22 papers · ranked by Valyu relevance
Tibin John, Yajun Zhou, Ayman Aljishi, Bastian Rieck + 2 more
Time is a critical component of memory and yet how the hippocampus incorporates temporal information with the sensory contents of memories remains unclear. We hypothesized that the hippocampus can learn arbitrary sequences through rapid changes in the tuning and population geometry of individual neurons to mirror the…
Yiren Ren, Grace Leslie, Thackery Brown, Thiago P. Fernandes
Music is omnipresent in daily life and may interact with critical cognitive processes including memory. Despite music’s presence during diverse daily activities including studying, commuting, or working, existing literature has yielded mixed results as to whether music improves or impairs memory for information…
Riccardo Mereu, Gabriele Trivigno, Gabriele Berton, Carlo Masone + 1 more
'Barbara Caputo'] Abstract— In robotics, Visual Place Recognition is a continuous process that receives as input a video stream to produce a hypothesis of the robot's current position within a map of known places. This task requires robust, scalable, and efficient techniques for real applications. This work proposes a…
Yiren Ren, Vishwadeep Ahluwalia, Claire Arthur, Thackery Brown
Statistical learning—the ability to extract patterns from noisy continuous experiences—is fundamental to human cognition. Yet, how contextual factors shape this process remains poorly understood. Music is an important example of such contextual factors, because it is ubiquitous in human experience and provides a rich…
Ai-Su Li, Jan Theeuwes, Dirk van Moorselaar
Through statistical learning, humans are able to extract temporal regularities, using the past to predict the future. Evidence suggests that learning relational structures makes it possible to anticipate the imminent future; yet, the neural dynamics of predicting the future and its time-course remain elusive. To…
Gabriele Berton, Gabriele Trivigno, Barbara Caputo, Carlo Masone
—Visual Place Recognition aims at recognizing previously visited places by relying on visual clues, and it is used in robotics applications for SLAM and localization. Since typically a mobile robot has access to a continuous stream of frames, this task is naturally cast as a sequence-to-sequence localization problem.…
Nataliya Strokina, Wenyan Yang, Joni Pajarinen, Nikolay Serbenyuk + 2 more
One of the key challenges in implementing reinforcement learning methods for real-world robotic applications is the design of a suitable reward function. In field robotics, the absence of abundant datasets, limited training time, and high variation of environmental conditions complicate the task further. In this paper…
Viacheslav Osaulenko
In this paper we start with a simple question, how is it possible that humans can recognize different movements over skin with only a prior visual experience of them? Or in general, what is the representation of spatial sequences that are invariant to scale, rotation, and translation across different modalities? To…
Xianhui He, Philipp K. Büchel, Simon Faghel-Soubeyrand, Janina Klingspohr + 2 more
Experiences reshape our internal representations of the world. However, the neural and cognitive dynamics of this process are largely unknown. Here, we investigated how sequence learning reorganizes neural representations and how sleep-dependent consolidation contributes to this transformation. Using high-density…
Lalit Pandey, Donsuk Lee, Samantha M. W. Wood, Justin N. Wood + 1 more
'Tianming Yang'] How do newborns learn to see? We propose that visual systems are space-time fitters, meaning visual development can be understood as a blind fitting process (akin to evolution) in which visual systems gradually adapt to the spatiotemporal data distributions in the newborn’s environment. To test whether…
Charlotte Volk, Christopher C. Pack, Shahab Bakhtiari
Generalization of visual perceptual learning (VPL) to unseen conditions varies across tasks. Previous work suggests that training curriculum may be integral to generalization, yet a theoretical explanation is lacking. We propose an explanatory theory of visual learning generalization and curriculum effects by…
Barna Zajzon, Renato Duarte, Abigail Morrison
To acquire statistical regularities from the world, the brain must reliably process, and learn from, spatiotemporally structured information. Although an increasing number of computational models have attempted to explain how such sequence learning may be implemented in the neural hardware, many remain limited in…
Athanasios Efthymiou, Stevan Rudinac, Monika Kackovic, Nachoem M. Wijnberg + 1 more
Artistic Sequences Authors: ['Athanasios Efthymiou' 'Stevan Rudinac' 'Monika Kackovic' 'Nachoem M. Wijnberg' 'Marcel Worring'] We propose Set2Seq Transformer, a novel sequential multiple instance architecture, that learns to rank permutation aware set representations of sequences. First, we illustrate that learning…
Vadym Gryshchuk, Cornelius Weber, Chu Kiong Loo, Stefan Wermter
Lifelong learning is a long-standing aim for artificial agents that act in dynamic environments, in which an agent needs to accumulate knowledge incrementally without forgetting previously learned representations. We investigate methods for learning from data produced by event cameras and compare techniques to mitigate…
Mingcan Yu, Junying Wang
Although principles of neuroscience like reinforcement learning, visual perception and attention have been applied in machine learning models, there is a huge gap between machine learning and mammalian learning. Based on the advances in neuroscience, we propose the "context sequence theory" to give a common explanation…
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…
Mahmoud Rokaya, Dalia I. Hemdan, Mohammed A. Alzain, El-Sayed Atlam
Introduction A central limitation of existing temporal image analysis and video understanding models lies in their reliance on explicit motion cues, dense supervision, or auxiliary modalities, which constrains their ability to infer latent temporal structure, evolving semantic states, and long-range dependencies from…
Lennart Luettgau, Tore Erdmann, Sebastijan Veselic, Kimberly L. Stachenfeld + 3 more
'Kimberly L. Stachenfeld' 'Zeb Kurth-Nelson' 'Rani Moran' 'Raymond J. Dolan'] Title: Significance Humans possess a remarkable ability to adapt rapidly and flexibly to novel situations, a key aspect of cognition. While past studies detail how we learn from single processes, we have limited understanding of how we…
Michael J. Lee, James J. DiCarlo, Tim Christian Kietzmann
A core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images. One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains through two connected mechanisms: 1) the re-representation of incoming…
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This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
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Molecular property prediction is a fundamental task in computational chemistry with critical applications in drug discovery and materials science. While recent works have explored Large Language Models (LLMs) for this task, they primarily rely on textual molecular representations such as SMILES/SELFIES, which can be…
Heeseung Lee, Daeho Kim, Heyin Lee, Namyoung Gwak + 6 more
- 1. Computational Science Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea - 2. Department of Materials Science and Engineering, Korea University, 145 Anam-ro, Seoul 02841, Republic of Korea - 3. Department of Chemical and Biological Engineering, Korea University, Seoul 02841…