24 papers · ranked by Valyu relevance
Milan Korda
This work makes several contributions on stability and performance verification of nonlinear dynamical systems controlled by neural networks. First, we show that the stability and performance of a polynomial dynamical system controlled by a neural network with semialgebraically representable activation functions (e.g.…
Julián Barreiro-Gomez, Salah Eddine Choutri, Boualem Djehiche
— In this paper, we present an approach to neural network mean-field-type control and its stochastic stability analysis by means of adversarial inputs (aka adversarial attacks). This is a class of data-driven mean-field-type control where the distribution of the variables such as the system states and control inputs…
Axel Hutt, Scott Rich, Taufik A Valiante, Jérémie Lefebvre
Heterogeneity is the norm in biology. The brain is no different: neuronal cell-types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs and ion channel distributions. While this biophysical diversity enriches neural systems’ dynamical repertoire, it remains challenging to…
Cristian Axenie
The stability-robustness-resilience-adaptiveness continuum in neuronal processing follows a hierarchical structure that explains interactions and information processing among the different time scales. Interestingly, using “canonical” neuronal computational circuits, such as Homeostatic Activity Regulation…
Flaviano Morone, Shivang Rawat, David J. Heeger, Stefano Martiniani
Stability is a fundamental requirement for both biological and engineered neural circuits, yet it is surprisingly difficult to guarantee in the presence of recurrent interactions. Standard linear dynamical models of recurrent networks are sensitive to the precise values of the synaptic weights, since stability requires…
Alvaro Detailleur, Guillaume Ducard, Christopher H. Onder
— This work presents several improvements to the closed-loop stability verification framework using semialgebraic sets and convex semidefinite programming to examine neural-network-based control systems regulating nonlinear dynamical systems. First, the utility of the framework is greatly expanded: two semialgebraic…
Osvaldo Matias Velarde, Hernán A. Makse, Lucas C. Parra, Drew Linsley
'Drew Linsley'] In the visual system of primates, image information propagates across successive cortical areas, and there is also local feedback within an area and long-range feedback across areas. Recent findings suggest that the resulting temporal dynamics of neural activity are crucial in several vision tasks. In…
Wayne W.M. Soo, Máté Lengyel
There continues to be a trade-off between the biological realism and performance of neural networks. Contemporary deep learning techniques allow neural networks to be trained to perform challenging computations at (near) human-level, but these networks typically violate key biological constraints. More detailed models…
Hui Chu
As a human brain-like computational model that can reflect the cognitive function of the brain, the problem of dynamic analysis of associative memory neural networks has attracted the attention of scholars. This paper combines associative memory neural networks with enterprise financial management risks, studies the…
Thomas J. Richner, Martynas Dervinis, Brian Nils Lundstrom
The brain is a highly recurrent, nonlinear network hypothesized to remain near the edge of chaos for optimal performance. Excitation and inhibition must be balanced precisely within every neuron to ensure a consistent level of dynamical stability and rich dynamics during transition to chaos. However, analysis of…
Saray Soldado-Magraner, Michael J. Seay, Rodrigo Laje, Dean V. Buonomano
'Dean V. Buonomano'] Title: Significance Cortical networks have the remarkable ability to self-assemble into dynamic regimes in which excitatory positive feedback is balanced by recurrent inhibition. This inhibition-stabilized regime is increasingly viewed as the default dynamic regime of the cortex, but how it emerges…
Gani Stamov, Trayan Stamov, Ivanka Stamova, Cvetelina Spirova + 2 more
'António Lopes' 'José F.F. Mendes'] In this paper, we focus on h-manifolds related to impulsive reaction-diffusion Cohen-Grossberg neural networks with time-varying delays. By constructing a new Lyapunov-type function and a comparison principle, sufficient conditions that guarantee the global practical exponential…
Plamen Dimitrov
This work presents the current collection of mathematical models related to neural networks and proposes a new family of such with extended structure and dynamics in order to attain a selection of cognitive capabilities. It starts by providing a basic background to the morphology and physiology of the biological and…
Maryada, Saray Soldado-Magraner, Martino Sorbaro, Rodrigo Laje + 2 more
Many neural computations emerge from self-sustained patterns of activity in recurrent neural circuits, which rely on balanced excitation and inhibition. Neuromorphic electronic circuits that use the physics of silicon to emulate neuronal dynamics represent a promising approach for implementing the brain’s computational…
Nahal Sharafi, Christoph Martin, Sarah Hallerberg
Neural networks have become a widely adopted tool for tackling a variety of problems in machine learning and artificial intelligence. In this contribution we use the mathematical framework of local stability analysis to gain a deeper understanding of the learning dynamics of feed forward neural networks. Therefore, we…
Marat Akhmet, Madina Tleubergenova, Zakhira Nugayeva, Nikolay Kolev Vitanov + 1 more
'Nikolay Kolev Vitanov' 'Zlatinka I. Dimitrova'] A new model of inertial neural networks with a generalized piecewise constant argument as well as unpredictable inputs is proposed. The model is inspired by unpredictable perturbations, which allow to study the distribution of chaotic signals in neural networks. The…
Marat Akhmeta, Madina Tleubergenovab, Akylbek Zhamanshin
In this paper, Cohen-Grossberg neural networks with unpredictable and compartmental periodic unpredictable strengths of connectivity between cells and inputs are investigated. To approve Poisson stability and unpredictability in neural networks, the method of included intervals and contraction mapping principle are…
Yue Kris Wu, Julijana Gjorgjieva
Inhibition stabilization is considered a ubiquitous property of cortical networks, whereby inhibition controls network activity in the presence of strong recurrent excitation. In networks with fixed connectivity, an identifying characteristic of inhibition stabilization is that increasing (decreasing) excitatory input…
Veerupaksh Singla, Qiyuan Zhao, Brett Savoie
The absence of computational methods to predict stressor-specific degradation susceptibilities represents a significant and costly challenge to the introduction of new materials into applications. Here, a machine-learning framework is developed that predicts stressor-specific stability scores from computationally…
Sara Tkaczyk, Johannes Karwounopoulos, Andreas Schöller, H. Lee Woodcock + 3 more
- 1. Faculty of Chemistry, Institute of Computational Biological Chemistry, Währingerstrasse 17, University of Vienna, Vienna, Austria - 2. Department of Pharmaceutical Sciences, Pharmaceutical Chemistry Division, Josef-Holaubek-Platz 2, University of Vienna, 1090 Vienna, Austria - 3. Doctoral School of Pharmaceutical…
James Swift, Matthew Arran Turner, James Christopher Reynolds
A rapid headspace analysis method for the authenticity testing of whiskies of different brands and years was developed for a low cost, deployable atmospheric pressure ionisation mass spectrometer, which required minimal sample preparation. Principal component analysis was applied to the time-averaged mass spectra, the…
Hannes Stagge, Theresa Kunz, Sina Ramsayer, Robert Güttel
The calculation of chemical equilibria in detailed reactor simulations frequently requires elaborate numerical solution of the governing equations in an iterative way, which is often computationally expensive and can significantly increase the overall computation time. In order to reduce these computational costs, we…
Authors not listed
Electrospray ionization (ESI) mass spectrometry is an essential technique for chemical analysis in a range of fields. In ESI, analytes can produce multiple charge states, which must be correctly assigned for identification. Existing approaches to charge state assignment can suffer from limited accuracy and/or poor…
Authors not listed
Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…