Search · four archives
Search · four archives
27 papers · ranked by Valyu relevance
Abhay Kumar Dwivedi, Shanu Saklani, Soumya Dutta
The extensive adoption of Deep Neural Networks has led to their increased utilization in challenging scientific visualization tasks. Recent advancements in building compressed data models using implicit neural representations have shown promising results for tasks like spatiotemporal volume visualization and…
Ian Charest, Nikolaus Kriegeskorte
In the early days of neuroimaging, brain function was investigated by averaging across voxels within a region, stimuli within a category and individuals within a group. These three forms of averaging discard important neuroscientific information. Recent studies have explored analyses that combine the evidence in…
Jörn Diedrichsen, Tobias Wiestler, Naveed Ejaz
How do populations of neurons represent a variable of interest? The notion of feature spaces is a useful concept to approach this question: According to this model, the activation patterns across a neuronal population are composed of different pattern components. The strength of each of these components varies with one…
Jin Liu, Xin Hu, Xinke Shen, Sen Song + 1 more
Despite the fact that human daily emotions are co-occurring by nature, most neuroscience studies have primarily adopted a univariate approach to identify the neural representation of emotion (emotion experience within a single emotion category) without adequate consideration to the co-occurrence of different emotions…
Helen Blank, Janine Bayer
Similarity-based categorization can be performed by memorizing category members as exemplars or by abstracting the central tendency of the category - the prototype. In similarity-based categorization of stimuli with clearly identifiable dimensions from two categories, prototype representations were previously located…
Xiangyang He, Yubo Tao, Qirui Wang, Hai Lin
Multivariate spatial data plays an important role in computational science and engineering simulations. The potential features and hidden relationships in multivariate data can assist scientists to gain an in-depth understanding of a scientific process, verify a hypothesis and further discover a new physical or…
Quan Wan, Ying Cai, Jason Samaha, Bradley R. Postle
How does the neural representation of visual working memory content vary with behavioural priority? To address this, we recorded electroencephalography (EEG) while subjects performed a continuous-performance 2-back working memory task with oriented-grating stimuli. We tracked the transition of the neural representation…
Nazanin Moradinasab, Suchetha Sharma, Ronen Bar-Yoseph, Shlomit Radom-Aizik + 4 more
'Shlomit Radom-Aizik' 'Kenneth C. Bilchick' 'Dan M. Cooper' 'Arthur Weltman' 'Donald E. Brown'] The multivariate time series classification (MTSC) task aims to predict a class label for a given time series. Recently, modern deep learning-based approaches have achieved promising performance over traditional methods for…
Karl J. Friston, Jörn Diedrichsen, Emma Holmes, Peter Zeidman
This technical note describes a variational or Bayesian implementation of representational similarity analysis (RSA) and pattern component modelling (PCM). It considers RSA and PCM as Bayesian model comparison procedures that assess the evidence for stimulus or condition-specific patterns of responses distributed over…
Stefano Anzellotti, Alfonso Caramazza, Rebecca Saxe, Saad Jbabdi
When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of…
Massimo Salvi
In this paper we give a new and simple algorithm to put any multivariate polynomial into a normal determinant form in which each entry has the form i i bi a x , and in each column the same variable appears. We also apply the algorithm to obtain a triangular determinant representation, a reduced determinant…
Yuanning Li, R. Mark Richardson, Avniel Singh Ghuman
The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the…
Silvia Bernardi, Marcus K. Benna, Mattia Rigotti, Jérôme Munuera + 2 more
The curse of dimensionality plagues models of reinforcement learning and decision-making. The process of abstraction solves this by constructing abstract variables describing features shared by different specific instances, reducing dimensionality and enabling generalization in novel situations. Here we characterized…
Michael P. Evers, Markus Kontny
We provide a novel representation of the total n-th derivative of the multivariate composite function f ◦ g, i.e. a generalized Fa`a di Bruno's formula. To this end, we make use of properties of the Kronecker product and the n-th derivative of the left-composite f, which allow the use of a multivariate form of partial…
