arXiv subjects

David Saad

Publications and source records attributed to David Saad.

At least 19 recordsLinked to original sources

arXiv2026

Cellular Adaptation to Signal Fluctuations as Learning

Cells represent one of the most fundamental units of life. Underlying their robust performance against environmental variability, such as temporal fluctuations of chemical signals, between different cell types, is a dynamical interrelation between the two components of an intracellular pathway: a gene-regulatory network and its upstream signal transducers. To understand how a single cell utilizes this feedback to self-regulate its gene-expressions, we develop a multiscale model of the pathway's components, in which the adaptive variables responsible for signal interpretation follow a feedback-induced learning process. We then derive a macroscopic theory capturing the covariations between these components - so-called collective modes. Our theory shows how cells can achieve robust output against signal fluctuations via self-regulation rather than simple noise suppression. Such robustness corresponds to a transition from random- to structured collective modes beyond a critical adaptation rate.

q-bio.MN↗

arXiv2026

On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification

Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor-based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time-series prediction and image recognition. Although RC has been demonstrated with several memristive devices, a comprehensive evaluation of device-level requirements remains limited. In this paper, we analyze and explain the operation of a parallel delayed feedback network (PDFN) RC architecture with volatile memristors, focusing on how device characteristics -- such as decay rate, quantization, and variability -- affect reservoir performance. We further discuss strategies to improve data representation in the reservoir using preprocessing methods and suggest potential improvements. The proposed approach achieves 95.89% classification accuracy on MNIST, comparable with the best reported memristor-based RC implementations. Furthermore, the method maintains high robustness under 20% device variability, achieving an accuracy of up to 94.2%. These results demonstrate that volatile memristors can support reliable spatio-temporal information processing and reinforce their potential as key building blocks for compact, high-speed, and energy-efficient neuromorphic computing systems.

cs.NE↗

arXiv2025

Preprocessing Methods for Memristive Reservoir Computing for Image Recognition

Reservoir computing (RC) has attracted attention as an efficient recurrent neural network architecture due to its simplified training, requiring only its last perceptron readout layer to be trained. When implemented with memristors, RC systems benefit from their dynamic properties, which make them ideal for reservoir construction. However, achieving high performance in memristor-based RC remains challenging, as it critically depends on the input preprocessing method and reservoir size. Despite growing interest, a comprehensive evaluation that quantifies the impact of these factors is still lacking. This paper systematically compares various preprocessing methods for memristive RC systems, assessing their effects on accuracy and energy consumption. We also propose a parity-based preprocessing method that improves accuracy by 2-6% while requiring only a modest increase in device count compared to other methods. Our findings highlight the importance of informed preprocessing strategies to improve the efficiency and scalability of memristive RC systems.

cs.NE↗

arXiv2025

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↗

arXiv2025

Optical neuromorphic computing via temporal up-sampling and trainable encoding on a telecom device platform

Mapping input signals to a high-dimensional space is a critical concept in various neuromorphic computing paradigms, including models such as Reservoir Computing (RC) and Extreme Learning Machines (ELM). We propose using commercially available telecom devices and technologies developed for high-speed optical data transmission to implement these models through nonlinear mapping of optical signals into a high-dimensional space where linear processing can be applied. We manipulate the output feature dimension by applying temporal up-sampling (at the speed of commercially available telecom devices) of input signals and a well-established wave-division-multiplexing (WDM). Our up-sampling approach utilizes a trainable encoding mask, where each input symbol is replaced with a structured sequence of masked symbols, effectively increasing the representational capacity of the feature space. This gives remarkable flexibility in the dynamical phase masking of the input signal. We demonstrate this approach in the context of RC and ELM, employing readily available photonic devices, including a semiconductor optical amplifier and nonlinear Mach-Zender interferometer (MZI). We investigate how nonlinear mapping provided by these devices can be characterized in terms of the increased controlled separability and predictability of the output state.

