23 papers · ranked by Valyu relevance
Viviane Kostrubiec, Pier-Giorgio Zanone, Armin Fuchs, J. A. Scott Kelso
'J. A. Scott Kelso'] Using an approach that combines experimental studies of bimanual movements to visual stimuli and theoretical modeling, the present paper develops a dynamical account of sensorimotor learning, that is, how new skills are acquired and old ones modified. A significant aspect of our approach is the…
James Hazelden, Eric Shea-Brown
Rich feature learning in tasks that unfold over time often requires the model to pass through bifurcations, constituting qualitative changes in the underlying model dynamics. We develop a local theory of gradient descent near these transitions through the empirical state-space neural tangent kernel (sNTK). Our central…
Eva van Tegelen, George van Voorn, Ioannis N. Athanasiadis, Peter van Heijster
Forecasting system behaviour near and across bifurcations is crucial for identifying potential shifts in dynamical systems. While machine learning has recently been used to learn critical transitions and bifurcation structures from data, most studies remain limited as they exclusively focus on discrete-time methods and…
Tomoki Kurikawa, Kunihiko Kaneko, Olaf Sporns
Learning is a process that helps create neural dynamical systems so that an appropriate output pattern is generated for a given input. Often, such a memory is considered to be included in one of the attractors in neural dynamical systems, depending on the initial neural state specified by an input. Neither neural…
Yohei Kondo, Kunihiko Kaneko, Shuji Ishihara
Dynamical systems are used to model a variety of phenomena in which the bifurcation structure is a fundamental characteristic. Here we propose a statistical machine-learning approach to derive lowdimensional models that automatically integrate information in noisy time-series data from partial observations. The method…
Thomas M. Bury, Daniel Dylewsky, Chris T. Bauch, Madhur Anand + 3 more
'Leon Glass' 'Alvin Shrier' 'Gil Bub'] Many natural and man-made systems are prone to critical transitions-abrupt and potentially devastating changes in dynamics. Deep learning classifiers can provide an early warning signal for critical transitions by learning generic features of bifurcations from large simulated…
Authors not listed
Despite explosive expansion of artificial intelligence based on artificial neural networks (ANNs), these are employed as "black boxes", as it is unclear how, during learning, they form memories or develop unwanted features, including spurious memories and catastrophic forgetting. Much research is available on isolated…
Tomoki Kurikawa, Kunihiko Kaneko, Tim Behrens
Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a…
Leonardo Giannantoni, Alessandro Savino, Stefano Di Carlo
In the expanding realm of computational biology, Reinforcement Learning (RL) emerges as a novel and promising approach, especially for designing and optimizing complex synthetic biological circuits. This study explores the application of RL in controlling Hopf bifurcations within ODE-based systems, particularly under…
Satoru Tadokoro, Akihiro Yamaguchi, Takao Namiki, Ichiro Tsuda
Machines with Control Inputs Authors: ['Satoru Tadokoro' 'Akihiro Yamaguchi' 'Takao Namiki' 'Ichiro Tsuda'] Abstract: By extending the extreme learning machine by additional control inputs, we achieved almost complete reproduction of bifurcation structures of dynamical systems. The learning ability of the proposed…
Keita Tokuda, Yuichi Katori
Introduction Nonlinear and non-stationary processes are prevalent in various natural and physical phenomena, where system dynamics can change qualitatively due to bifurcation phenomena. Machine learning methods have advanced our ability to learn and predict such systems from observed time series data. However…
Chengzuo Zhuge, Jiawei Li, Wei Chen
Tipping points occur in many real-world systems, at which the system shifts suddenly from one state to another. The ability to predict the occurrence of tipping points from time series data remains an outstanding challenge and a major interest in a broad range of research fields. Particularly, the widely used methods…
Daniel Köglmayr, Christoph Räth
Model-free and data-driven prediction of tipping point transitions in nonlinear dynamical systems is a challenging and outstanding task in complex systems science. We propose a novel, fully data-driven machine learning algorithm based on next-generation reservoir computing to extrapolate the bifurcation behavior of…
Jascha Sohl‐Dickstein
— Some fractals – for instance those associated with the Mandelbrot and quadratic Julia sets – are computed by iterating a function, and identifying the boundary between hyperparameters for which the resulting series diverges or remains bounded [7]. Neural network training similarly involves iterating an update…
Gergő Bohner, Maneesh Sahani
In a common experimental setting, the behaviour of a noisy dynamical system is monitored in response to manipulations of one or more control parameters. Here, we introduce a structured model to describe parametric changes in qualitative system behaviour via stochastic bifurcation analysis. In particular, we describe an…
Elisabeth Roesch, Michael P.H. Stumpf
Dynamical systems with intricate behaviour are all-pervasive in biology. Many of the most interesting biological processes indicate the presence of bifurcations, i.e. phenomena where a small change in a system parameter causes qualitatively different behaviour. Bifurcation theory has become a rich field of research in…
Ulises Pereira, Nicolas Brunel
Two strikingly distinct types of activity have been observed in various brain structures during delay periods of delayed response tasks: Persistent activity (PA), in which a sub-population of neurons maintains an elevated firing rate throughout an entire delay period; and Sequential activity (SA), in which…
Francisco Páscoa dos Santos, Paul FMJ Verschure
Although the primary function of excitatory-inhibitory (E-I) homeostasis is the maintenance of mean firing rates, the conjugation of multiple homeostatic mechanisms is thought to be pivotal to ensuring edge-of-bifurcation dynamics in cortical circuits. However, computational studies on E-I homeostasis have focused…
Nathaniel Bridges, Matthew Stickle, Karen Moxon
When learning to use a brain-machine interface (BMI), the brain modulates neuronal activity patterns, exploring and exploiting the state space defined by their neural manifold. Neurons directly involved in BMI control can display marked changes in their firing patterns during BMI learning. However, whether these…
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…
Ben Engelhard, Ran Darshan, Nofar Ozeri-Engelhard, Zvi Israel + 4 more
During sensorimotor learning, neuronal networks change to optimize the associations between action and perception. In this study, we examine how the brain harnesses neuronal patterns that correspond to the current action-perception state during learning. To this end, we recorded activity from motor cortex while monkeys…
J. A. Menéndez, J. A. Hennig, M. D. Golub, E. R. Oby + 5 more
A remarkable demonstration of the flexibility of mammalian motor systems is primates’ ability to learn to control brain-computer interfaces (BCIs). This constitutes a completely novel motor behavior, yet primates are capable of learning to control BCIs under a wide range of conditions. BCIs with carefully calibrated…
Minija Tamosiunaite, Christian Tetzlaff, Florentin Wörgötter
In many situations it is behaviorally relevant for an animal to respond to co-occurrences of perceptual, possibly polymodal features, while these features alone may have no importance. Thus, it is crucial for animals to learn such feature combinations in spite of the fact that they may occur with variable intensity and…