24 papers · ranked by Valyu relevance
Yijun Ma, Zehong Wang, Weixiang Sun, Yanfang Ye
Temporal graph learning is pivotal for deciphering dynamic systems, where the core challenge lies in explicitly modeling the underlying evolving patterns that govern network transformation. However, prevailing methods are predominantly task-centric and rely on restrictive assumptions such as short-term dependency…
Tony Lindeberg
This article presents an overview of a theory for performing temporal smoothing of temporal signals in such a way that: (i) temporally smoothed signals at coarser temporal scales are guaranteed to constitute simplifications of corresponding temporally smoothed signals at any finer temporal scale (including the original…
Peter Delmastro, Rushiv Arora, Edward A. Rietman, Hava T. Siegelmann
Recurrent Neural Networks (RNNs) have shown great success in modeling timedependent patterns, but there is limited research on their learned representations of latent temporal features and the emergence of these representations during training. To address this gap, we use timed automata (TA) to introduce a family of…
Yohan J. John, Kayle S. Sawyer, Karthik Srinivasan, Eli J. Müller + 2 more
'Brandon R. Munn' 'James M. Shine'] Title: Abstract Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static…
Janet M. Baker, Peter Cariani
Time is essential for understanding the brain. A temporal theory for realizing major brain functions (e.g., sensation, cognition, motivation, attention, memory, learning, and motor action) is proposed that uses temporal codes, time-domain neural networks, correlation-based binding processes and signal dynamics. It…
Nicole Sandra-Yaffa Dumont, Andreas Stöckel, P. Michael Furlong, Madeleine Bartlett + 4 more
'Madeleine Bartlett' 'Chris Eliasmith' 'Terrence C. Stewart' 'Baingio Pinna' 'Amedeo D’Angiulli'] The Neural Engineering Framework (Eliasmith & Anderson, 2003) is a long-standing method for implementing high-level algorithms constrained by low-level neurobiological details. In recent years, this method has been…
Pednekar, Amrapali, Álvaro Garrido-Pérez, Yara Khaluf + 1 more
This study explores the interference in temporal processing within a dual-task paradigm from an artificial intelligence (AI) perspective. In this context, the dualtask setup is implemented as a simplified version of the Overcooked environment with two variations, single task (T) and dual task (T+N). Both variations…
Tony Lindeberg
This article presents an overview of a theory for performing temporal smoothing on temporal signals in such a way that: (i) temporally smoothed signals at coarser temporal scales are guaranteed to constitute simplifications of corresponding temporally smoothed signals at any finer temporal scale (including the original…
Tomoya Maruyama, Jing Gong, Masahiro Takinoue
Bio-soft matter droplets formed via liquid-liquid phase separation (LLPS) of biopolymers have been found in living cells. Synthetic LLPS droplets have recently been employed in nanobiotechnology for artificial cell construction, molecular robotics, molecular computing, diagnosis, and therapeutics. Controlling the…
Qiaorong S. Yu, Zhaoze Wang, Vijay Balasubramanian
Hippocampal place and time cells encode spatial and temporal aspects of experience. Both have the same neural substrate, but have been modeled as having different functions and mechanistic origins, place cells as continuous attractors, and time cells as leaky integrators. Here, we show that both types emerge from two…
Erin R. Bigus, Hyun-Woo Lee, John C. Bowler, Jiani Shi + 1 more
Episodic memory requires encoding the temporal structure of experience and relies on brain circuits in the medial temporal lobe, including the medial entorhinal cortex (MEC). Recent studies have identified MEC ’time cells’, which fire at specific moments during interval timing tasks, collectively tiling the entire…
Authors not listed
Scanning ion conductance microscopy (SICM) offers non-contact, label-free imaging of live cells with nanometer-scale resolution. However, its conventional imaging mode is inherently slow due to repeated vertical scanning, limiting temporal resolution and causing inertial issues. Here, we present Scanning Counter Ion…
Christina Yi Jin, Anna Razafindrahaba, Raphaël Bordas, Virginie van Wassenhove
The internal clock is a psychological model for timing behavior. According to information theory, psychological time might be a manifestation of information flow during sensory processing. Herein, we tested three hypotheses: (1) whether sensory adaptation reduces (or novelty increases) the rate of the internal clock…
Zafeirios Fountas, Alexey Zakharov
Enquiries concerning the underlying mechanisms and the emergent properties of a biological brain have a long history of theoretical postulates and experimental findings. Today, the scientific community tends to converge to a single interpretation of the brain's cognitive underpinnings – that it is a Bayesian inference…
Rui Cao, Ian M. Bright, Marc W. Howard
In interval reproduction tasks, animals must remember the event starting the interval and anticipate the time of the planned response to terminate the interval. The interval reproduction task thus allows for studying both memory for the past and anticipation of the future. We analyzed previously published recordings…
Matthew Bailey, Mark Wilson
One of the critical tools of persistent homology is the persistence diagram. We demonstrate the applicability of a persistence diagram showing the existence of topological features (here rings in a 2D network) generated over time instead of space as a tool to analyse trajectories of biological networks. We show how the…
M. A. Elfouly, T. S. Amer
Movement disorders, like Parkinson’s disease, happen because of unusual patterns in the connections between the cortex and basal ganglia, often caused by timing issues in feedback pathways. This study uses a two-delay nonlinear dynamic model, based on the delayed the van der Pol oscillator, to examine how delays in the…
Nathan Thomas Han, Michael B. Steinborn, Liyu Cao
The complication clock, originally introduced by Wilhelm Wundt, remains a pivotal method in experimental psychology for probing the subjective timing of events. By localising the position of a moving pointer, one can objectively measure when someone perceives an event to have occurred. The method therefore maps…
Authors not listed
Atomistic simulations provide essential mechanistic insights into chemical processes, yet many important phenomena in chemistry and materials science occur on timescales that are inaccessible to molecular dynamics. Existing computational approaches force a choice between atomic resolution on relatively short timescales…
Jane Kondev, Marc Kirschner, Hernan G. Garcia, Gabriel L. Salmon + 1 more
Many biological processes can be thought of as the result of an underlying dynamics in which the system repeatedly undergoes distinct and abortive trajectories with the dynamical process only ending when some specific process, purpose, structure or function is achieved. A classic example is the way in which…
Authors not listed
A framework for catalysis based on categorical aperture selection rather than temporal acceleration is presented. Traditional catalysis theory describes catalysts as agents that accelerate reactions by lowering activation energies, implicitly treating time as the fundamental variable and reaction rate enhancement as…
Sreejan Kumar, Matthieu B. Le Cauchois, Alexander Mathis, Lea Duncker + 2 more
The brain seamlessly transforms sensory information into precisely-timed movements, enabling us to type familiar words, play musical instruments, or perform complex motor routines with millisecond precision. This process often involves organizing actions into stereotyped “chunks”. Intriguingly, brain regions that are…
Marina Gorostiola González, Remco L. van den Broek, Thomas G.M. Braun, Magdalini Chatzopoulou + 4 more
Proteochemometric (PCM) modelling is a powerful computational drug discovery tool used in bioactivity prediction of potential drug candidates relying on both chemical and protein information. In PCM features are computed to describe small molecules and proteins, which directly impact the quality of the predictive…
Authors not listed
Kinetic modeling is essential for predicting changes in food quality during processing and storage. This study evaluates the application of physics-informed neural networks (PINN) for food kinetic modeling, integrating kinetic insights into neural network frameworks. Based on three case studies, namely seed drying…