Elisa Frutos-Bernal, Laura Vicente-González, Jose Luis Vicente-Villardón
'Jose Luis Vicente-Villardón'] In behavioral research, it is very common to have manage multiple datasets containing information about the same set of individuals, in such a way that one dataset attempts to explain the others. To address this need, in this paper the Tucker3-PCovR model is proposed. This model is a…
Yuanqing Li, Jinyi Long, Lin He, Haidong Lu + 3 more
'Essa Yacoub'] Considering the two-class classification problem in brain imaging data analysis, we propose a sparse representation-based multi-variate pattern analysis (MVPA) algorithm to localize brain activation patterns corresponding to different stimulus classes/brain states respectively. Feature selection can be…
Subhashis Hazarika, Ayan Biswas, Phillip Wolfram, Earl Lawrence + 1 more
'Nathan M. Urban'] With the increasing computational power of current supercomputers, the size of data produced by scientific simulations is rapidly growing. To reduce the storage footprint and facilitate scalable post-hoc analyses of such scientific data sets, various data reduction/summarization methods have been…
W. Jeffrey Johnston, Stefano Fusi
Humans and other animals demonstrate a remarkable ability to generalize knowledge across distinct contexts and objects during natural behavior. We posit that this ability to generalize arises from a specific representational geometry, that we call abstract and that is referred to as disentangled in machine learning.…
V. Yu. Korolev, Alexander Zeifman
In the paper, multivariate probability distributions are considered that are representable as scale mixtures of multivariate elliptically contoured stable distributions. It is demonstrated that these distributions form a special subclass of scale mixtures of multivariate elliptically contoured normal distributions.…
Authors not listed
The analysis of nonadiabatic molecular dynamics (NAMD) data presents significant challenges due to its high dimensionality and complexity. To address these issues, we introduce ULaMDyn, a Python-based, open-source package designed to automate the unsupervised analysis of large datasets generated by NAMD simulations.…
Yuanqing Lu, Timur Fazletdinov, Zhiwen Pan, Katrin Wondraczek + 1 more
The synthesis of nanoscale particles and particle aggregates from liquid or gaseous precursors is affected by a variety of trade-off relations, for example, in terms of product composition, yield, or energy efficiency. Machine-supported process evaluation and learning (ML) of these relations enables optimization…
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…
Wojciech Michno, Patrick Wehrli, Srinivas Koutarapu, Christian Marsching + 9 more
Understanding of Alzheimer’s disease (AD) pathophysiology, requires molecular assessment of how key pathological factors, specifically amyloid β (Aβ) plaques, influence the surrounding microenvironment. Here, neuronal lipids are particularly of interest as these are implicated in pathological- and neurodegenerative…
Authors not listed
Machine learning is increasingly used to predict reaction properties such as barrier heights, reaction energies, rates, or yields, as well as the underlying molecular geometries, including transition state structures. While such predictions have the potential to provide mechanistic insight for high-impact applications…
Jose L. Medina-Franco, Edgar López-López, Johny R. Rodríguez-Pérez, Héctor F. Cortés-Hernández + 1 more
In Chemoinformatics, as in many other computational-related disciplines, it is a common practice to identify the “single best” approach or methodology, for instance, identify the best fingerprint representation, the best single virtual screening approach or protocol, the optimal representation of the chemical space…
Edgar López-López, José L. Medina-Franco
Drug-induced liver injury (DILI) is the principal reason for failure in developing drug candidates. It is the most common reason to withdraw from the market after a drug has been approved for clinical use. Therefore, a current challenge is enhancing the accuracy of DILI events' predictive models. In this context, data…
Stephanie Wankowicz, James Fraser
In their folded state, biomolecules exchange between multiple conformational states, crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules more accurately, we must move towards modeling and predicting…