physics.optics↗

arXiv2024

Viral Load Inference in Non-Adaptive Pooled Testing

Medical diagnostic testing can be made significantly more efficient using pooled testing protocols. These typically require a sparse infection signal and use either binary or real-valued entries of O(1). However, existing methods do not allow for inferring viral loads which span many orders of magnitude. We develop a message passing algorithm coupled with a PCR (Polymerase Chain Reaction) specific noise function to allow accurate inference of realistic viral load signals. This work is in the non-adaptive setting and could open the possibility of efficient screening where viral load determination is clinically important.

cond-mat.stat-mech↗

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↗

arXiv2023

Infection-induced Cascading Failures -- Impact and Mitigation

In the context of epidemic spreading, many intricate dynamical patterns can emerge due to the cooperation of different types of pathogens or the interaction between the disease spread and other failure propagation mechanism. To unravel such patterns, simulation frameworks are usually adopted, but they are computationally demanding on big networks and subject to large statistical uncertainty. Here, we study the two-layer spreading processes on unidirectionally dependent networks, where the spreading infection of diseases or malware in one layer can trigger cascading failures in another layer and lead to secondary disasters, e.g., disrupting public services, supply chains, or power distribution. We utilize a dynamic message-passing method to devise efficient algorithms for inferring the system states, which allows one to investigate systematically the nature of complex intertwined spreading processes and evaluate their impact. Based on such dynamic message-passing framework and optimal control, we further develop an effective optimization algorithm for mitigating network failures.

physics.soc-ph↗

arXiv2023

The futility of being selfish in vaccine distribution

We study vaccine budget-sharing strategies in the SIR (Susceptible-Infected-Recovered) model given a structured community network to investigate the benefit of sharing vaccine across communities. The network studied comprises two communities, one of which controls vaccine budget and may share it with the other. Different scenarios are considered regarding the connectivity between communities, infection rates and the unvaccinated fraction of the population. Properties of the SIR model facilitates the use of Dynamic Message Passing (DMP) and optimal control methods to investigate preventive and reactive budget-sharing scenarios. Our results show a large set of budget-sharing strategies in which the sharing community benefits from the reduced global infection rates with no detrimental impact on its local infection rate.

physics.soc-ph↗

arXiv2021

Bilevel optimization in flow networks: A message-passing approach

Optimizing embedded systems, where the optimization of one depends on the state of another, is a formidable computational and algorithmic challenge, that is ubiquitous in real world systems. We study flow networks, where bilevel optimization is relevant to traffic planning, network control and design, and where flows are governed by an optimization requirement subject to the network parameters. We employ message-passing algorithms in flow networks with sparsely coupled structures to adapt network parameters that govern the network flows, in order to optimize a global objective. We demonstrate the effectiveness and efficiency of the approach on randomly generated graphs.

math.OC↗

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, Erd\H{o}s R\'{e}nyi 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

Impact of presymptomatic transmission on epidemic spreading in contact networks: A dynamic message-passing analysis

Infectious diseases that incorporate pre-symptomatic transmission are challenging to monitor, model, predict and contain. We address this scenario by studying a variant of a stochastic susceptible-exposed-infected-recovered model on arbitrary network instances using an analytical framework based on the method of dynamic message-passing. This framework provides a good estimate of the probabilistic evolution of the spread on both static and contact networks, offering a significantly improved accuracy with respect to individual-based mean-field approaches while requiring a much lower computational cost compared to numerical simulations. It facilitates the derivation of epidemic thresholds, which are phase boundaries separating parameter regimes where infections can be effectively contained from those where they cannot. These have clear implications on different containment strategies through topological (reducing contacts) and infection parameter changes (e.g., social distancing and wearing face masks), with relevance to the recent COVID-19 pandemic.

