8 papers · ranked by Valyu relevance
Jianqiang Sun, Wei Cao, Ikumi Kamachi, Kentaro K. Shimizu + 1 more
Time-series RNA sequencing provides a powerful framework for studying dynamic gene regulation, yet conventional analyses usually represent gene expression profiles as real-valued vectors in Euclidean space and quantify similarity using correlation or distance. Inspired by quantum information theory, we present a…
HaDi MaBouDi, Krishna Subramani, Hamid Soltanian-Zadeh, Shun-ichi Amari + 1 more
Natural scenes contain higher-order statistical structures that can be encoded in their spatial phase information. Nevertheless, little progress has been made in modeling phase information of images, and understanding efficient representation of the image phases in the brain. In order to capture spatial phase structure…
Dipayan Biswas, P. Sooryakiran, V. Srinivasa Chakravarthy
Recurrent neural networks with associative memory properties are typically based on fixed-point dynamics, which is fundamentally distinct from the oscillatory dynamics of the brain. There have been proposals for oscillatory associative memories, but here too, in the majority of cases, only binary patterns are stored as…
Hanna Bugler, Rodrigo Pommot Berto, Roberto Souza, Ashley D. Harris
To determine the significance of complex-valued inputs and complex-valued convolutions compared to real-valued inputs and real-valued convolutions in Convolutional Neural Networks (CNNs) for frequency and phase correction (FPC) of GABA-edited Magnetic Resonance Spectroscopy (MRS) data. An ablation study was performed…
Michael Hersche, Stefan Lippuner, Matthias Korb, Luca Benini + 1 more
'Abbas Rahimi'] Brain-inspired high-dimensional (HD) computing represents and manipulates data using very long, random vectors with dimensionality in the thousands. This representation provides great robustness for various classification tasks where classifiers operate at low signal-to-noise ratio (SNR) conditions.…
Xihe Xie, Chang Cai, Pablo F. Damasceno, Srikantan Nagarajan + 1 more
How do functional brain networks emerge from the underlying wiring of the brain? We examine how resting-state functional activation patterns emerge from the underlying connectivity and length of white matter fibers that constitute its “structural connectome”. By introducing realistic signal transmission delays along…
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…
Maciej Kaczyński, Zbigniew Piotrowski, Naveen Chilamkurti
This paper presents a method of high-capacity and transparent watermarking based on the usage of deep neural networks with the adjustable subsquares properties algorithm to encode the data of a watermark in high-quality video using the H.265/HEVC (High-Efficiency Video Coding) codec. The aim of the article is to…