25 papers · ranked by Valyu relevance
Midhula Chandran, Anna Thorwart
Ability to recall the timing of events is a crucial aspect of associative learning. Yet, traditional theories of associative learning have often overlooked the role of time in learning association and shaping the behavioral outcome. They address temporal learning as an independent and parallel process. Temporal Coding…
Rannie Xu, Russell M. Church, Yuka Sasaki, Takeo Watanabe
Our ability to discriminate temporal intervals can be improved with practice. This learning is generally thought to reflect an enhancement in the representation of a trained interval, which leads to interval-specific improvements in temporal discrimination. In the present study, we asked whether temporal learning is…
Rannie Xu, Russell M. Church, Yuka Sasaki, Takeo Watanabe
The ability to discriminate sub-second intervals can be improved with practice, a process known as temporal perceptual learning (TPL). A central question in TPL is whether training improves the low-level sensory representation of a temporal interval or optimizes a set of task-specific response strategies. Here, we…
Julian Ng-Kee-Kwong, Mufeng Tang, Thomas Akam, Rafal Bogacz
The ability to extract and exploit temporal structure across diverse tasks is central to human cognition. Neuroscientists have typically relied on recurrent neural networks (RNNs) trained with backpropagation through time (BPTT) when modelling neural and behavioural processes such as decision-making and motor control.…
Cybelle M. Smith, Sharon L. Thompson-Schill, Anna C. Schapiro
Our environment contains temporal information unfolding simultaneously at multiple timescales. How do we learn and represent these dynamic and overlapping information streams? We investigated these processes in a statistical learning paradigm with simultaneous short and long timescale contingencies. Human participants…
Alan Veliz-Cuba, Harel Shouval, Krešimir Josić, Zachary P. Kilpatrick
Neuronal circuits can learn and replay firing patterns evoked by sequences of sensory stimuli. After training, a brief cue can trigger a spatiotemporal pattern of neural activity similar to that evoked by a learned stimulus sequence. Network models show that such sequence learning can occur through the shaping of…
Felix Ball, Inga Spuerck, Toemme Noesselt
While temporal expectations (TE) generally improve reactions to temporally predictable events, it remains unknown how temporal rule learning and explicit knowledge about temporal rules contribute to performance improvements and whether any contributions generalise across modalities. Here, participants discriminated the…
Oussama H Hamid, Andreas Wendemuth, Jochen Braun
Background We investigated how temporal context affects the learning of arbitrary visuo-motor associations. Human observers viewed highly distinguishable, fractal objects and learned to choose for each object the one motor response (of four) that was rewarded. Some objects were consistently preceded by specific other…
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…
Niloufar Razmi, Xufeng Caesar Dai, Leah Bakst, Matthew R. Nassar
People rapidly recalibrate their expectations about the world in the face of surprising observations. This recalibration should depend on the temporal structure of the environment, however how people should and do learn temporal structures remains unknown. To examine this gap, we developed a Bayesian model that infers…
Jackson Rozells, Jeffrey P. Gavornik
The brain uses temporal information to link discrete events into memory structures supporting recognition, prediction, and a wide variety of complex behaviors. It is still an open question how experience-dependent synaptic plasticity creates memories including temporal and ordinal information. Various models have been…
Matthew Farrell, Cengiz Pehlevan
Understanding how neural circuits generate sequential activity is a longstanding challenge. While foundational theoretical models have shown how sequences can be stored as memories with Hebbian plasticity rules, these models considered only a narrow range of Hebbian rules. Here we introduce a model for arbitrary…
Zoran Tiganj, Samuel J. Gershman, Per B. Sederberg, Marc W. Howard
Natural learners must compute an estimate of future outcomes that follow from a stimulus in continuous time. Widely used reinforcement learning algorithms discretize continuous time and estimate either transition functions from one step to the next (model-based algorithms) or a scalar value of exponentially-discounted…
Adam Kohan, Ed Rietman, Hava T. Siegelmann
In artificial neural networks, weights are a static representation of synapses. However, synapses are not static, they have their own interacting dynamics over time. To instill weights with interacting dynamics, we use a model describing synchronization that is capable of capturing core mechanisms of a range of neural…
Benjamin J. De Corte, Başak Akdoğan, Peter D. Balsam
Timing underlies a variety of functions, from walking to perceiving causality. Neural timing models typically fall into one of two categories-“ramping” and “population-clock” theories. According to ramping models, individual neurons track time by gradually increasing or decreasing their activity as an event approaches.…
Gabriele Cimolino, François Rivest
Animals can quickly learn the timing of events with fixed intervals and their rate of acquisition does not depend on the length of the interval. In contrast, recurrent neural networks that use gradient based learning have difficulty predicting the timing of events that depend on stimulus that occurred long ago. We…
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…
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Accurate prediction of chemical reaction yields remains essential for accelerating synthesis optimization, yet current machine learning models face critical limitations in capturing temporal dynamics, providing calibrated uncertainty estimates, and explicitly modeling reactant-to-product transformations. Here we…
Dorothée B. Hoppe, Petra Hendriks, Michael Ramscar, Jacolien van Rij
Error-driven learning algorithms, which iteratively adjust expectations based on prediction error, are the basis for a vast array of computational models in the brain and cognitive sciences that often differ widely in their precise form and application: they range from simple models in psychology and cybernetics to…
Oswald Berthold, Verena V. Hafner
—In this paper, we propose the concept of sensorimotor tappings, a new graphical technique that explicitly represents relations between the time steps of an agent's sensorimotor loop and a single training step of an adaptive internal model. In the simplest case this is a relation linking two time steps. In realistic…
Michail Maniadakis, Panos Trahanias
The representation of the environment assumes the encoding of four basic dimensions in the brain, that is the 3D space and time. The vital role of time for cognition is a topic that recently attracted increasing research interest. Surprisingly, the scientific community investigating mind-time interactions has mainly…
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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…
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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…
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Accurately modeling the dynamics of open quantum systems is critical for advancing quantum technologies, yet traditional methods often struggle with balancing accuracy and efficiency. Machine learning (ML) offers a promising alternative, particularly through recursive models that predict system evolution based on the…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…