dependent (short-to-long-term) memories distributed across the network is observed, dependent on its intrinsic and geometrical properties as described by the model. Loop network topology abstraction using the model separates the flowing patterns of information from its physical constraints and describes systems of any scale and complexity. The accuracy of flow statistics are limited by the resolution of local external measuring clock(s) revealing the universal nature of information dynamics through the stated two principles.