19 papers · ranked by Valyu relevance
Shachar Gal, Yael Coldham, Michal Bernstein-Eliav, Ido Tavor
The search for an ‘ideal’ approach to investigate the functional connections in the human brain is an ongoing challenge for the neuroscience community. While resting-state functional magnetic resonance imaging (fMRI) has been widely used to study individual functional connectivity patterns, recent work has highlighted…
Sasideep Pasumarthi, Satwik Bathula, Nitya Tiwari, Himanshu Padole
Objective Recent advances in neuroimaging have highlighted the growing utility of resting-state functional magnetic resonance imaging (rs-fMRI) as an alternative to task-based fMRI. In addition to being simpler, cost-effective, and time-efficient, rs-fMRI is particularly advantageous for non-compliant populations such…
David G. Ellis, Michele R. Aizenberg
Accurate individual functional mapping of task activations is a potential tool for biomarker discovery and is critically important for clinical care. While structural imaging does not directly map task activation, we hypothesized that structural imaging contains information that can accurately predict variations in…
David G. Ellis, Michele R. Aizenberg
Accurate individual functional mapping of task activations is a potential tool for biomarker discovery and is critically important for clinical care. While structural imaging does not directly map task activation, we hypothesized that structural imaging contains information that can accurately predict variations in…
Sasideep Pasumarthi, Nitya Tiwari, Himanshu Padole
Predicting task-induced brain activation from resting-state fMRI (rs-fMRI) remains a significant challenge in computational neuroimaging, primarily due to the difficulty in simultaneously modeling the detailed temporal evolution and high spatial resolution of intrinsic neural activity. Most existing literature relies…
Junbeom Kwon, Jungwoo Seo, Heehwan Wang, Taesup Moon + 2 more
'Jiook Cha'] Title: Abstract Accurate prediction of the brain’s task reactivity from resting-state functional magnetic resonance imaging (fMRI) data remains a significant challenge in neuroscience. Traditional statistical approaches often fail to capture the complex, nonlinear spatiotemporal patterns of brain function.…
Ruben Sanchez-Romero, Takuya Ito, Ravi D. Mill, Stephen José Hanson + 1 more
Brain activity flow models estimate the movement of task-evoked activity over brain connections to help explain network-generated task functionality. Activity flow models have been shown to accurately generate task-evoked brain activations across a wide variety of brain regions and task conditions. However, these…
Parisa Mollaei, Amir Barati Farimani
Transition Pathways of G Protein-Coupled Receptor Revealed by Machine Learning Authors: ['Parisa Mollaei' 'Amir Barati Farimani'] Approximately, one-third of all U.S. Food and Drug Administration approved drugs target G protein-coupled receptors (GPCRs). However, more knowledge of protein structure-activity correlation…
Alexandre Englebert, Olivier Cornu, Christophe De Vleeschouwer
The need for Explainable AI is increasing with the development of deep learning. The saliency maps derived from convolutional neural networks generally fail in localizing with accuracy the image features justifying the network prediction. This is because those maps are either low-resolution as for CAM [Zhou et al.…
Authors not listed
Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this…
Marcus Lewis, Scott Purdy, Subutai Ahmad, Jeff Hawkins
The neocortex is capable of anticipating the sensory results of movement but the neural mechanisms are poorly understood. In the entorhinal cortex, grid cells represent the location of an animal in its environment, and this location is updated through movement and path integration. In this paper, we propose that…
Umut Güçlü, Marcel van Gerven
Converging evidence suggests that the mammalian ventral visual pathway encodes increasingly complex stimulus features in downstream areas. Using deep convolutional neural networks, we can now quantitatively demonstrate that there is indeed an explicit gradient for feature complexity in the ventral pathway of the human…
Pulkit Agrawal, Dustin Stansbury, Jitendra Malik, Jack L. Gallant
The human brain is adept at solving difficult high-level visual processing problems such as image interpretation and object recognition in natural scenes. Over the past few years neuroscientists have made remarkable progress in understanding how the human brain represents categories of objects and actions in natural…
Sai Mahit Vadaddi, Qiyuan Zhao, Brett M Savoie
Activation energy characterization of competing reactions is a costly, but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a…
Parth Mahendra
Most of the real world is governed by complex and chaotic dynamical systems. All of these dynamical systems pose a challenge in modelling them using neural networks. Currently, reservoir computing, which is a subset of recurrent neural networks, is actively used to simulate complex dynamical systems. In this work, a…
V S Vismaya, Alok Hareendran, Bharath V Nair, Sishu Shankar Muni + 1 more
'Martin Lellep'] This paper explores the prediction of subsequent steps in H´enon Map using various machine learning techniques. The H´enon map, well known for its chaotic behaviour, finds applications in various fields including cryptography, image encryption, and pattern recognition. Machine learning methods…
Cem Uran, Alina Peter, Andreea Lazar, William Barnes + 6 more
Predictive coding is an important candidate theory of self-supervised learning in the brain. Its central idea is that neural activity results from an integration and comparison of bottom-up inputs with contextual predictions, a process in which firing rates and synchronization may play distinct roles. Here, we…
Authors not listed
Pharmacophores are widely used to describe protein-ligand interactions, and the Grids of Pharmacophore Interaction Fields (GRAIL) method extends this concept by representing binding pockets as interpretable sets of interaction type-specific pharmacophoric maps. In this work, we propose a hybrid framework for binding…
Md. Shoaibur Rahman
This article presents an overview of the generalized formulations of the computations, optimization, and tuning of a deep feedforward neural network. A small network has been used to systematically explain the computing steps, which were then used to establish the generalized forms of the computations in forward and…