arXiv subjects

Davide Cipollini

Publications and source records attributed to Davide Cipollini.

4 recordsLinked to original sources

arXiv2026

Persistent currents in signed directed networks

Network theory can be fruitfully used to describe quantum coherence in physical systems. To that purpose we introduce persistent currents in signed directed networks by interpreting the signed magnetic Laplacian as an effective Hamiltonian and the associated edge phases as a discrete gauge field. In a canonical ensemble, persistent currents arise as thermodynamic responses to variations of gauge-invariant fluxes. We show that these fluxes are naturally defined on the cycle space of the network, and that the resulting currents are constrained to the divergence-free subspace and decompose onto independent cycles. This formulation provides a direct generalization of persistent currents from rings and lattices to arbitrary topologies. Detection of persistent currents provides a signature of the quantum phase coherence supported by the network, and a direct signature of the geometry of its cycle space. Such a mapping, not only allows a practical way to deal with quantum coherence for a variety of situations in the field of quantum technologies, but it also allows a physical interpretation of the importance of the Laplacian operator in graph theory, linking its role to the one of Hamiltonian (i.e. a tight-binding one) in physical systems. To test the power of the method, we construct a signed directed network that reproduces the Hofstadter butterfly spectrum.

cond-mat.dis-nn↗

arXiv2025

Tree networks of real-world data: analysis of efficiency and spatiotemporal scales

Hierarchical tree structures are common in many real-world systems, from tree roots and branches to neuronal dendrites and biologically inspired artificial neural networks, as well as in technological networks for organizing and searching complex datasets of high-dimensional patterns. Within the class of hierarchical self-organized systems, we investigate the interplay of structure and function, associated with the emergence of complex tree structures in disordered environments. Using an algorithm that creates and searches trees of real-world patterns, our work stands at the intersection of statistical physics, machine learning, and network theory. We resolve the network properties over multiple phase transitions and across a continuity of scales, using the von Neumann entropy, its generalized susceptibility, and the recent definition of thermodynamic-like quantities, such as work, heat, and efficiency. We show that scale-invariance, i.e. power-law Laplacian spectral density, is a key feature to construct trees capable of combining fast information flow and sufficiently rich internal representation of information, enabling the system to achieve its functional task efficiently. Moreover, the complexity of the environmental conditions the system has adapted to is encoded in the value of the exponent of the power-law spectral density, inherently related to the network spectral dimension, and directly influencing the traits of those functionally efficient networks. Thereby, we provide a novel metric to estimate the complexity of high-dimensional datasets.

cond-mat.stat-mech↗

arXiv2024

Fused-MemBrain: a spiking processor combining CMOS and self-assembled memristive networks

In an era characterized by the rapid growth of data processing, developing new and efficient data processing technologies has become a priority. We address this by proposing a novel type of neuromorphic technology we call Fused-MemBrain. Our proposal is inspired by Golgi's theory modeling the brain as a syncytial continuum, in contrast to Cajal's theory of neurons and synapses being discrete elements. While Cajal's theory has long been the dominant and experimentally validated view of the nervous system, recent discoveries showed that a species of marine invertebrate (ctenophore Mnemiopsis leidyi) may be better described by Golgi's theory. The core idea is to develop hardware that functions analogously to a syncytial network, exploiting self-assembled memristive systems and combining them with CMOS technologies, interfacing with the silicon back-end-of-line. In this way, a memristive self-assembled material can cheaply and efficiently replace the synaptic connections between CMOS neuron implementations in neuromorphic hardware, enhancing the capability of massively parallel computation. The fusion of CMOS circuits with a memristive ``plexus'' allows information transfer without requiring engineered synapses, which typically consume significant area. As the first step toward this ambitious goal, we present a simulation of a memristive network interfaced with spiking neural networks. Additionally, we describe the potential benefits of such a system, along with key technical aspects it should incorporate.

cs.ET↗

arXiv2022

Ferroelastic domain walls in BiFeO$_3$ as memristive networks

Electronic conduction along individual domain walls (DWs) has been reported in BiFeO$_3$ (BFO) and other nominally insulating ferroelectrics. DWs in these materials separate regions of differently oriented electrical polarization (domains) and are just a few atoms wide, providing self-assembled nanometric conduction paths. In this work, it is shown that electronic transport is possible also from wall to wall through the dense network of as-grown DWs in BFO thin films. Electric field cycling at different points of the network, performed locally by conducting atomic force microscope (cAFM), induces resistive switching selectively at the DWs, both for vertical (single wall) and lateral (wall-to-wall) conduction. These findings are the first step towards investigating DWs as memristive networks for information processing and in-materio computing.

cond-mat.mtrl-sci↗