arXiv subjects

Ho Fai Po

Publications and source records attributed to Ho Fai Po.

7 recordsLinked to original sources

arXiv2026

Analysis of Evolving Cortical Neuronal Networks Using Visual Informatics

Understanding how neuronal population activity changes during development and after stimulation is essential for studying neuronal network dynamics. This work examines how visual informatics can summarize high-dimensional spiking activity while retaining information that is biologically interpretable. We develop a framework based on Minimum-Distortion Embedding (MDE), and compare it with Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). In addition to evaluating the embeddings by visual separation, we quantify whether they preserve the cosine-shape radius within each condition and the pairwise distances between condition centroids. Our \emph{in silico} experiments show that MDE with a cosine metric captures the trajectory of simulated network maturation and preserves the contraction of the activity cloud as connectivity increases. Complementary \emph{in vitro} experiments on human cortical cultures show a coherent developmental trajectory from Day In VITRO 23 (DIV23) to DIV64. We also study weak and strong stimulation in simulation, and long-term potentiation stimulation in primary cortical cultures. In the stimulation experiments, MDE separates activity phases more clearly than PCA and preserves transient changes in within-phase variability that are missed by PCA. These results show that metric selection is central to dimensionality reduction of neuronal data. In particular, cosine distance between population activity vectors provides embeddings that better reflect changes in population activity patterns than Euclidean distance. The proposed framework provides a quantitative way to visualize network development and stimulation-induced changes in neuronal activity.

q-bio.NC↗

arXiv2024

Re-routing game: The inadequacy of mean-field approach in modeling the herd behavior in path switching

Coordination of vehicle routes is a feasible way to ease traffic congestions amid a fixed road infrastructure. Nevertheless, even the optimal route configurations are provided to individual drivers, it is hard to achieve as greedy drivers may switch to other routes for a lower individual cost. Recent research uses mean-field cavity approach from the studies of spin glasses to analyze the impact of path switching in optimized transportation networks. However, this method only provides a mean-field approximation, which does not take into account the collective herd behavior in path switching due to un-coordinated individual decisions. In this study, we propose an exhaustive cavity approach to investigate the impact of un-coordinated path switching in a re-routing game and reveal that greedy drivers' decision can be highly correlated which leads to the failure of mean-fielded approaches. Our theoretical results fits well with simulations, and our developed framework can be generalized to analyze other games with multiple players and rounds. Our results shed light on the impact of herd behavior of un-coordinated human drivers in suppressing congestions through path coordination.

physics.soc-ph↗

arXiv2024

Complete Realization of Energy Landscape and Non-equilibrium Trapping Dynamics in Spin Glass and Optimization Problem

Energy landscapes are high-dimensional surfaces representing the dependence of system energy on variable configurations, which determine crucially the system's emergent behavior but are difficult to be analyzed due to their high-dimensional nature. In this article, we introduce an approach to reveal the complete energy landscapes of small spin glasses and Boolean satisfiability problems, which also unravels their non-equilibrium dynamics at an arbitrary temperature for an arbitrarily long time. In contrary to our common belief, our results show that it can be less likely to identify the ground states when temperature decreases, due to trapping in individual local minima, which ceases at different time, leading to multiple abrupt jumps with time in the ground-state probability. Simulations agree well with theoretical predictions on these remarkable phenomena. Finally, for large systems, we introduce a variant approach to extract partially the energy landscapes and observe both analytically and in simulations similar phenomena. This work introduces new methodology to unravel the non-equilibrium dynamics of glassy systems, and provides us with a clear, complete and new physical picture on their long-time behaviors inaccessible by modern numerics.

cond-mat.dis-nn↗

arXiv2024

Inferring Structure of Cortical Neuronal Networks from Firing Data: A Statistical Physics Approach

