10 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…
Liang Ma, Qiang Li, Tulay Adali, Armin Iraji + 1 more
Understanding complex dynamics from spatiotemporal signals requires robust tools capable of decoding reoccurring patterns. Traditional ICA methods often overlook the spatially non-stationarity nature of brain activity across both the frequency and spatial domains. We propose a novel data-driven approach, named…
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…
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…
Xinyu Ye, Xiaodong Ma, Ziyi Pan, Zhe Zhang + 3 more
to propose a two-step non-local principal component analysis (PCA) method and demonstrate its utility for denoising diffusion tensor MRI (DTI) with a few diffusion directions. A two-step denoising pipeline was implemented to ensure accurate patch selection even with high noise levels and was coupled with data…
Laura Sainz Villalba, P. Michael Furlong, Madeleine Bartlett, Nicole Sandra-Yaffa Dumont
The brain faces the feature binding problem: how are multiple stimulus features and variables combined into coherent representations that support flexible behavior? A key finding from neuroscience is that some brain regions employ factorized representations, where distinct features are encoded in neural state space in…
Paul S. Scotti, Jiageng Chen, Julie D. Golomb
Inverted encoding models (IEMs) have recently become a popular method for investigating neural representations by reconstructing the contents of perception, attention, and memory from neuroimaging data. However, the standard IEM procedure can produce spurious results and interpretation issues. Here we present a novel…
Jörn Diedrichsen, Atsushi Yokoi, Spencer A. Arbuckle
Representational models specify how complex patterns of neural activity relate to visual stimuli, motor actions, or abstract thoughts. Here we review pattern component modeling (PCM), a practical Bayesian approach for evaluating such models. Similar to encoding models, PCM evaluates the ability of models to predict…