13 papers · ranked by Valyu relevance
Christopher R. Holdgraf, Jochem W. Rieger, Cristiano Micheli, Stephanie Martin + 2 more
'Stephanie Martin' 'Robert T. Knight' 'Frederic E. Theunissen'] Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate…
Tomoya Nakai, Charlotte Constant-Varlet, Jérôme Prado
Cognitive computational neuroscience has received broad attention in recent years as an emerging area integrating cognitive science, neuroscience, and artificial intelligence. At the heart of this field, approaches using encoding models allow for explaining brain activity from latent and high-dimensional features…
Jesús E. Garca, Verónica A. González-López, Gustavo H. Tasca, Karina Y. Yaginuma + 1 more
In the framework of coding theory, under the assumption of a Markov process $(X_{t})$ on a finite alphabet $A,$ the compressed representation of the data will be composed of a description of the model used to code the data and the encoded data. Given the model, the Huffman’s algorithm is optimal for the number of bits…
Erick Lamilla, Christian Sacarelo, Manuel S. Alvarez-Alvarado, Arturo Pazmino + 4 more
'Arturo Pazmino' 'Peter Iza' 'Yichuang Sun' 'Haeyoung Lee' 'Oluyomi Simpson'] Based on orbital angular momentum (OAM) properties of Laguerre-Gaussian beams LG( $p,ℓ$), a robust optical encoding model for efficient data transmission applications is designed. This paper presents an optical encoding model based on an…
Richard Antonello, Alexander Huth
Many recent studies have shown that representations drawn from neural network language models are extremely effective at predicting brain responses to natural language. But why do these models work so well? One proposed explanation is that language models and brains are similar because they have the same objective: to…
MEHRAD SARMASHGHI, SHANTANU P. JADHAV, URI T. EDEN
Neurons can code for multiple variables simultaneously and neuroscientists are often interested in classifying neurons based on their receptive field properties. Statistical models provide powerful tools for determining the factors influencing neural spiking activity and classifying individual neurons. However, as…
S. Thomas Christie, Hayden R. Johnson, Paul R. Schrater
Human response times conform to several regularities including the Hick-Hyman law, the power law of practice, speed-accuracy trade-offs, and the Stroop effect. Each of these has been thoroughly modeled in isolation, but no account describes these phenomena as predictions of a unified framework. We provide such a…
Tien Thanh Thach, Antonio M. Scarfone
Accurate forecasting of stock market indices is crucial for investors, financial analysts, and policymakers. The integration of encoder and decoder architectures, coupled with an attention mechanism, has emerged as a powerful approach to enhance prediction accuracy. This paper presents a novel framework that leverages…
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.…
Byron H. Price, Jeffrey P. Gavornik
While it is universally accepted that the brain makes predictions, there is little agreement about how this is accomplished and under which conditions. Accurate prediction requires neural circuits to learn and store spatiotemporal patterns observed in the natural environment, but it is not obvious how such information…
Lei M. Li, Boris Ryabko
We consider the lossless compression bound of any individual data sequence. Conceptually, its Kolmogorov complexity is such a bound yet uncomputable. According to Shannon’s source coding theorem, the average compression bound is $nH$, where n is the number of words and H is the entropy of an oracle probability…
Zak Hussain, Marcel Binz, Rui Mata, Dirk U. Wulff
Large language models (LLMs) have the potential to revolutionize behavioral science by accelerating and improving the research cycle, from conceptualization to data analysis. Unlike closed-source solutions, open-source frameworks for LLMs can enable transparency, reproducibility, and adherence to data protection…
Emir Öztürk, Altan Mesut, Stefano Cirillo
Learning-based data compression methods have gained significant attention in recent years. Although these methods achieve higher compression ratios compared to traditional techniques, their slow processing times make them less suitable for compressing large datasets, and they are generally more effective for short…