Emergent neural network behavior in dynamically driven superconducting loop disordered systems

Disordered systems with an irregular energy landscape comprising finitely many local minima are ideal to model computational properties observed in the biological brain, consistent with the neural network model developed by Hopfield [1]. Computational properties, such as categorization, associative memory, time-sequence retention, etc., can be understood as the phase-space flow of the state of the system in response to external excitations. We present a system of superconducting loops coupled through Josephson junctions with disorder introduced into the geometry of the loops and junctions. The loops trap multiples of quantized magnetic flux allowing a multi-dimensional state-space of flux configurations, while the Josephson junctions allow traversal of flux between the loops, as a mechanism to update the state of the system. External excitations drive the system into different trapped flux states, observed as circulating supercurrents around the loop. A 3-loop disordered network is shown in simulations and experiments to exhibit emergent behavior such as categorization and associative memory. Additionally, the dynamics of the internal state of the system is statistically correlated with outgoing flux from the network, that can be observed as a spiking voltage, with each spike corresponding to a magnetic flux quantum Φ₀ = 2.067 x 10¹⁵ T·m².



[1] Hopfield, John J. "Neural networks and physical systems with emergent collective computational abilities." Proceedings of the national academy of sciences 79.8 (1982): 2554-2558.