physics.soc-ph↗

arXiv2020

Space of Functions Computed by Deep-Layered Machines

We study the space of functions computed by random-layered machines, including deep neural networks and Boolean circuits. Investigating the distribution of Boolean functions computed on the recurrent and layer-dependent architectures, we find that it is the same in both models. Depending on the initial conditions and computing elements used, we characterize the space of functions computed at the large depth limit and show that the macroscopic entropy of Boolean functions is either monotonically increasing or decreasing with the growing depth.

cs.LG↗

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↗

arXiv2020

Reducing urban traffic congestion due to localized routing decisions

Balancing traffic flow by influencing drivers' route choices to alleviate congestion is becoming increasingly more appealing in urban traffic planning. Here, we introduce a discrete dynamical model comprising users who make their own routing choices on the basis of local information and those who consider routing advice based on localized inducement. We identify the formation of traffic patterns, develop a scalable optimization method for identifying control values used for user guidance, and test the effectiveness of these measures on synthetic and real-world road networks.

physics.soc-ph↗

arXiv2019

Scalable Influence Estimation Without Sampling

In a diffusion process on a network, how many nodes are expected to be influenced by a set of initial spreaders? This natural problem, often referred to as influence estimation, boils down to computing the marginal probability that a given node is active at a given time when the process starts from specified initial condition. Among many other applications, this task is crucial for a well-studied problem of influence maximization: finding optimal spreaders in a social network that maximize the influence spread by a certain time horizon. Indeed, influence estimation needs to be called multiple times for comparing candidate seed sets. Unfortunately, in many models of interest an exact computation of marginals is #P-hard. In practice, influence is often estimated using Monte-Carlo sampling methods that require a large number of runs for obtaining a high-fidelity prediction, especially at large times. It is thus desirable to develop analytic techniques as an alternative to sampling methods. Here, we suggest an algorithm for estimating the influence function in popular independent cascade model based on a scalable dynamic message-passing approach. This method has a computational complexity of a single Monte-Carlo simulation and provides an upper bound on the expected spread on a general graph, yielding exact answer for treelike networks. We also provide dynamic message-passing equations for a stochastic version of the linear threshold model. The resulting saving of a potentially large sampling factor in the running time compared to simulation-based techniques hence makes it possible to address large-scale problem instances.

cs.SI↗

arXiv2019

Large Deviation Analysis of Function Sensitivity in Random Deep Neural Networks

Mean field theory has been successfully used to analyze deep neural networks (DNN) in the infinite size limit. Given the finite size of realistic DNN, we utilize the large deviation theory and path integral analysis to study the deviation of functions represented by DNN from their typical mean field solutions. The parameter perturbations investigated include weight sparsification (dilution) and binarization, which are commonly used in model simplification, for both ReLU and sign activation functions. We find that random networks with ReLU activation are more robust to parameter perturbations with respect to their counterparts with sign activation, which arguably is reflected in the simplicity of the functions they generate.

cond-mat.dis-nn↗

arXiv2019

Competition, Collaboration, and Optimization in Multiple Interacting Spreading Processes

Competition and collaboration are at the heart of multi-agent probabilistic spreading processes. The battle on public opinion and competitive marketing campaigns are typical examples of the former, while the joint spread of multiple diseases such as HIV and tuberculosis demonstrates the latter. These spreads are influenced by the underlying network topology, the infection rates between network constituents, recovery rates and, equally importantly, the interactions between the spreading processes themselves. Here, for the first time we derive dynamic message-passing equations that provide an exact description of the dynamics of two interacting spreading processes on tree graphs, and develop systematic low-complexity models that predict the spread on general graphs. We also develop a theoretical framework for an optimal control of interacting spreading processes through an optimized resource allocation under budget constraints and within a finite time window. Derived algorithms can be used to maximize the desired spread in the presence of a rival competitive process, and to limit the spread through vaccination in the case of coupled infectious diseases. We demonstrate the efficacy of the framework and optimization method on both synthetic and real-world networks.

physics.soc-ph↗