Understanding the relation between cortical neuronal network structure and neuronal activity is a fundamental unresolved question in neuroscience, with implications to our understanding of the mechanism by which neuronal networks evolve over time, spontaneously or under stimulation. It requires a method for inferring the structure and composition of a network from neuronal activities. Tracking the evolution of networks and their changing functionality will provide invaluable insight into the occurrence of plasticity and the underlying learning process. We devise a probabilistic method for inferring the effective network structure by integrating techniques from Bayesian statistics, statistical physics and principled machine learning. The method and resulting algorithm allow one to infer the effective network structure, identify the excitatory and inhibitory nature of its constituents, and predict neuronal spiking activities by employing the inferred structure. We validate the method and algorithm's performance using synthetic data, spontaneous activity of an in silico emulator and realistic in vitro neuronal networks of modular and homogeneous connectivity, demonstrating excellent structure inference and activity prediction. We also show that our method outperforms commonly used existing methods for inferring neuronal network structure. Inferring the evolving effective structure of neuronal networks will provide new insight into the learning process due to stimulation in general and will facilitate the development of neuron-based circuits with computing capabilities.

physics.soc-ph↗

arXiv2021

Scalable Node-Disjoint and Edge-Disjoint Multi-wavelength Routing

Probabilistic message-passing algorithms are developed for routing transmissions in multi-wavelength optical communication networks, under node and edge-disjoint routing constraints and for various objective functions. Global routing optimization is a hard computational task on its own but is made much more difficult under the node/edge-disjoint constraints and in the presence of multiple wavelengths, a problem which dominates routing efficiency in real optical communication networks that carry most of the world's Internet traffic. The scalable principled method we have developed is exact on trees but provides good approximate solutions on locally tree-like graphs. It accommodates a variety of objective functions that correspond to low latency, load balancing and consolidation of routes, and can be easily extended to include heterogeneous signal-to-noise values on edges and a restriction on the available wavelengths per edge. It can be used for routing and managing transmissions on existing topologies as well as for designing and modifying optical communication networks. Additionally, it provides the tool for settling an open and much debated question on the merit of wavelength-switching nodes and the added capabilities they provide. The methods have been tested on generated networks such as random-regular, Erdős Rényi and power-law graphs, as well as on the UK and US optical communication networks. They show excellent performance with respect to existing methodology on small networks and have been scaled up to network sizes that are beyond the reach of most existing algorithms.

physics.soc-ph↗

arXiv2020

The Futility of Being Selfish -- The Impact of Selfish Routing on Uncoordinated and Optimized Transportation Networks

Optimizing traffic flow is essential for easing congestion. However, even when globally-optimal, coordinated and individualized routes are provided, users may choose alternative routes which offer lower individual costs. By analyzing the impact of selfish route-choices on performance using the cavity method, we find that a small ratio of selfish route-choices improves the global performance of uncoordinated transportation networks, but degrades the efficiency of optimized systems. Remarkably, compliant users always gain in the former and selfish users may gain in the latter, under some parameter conditions. The theoretical results are in good agreement with large-scale simulations. Iterative route-switching by a small fraction of selfish users leads to Nash equilibria close to the globally optimal routing solution. Our theoretical framework also generalizes the use of the cavity method, originally developed for the study of equilibrium states, to analyze iterative game-theoretical problems. These results shed light on the feasibility of easing congestion by route coordination when not all vehicles follow the coordinated routes.

physics.soc-ph↗

arXiv2016

Evolving Powergrids in Self-Organized Criticality: An analogy with Sandpile and Earthquakes

The stability of powergrid is crucial since its disruption affects systems ranging from street lightings to hospital life-support systems. Nevertheless, large blackouts are inevitable if powergrids are in the state of self-organized criticality (SOC). In this paper, we introduce a simple model of evolving powergrid and establish its connection with the sandpile model, i.e. a prototype of SOC, and earthquakes, i.e. a system considered to be in SOC. Various aspects are examined, including the power-law distribution of blackout magnitudes, their inter-event waiting time, the predictability of large blackouts, as well as the spatial-temporal rescaling of blackout data. We verified our observations on simulated networks as well as the IEEE 118-bus system, and show that both simulated and empirical blackout waiting times can be rescaled in space and time similarly to those observed between earthquakes. Finally, we suggested proactive maintenance strategies to drive the powergrids away from SOC to suppress large blackouts.

physics.soc-ph↗