22 papers · ranked by Valyu relevance
Mohsen Sadeghi, James N. Ingram, Daniel M. Wolpert, Ramesh Balasubramaniam
'Ramesh Balasubramaniam'] Sensorimotor learning typically shows generalization from one context to another. Models of sensorimotor learning characterize this with a fixed generalization function that couples learning between contexts. Here we examine whether such coupling is indeed fixed or changes with experience. We…
V. K. Chandrasekar, Jane H. Sheeba, B. Subash, M. Lakshmanan + 1 more
'Jürgen Kurths'] Adaptive coupling, where the coupling is dynamical and depends on the behaviour of the oscillators in a complex system, is one of the most crucial factors to control the dynamics and streamline various processes in complex networks. In this paper, we have demonstrated the occurrence of multi-stable…
A. Provata, Georgios C. Boulougouris, Johanne Hizanidis
Adaptive link sizes is a major breakthrough step in evolving networks and is now considered as an essential process both in biological and artificial neural networks. In adaptive networks the link weights change in time and, in brain dynamics, these changes are controlled by the potential variations of the pre- and…
Timo Nachstedt, Christian Tetzlaff, Poramate Manoonpong
Rhythmic neural signals serve as basis of many brain processes, in particular of locomotion control and generation of rhythmic movements. It has been found that specific neural circuits, named central pattern generators (CPGs), are able to autonomously produce such rhythmic activities. In order to tune, shape and…
Nilanjan Panda
Adaptive control of synchrony in neuronal networks is central to understanding both normal brain function and pathological states such as epilepsy and tremor. We study a modified FitzHugh–Nagumo (FHN) network in which the local excitability is extended by a fifth–order nonlinearity and the global coupling strength…
Benoit Duchet, Christian Bick, Áine Byrne
Understanding the effect of spike-timing-dependent plasticity (STDP) is key to elucidate how neural networks change over long timescales and to design interventions aimed at modulating such networks in neurological disorders. However, progress is restricted by the significant computational cost associated with…
Dmitry V. Kasatkin, Vladimir I. Nekorkin, José F. F. Mendes
Adaptive interactions are an important property of many real-word network systems. A feature of such networks is the change in their connectivity depending on the current states of the interacting elements. In this work, we study the question of how the heterogeneous character of adaptive couplings influences the…
Benjamin Jüttner, Erik A. Martens
Networks of coupled dynamical units give rise to collective dynamics such as the synchronization of oscillators or neurons in the brain. The ability of the network to adapt coupling strengths between units in accordance with their activity arises naturally in a variety of contexts, including neural plasticity in the…
Eckehard Schöll, Jakub Sawicki, Rico Berner, Plamen Ch. Ivanov
Adaptive networks provide a paradigm to investigate natural phenomena in fields ranging from physics, chemistry, biology and neuroscience to physiology, medicine and socio-economic systems ([9]). A fundamental problem is to understand how global behaviors emerge out of interactions among dynamically changing entities…
Jie Zhou, Yong Zou, Shuguang Guan, Zonghua Liu + 2 more
'S. Boccaletti'] In this paper, we propose a strategy for the control of mobile chaotic oscillators by adaptively rewiring connections between nearby agents with local information. In contrast to the dominant adaptive control schemes where coupling strength is adjusted continuously according to the states of the…
Sergei A. Plotnikov, Judith Lehnert, Аlexander L. Fradkov, Eckehard Schöll
'Eckehard Schöll'] We study synchronization in delay-coupled neural networks of heterogeneous nodes. It is well known that heterogeneities in the nodes hinder synchronization when becoming too large. We show that an adaptive tuning of the overall coupling strength can be used to counteract the effect of the…
Muhammad Iqbal, Muhammad Rehan, Keum-Shik Hong
This paper exploits the dynamical modeling, behavior analysis, and synchronization of a network of four different FitzHugh-Nagumo (FHN) neurons with unknown parameters linked in a ring configuration under direction-dependent coupling. The main purpose is to investigate a robust adaptive control law for the…
Authors not listed
Accurate and efficient calculation of alchemical free energies is a critical challenge in computational chemistry, frequently hindered by the inherent limitations of conventional Thermodynamic Integration (TI) methods. These limitations include poor phasespace overlap between discrete alchemical states, inefficient…
Zihan Liu, Prashant N. Kambali, C. Nataraj
Dynamics Based Features Authors: ['Zihan Liu' 'Prashant N. Kambali' 'C. Nataraj'] - The proposed method combines physics-based modeling and data-based modeling to model complex nonlinear systems and adapt to parameter changes. - The physical model is parameterized to synthesize data for model training in the absence of…
Finlay Clark, Graeme Robb, Daniel Cole, Julien Michel
Alchemical absolute binding free energy (ABFE) calculations have substantial potential in drug discovery, but are often prohibitively computationally expensive. To unlock their potential, efficient automated ABFE workflows are required to reduce both computational cost and human intervention. We present a…
Priyan Bhattacharya, Karthik Raman, Arun K. Tangirala
Establishing a mapping between the emergent biological properties and network structure has always been of great relevance in systems and synthetic biology. Adaptation is one such biological property of paramount importance, which aids in regulation in the face of environmental disturbances. In this paper, we present a…
Priscilla Balestrucci, Marc Ernst
Many characteristics of sensorimotor adaptation are well predicted by the Kalman filter, a relatively simple learning algorithm for the optimal estimation of dynamic variables given noisy measurements. Yet not all Kalman filter predictions are confirmed empirically, suggesting that the model might be too simplistic to…
Alejandra C. Ventura, Horacio G. Rotstein
Degeneracy in dynamic models refers to these situations where multiple combinations of parameter values produce identical patterns for the observable variable. We investigate this phenomenon in two qualitatively different adaptive circuit mechanisms: nonlinear feedback loop (NFBL) and incoherent feedback loop (IFFL).…
Authors not listed
For applications in gas sensing, purification, and capture, we often wish to search a large set of metal-organic frameworks (MOFs) for the top-K in terms of their Henry coefficient of an adsorbate. A molecular simulation to predict the Henry coefficient of a MOF constitutes a Monte Carlo integration where each sample…
Omar Makke, Feng Lin
—Deep learning using neural networks has revolutionized machine learning and put artificial intelligence into everyday life. In order to introduce self-learning to dynamic systems other than neural networks, we extend the Brandt-Lin learning algorithm of neural networks to a large class of dynamic systems. This…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Authors not listed
This paper formally defines an operational isomorphism between spectral damping in molecular vibronic systems and neuromodulatory control in biological sensory systems. Without asserting causal continuity or physical identity across scales, we show that both domains instantiate the same class of output-selective…