18 papers · ranked by Valyu relevance
Md Tawheedul Islam Bhuian, Kyoung-Don Kang
Event cameras are bio-inspired sensors that asynchronously capture logarithmic intensity changes, offering inherent advantages in high-speed and high-dynamic-range scenarios. However, the sparse and asynchronous nature of event streams poses a fundamental challenge for modern deep learning architectures. To enable…
Hongwei Ren, Youxin Jiang, Tuopusen Huang, Xiangqian Wu
Event cameras offer distinctive advantages, including microsecond-level latency and high dynamic range, rendering them promising for challenging perception tasks. Inspired by biological vision, they output asynchronous and sparse event streams rather than dense image frames, creating a fundamental mismatch with…
Lingyun Ke, M. Hu
Encoding static images into spike trains is a crucial step for enabling Spiking Neural Networks (SNNs) to process visual information efficiently. However, existing schemes such as rate coding, Poisson encoding, and time-to-first-spike (TTFS) often ignore spatial relationships and yield temporally inconsistent spike…
Ali Mehrabi, Neethu Sreenivasan, Upul Gunawardana, Gaetano Gargiulo + 1 more
Reliable and low-latency seizure detection from electroencephalography (EEG) is critical for continuous clinical monitoring and emerging wearable health technologies. Spiking neural networks (SNNs) provide an event-driven computational paradigm that is well suited to real-time signal processing, yet achieving…
Lekai Qian, Haoyu Gu, Dehan Li, Boyu Cao + 1 more
Symbolic music representation is a fundamental challenge in computational musicology. While grid-based representations effectively preserve pitch-time spatial correspondence, their inherent data sparsity leads to low encoding efficiency. Discrete-event representations achieve compact encoding but fail to adequately…
Meisen Wang, Hao Deng, Wei Bao, Ma Yuanxiao + 4 more
Event cameras provide microsecond-level temporal resolution, low latency, and high dynamic range, offering potential for perception under fast motion and challenging illumination conditions. However, existing Event-based Object Detection (EOD) methods face limitations at both the representation and model levels: prior…
Claudio Cimarelli, Jose Andres Millan-Romera, Holger Voos, Jose Luis Sanchez-Lopez + 1 more
Event-based (neuromorphic) cameras depart from frame-based sensing by reporting asynchronous per-pixel brightness changes. This produces sparse, low-latency data streams with extreme temporal resolution but demands new processing paradigms. In this survey, we systematically examine neuromorphic vision along three main…
Juliette Boscheron, Pepijn Schoenmakers, Arthur Trivier, Florian Lance + 4 more
Episodic autobiographical memory (EAM) relies on reactivations of cortical regions engaged during event encoding. While reinstatement of sensory regions is well documented, the role of internal signals from the person’s body has received less attention. Here, we investigated whether motor representations from encoding…
R. R. Ogden, Fridovich-Keil, David, Takashi Tanaka
The use of remote vision sensors for autonomous decision-making poses the challenge of transmitting high-volume visual data over resource-constrained channels in real-time. In robotics and control applications, many systems can quickly destabilize, which can exacerbate the issue by necessitating higher sampling…
Matthew Logie, Camille Grasso, Virginie van Wassenhove
How does the structure of events influence the when and the where of life experiences in comparison to the what? We developed a novel virtual reality (VR) environment to understand how the quantity of information within nested structures influence participants’ memory for events. Participants moved through a series of…
Ziyuan Yin, John Moraros, Shuihua Wang
Scalp electroencephalography (EEG) based seizure prediction plays a critical role in improving the quality of life for patients with drug-resistant epilepsy, offering the potential for real-time warnings and timely interventions. Despite its clinical significance and decades of research, the field still lacks an open…
Silvy H.P. Collin
Continuous experiences are constantly segmented into separate events in memory, which is accompanied by increased hippocampal activity at the boundaries between these events. Extracting knowledge across all these experiences about what type of events to expect in a certain environment leads to event schemas being…
Anu Roopa Devi Sekar, Ruban Nersisson
Human facial emotion recognition (FER) is a vibrant research field. This research proposes a novel, biologically inspired hybrid FER framework that uniquely connects event-driven Spiking Neural Networks (SNNs) with deep learning, specifically a Spike-based Support Vector Machine (S-SVM), which is designed for its…
Cedric Foucault, Tiffany Bounmy, Sébastien Demortain, Bertrand Thirion + 2 more
Assessing probabilities and predicting future events are fundamental for perception and adaptive behavior, yet the neural representations of probability remain elusive. While previous studies have shown that neural activity in several brain regions correlates with probability-related factors such as surprise and…
Quanlong Fan, Gang Xu, Yunge Wang, Mengke Wu + 1 more
In real-time event detection, social media platforms like Twitter, Instagram, and Weibo provide valuable data, where users share updates, opinions, and multimedia content. However, existing event detection algorithms typically rely on a single data source or modality, limiting their ability to process the complex…
Sahil Thapa, Miguelangel Tamargo, Oluwatosin Oluwadare
Alternative splicing (AS) is a fundamental regulatory mechanism that expands transcriptomic and proteomic diversity by generating multiple mRNA isoforms from a single gene. Aberrant AS has been implicated in numerous diseases through the production of dysfunctional or pathogenic protein variants. However, much of the…
Andrew D Levy, Peter Zeidman, Karl Friston
Sequential experimental paradigms are fundamental to cognitive neuroscience, yet standard event-related response analysis struggles with the temporal variability inherent to these designs. Conventional epoching treats each event within a sequence as an independent response, discarding the temporal dependencies between…
Fang Wang, Xiaoqiang Liang, Xingqian Du, Mariusz Szwoch
In this paper, we explore the spiking encoding methodology within spiking neural networks for affective state recognition, deriving inspiration from the principles of quantum entanglement. A pioneering encoding strategy is proposed based on the strategic utilization of the quantum mechanical phenomenon of entanglement.…