30 papers · ranked by Valyu relevance
Lorenzo Posani
Neural decoding is a powerful approach for inferring which variables are represented in the activity of a population of neurons, with broad applications ranging from basic neuroscience to clinical settings such as brain-computer interfaces. More recently, decoding has also been used as a cross-validated tool for…
Joshua I. Glaser, Ari S. Benjamin, Raeed H. Chowdhury, Matthew G. Perich + 2 more
'Matthew G. Perich' 'Lee E. Miller' 'Konrad P. Kording'] Title: Abstract Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve decoding…
Ethan M. Meyers
Neural decoding is a powerful method to analyze neural activity. However, the code needed to run a decoding analysis can be complex, which can present a barrier to using the method. In this paper we introduce a package that makes it easy to perform decoding analyses in the R programing language. We describe how the…
Benjamin I. Rapoport, Lorenzo Turicchia, Woradorn Wattanapanitch, Thomas J. Davidson + 2 more
'Thomas J. Davidson' 'Rahul Sarpeshkar' 'Michal Zochowski'] The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain- machine interfaces (BMIs). Such systems require input from tens to hundreds of brain-implanted recording…
Ethan M. Meyers
Neural decoding is a powerful method to analyze neural activity. However, the code needed to run a decoding analysis can be complex, which can present a barrier to using the method. In this paper we introduce a package that makes it easy to perform decoding analyses in the R programing language. We describe how the…
Joshua I. Glaser, Ari S. Benjamin, Raeed H. Chowdhury, Matthew G. Perich + 2 more
'Matthew G. Perich' 'Lee E. Miller' 'Konrad P. Körding'] - 1. Interdepartmental Neuroscience Program, Northwestern University, Chicago, IL, USA - 2. Department of Physical Medicine and Rehabilitation, Northwestern University and Shirley Ryan Ability Lab, Chicago, IL, USA - 3. Department of Physiology, Northwestern…
Nima Maleki, Hamid Karimi-Rouzbahani
Sensory neural coding, the brain’s process of transforming inputs into informative patterns of neural activity, generates complex and multiplexed neural codes which are hard to interpret. Although decoding methods have facilitated the interpretation of these codes, the specific features of neural activity that…
Jesse A. Livezey, Joshua I. Glaser
Decoding behavior, perception, or cognitive state directly from neural signals has applications in brain-computer interface research as well as implications for systems neuroscience. In the last decade, deep learning has become the state-of-the-art method in many machine learning tasks ranging from speech recognition…
C. Daniel Greenidge, Benjamin Scholl, Jacob L. Yates, Jonathan W. Pillow
Neural decoding methods provide a powerful tool for quantifying the information content of neural population codes and the limits imposed by correlations in neural activity. However, standard decoding methods are prone to overfitting and scale poorly to high-dimensional settings. Here, we introduce a novel decoding…
Markus Frey, Sander Tanni, Catherine Perrodin, Alice O’Leary + 6 more
Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data often depends on manual operations and requires considerable knowledge about the nature of the representation. Decoding provides a…
Markus Frey, Sander Tanni, Catherine Perrodin, Alice O'Leary + 9 more
Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a…
Yağmur Güçlütürk, Umut Güçlü, Katja Seeliger, Sander Bosch + 2 more
'Rob van Lier' 'Marcel van Gerven'] Here, we present a novel approach to solve the problem of reconstructing perceived stimuli from brain responses by combining probabilistic inference with deep learning. Our approach first inverts the linear transformation from latent features to brain responses with maximum a…
Seungbin Park, Megan Lipton, Maria C. Dadarlat
Two-photon imaging has been a critical tool for dissecting brain circuits and understanding brain function. However, relating slow two-photon calcium imaging data to fast behaviors has been challenging due to relatively low imaging sampling rates, thus limiting potential applications to neural prostheses. Here, we show…
Célia Loriette, Julian L. Amengual, Suliann Ben Hamed
One of the major challenges in system neurosciences consists in developing techniques for estimating the cognitive information content in brain activity. This has an enormous potential in different domains spanning from clinical applications, cognitive enhancement to a better understanding of the neural bases of…
Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Alyson K. Fletcher + 5 more
'Alyson K. Fletcher' 'Sundeep Rangan' 'Michael Trumpis' 'Brinnae Bent' 'Chia‐Han Chiang' 'Jonathan Viventi'] This paper considers the problem of neural decoding from parallel neural measurements systems such as micro-electrocorticography (µ-ECoG). In systems with large numbers of array elements at very high sampling…
Mingkang Li, Ruixue Wang, Guihua Wan, Yuqi Yang + 1 more
Calcium imaging has gained extensive application in neural decoding tasks because of its high precision in observing cortical neural activity. Nevertheless, the immense data volume and complexity of automated signal extraction algorithms in calcium imaging result in significant delays in extracting neuronal calcium…
Nikolaus Kriegeskorte, Pamela K. Douglas
Encoding and decoding models are widely used in systems, cognitive, and computational neuroscience to make sense of brain-activity data. However, the interpretation of their results requires care. Decoding models can help reveal whether particular information is present in a brain region in a format the decoder can…
Vicente Botella‐Soler, Stéphane Deny, Olivier Marre, Gašper Tkačik
Retinal circuitry transforms spatiotemporal patterns of light into spiking activity of ganglion cells, which provide the sole visual input to the brain. Recent advances have led to a detailed characterization of retinal activity and stimulus encoding by large neural populations. The inverse problem of decoding, where…
Shahrzad Latifi, Jonathan Chang, Mehdi Pedram, Roshanak Latifikhereshki + 1 more
Neuronal networks in the motor cortex are crucial for driving complex movements. Yet it remains unclear whether distinct neuronal populations in motor cortical subregions encode complex movements. Using in vivo two-photon calcium imaging (2P) on head- fixed grid-walking animals, we tracked the activity of excitatory…
Ye Chen, Peter Beech, Ziwei Yin, Shanshan Jia + 4 more
'Zhaofei Yu' 'Jian K. Liu' 'Daniele Marinazzo'] Understanding the computational mechanisms that underlie the encoding and decoding of environmental stimuli is a crucial investigation in neuroscience. Central to this pursuit is the exploration of how the brain represents visual information across its hierarchical…
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…
Merse E Gáspár, Pierre-Olivier Polack, Peyman Golshani, Máté Lengyel + 3 more
'Gergő Orbán' 'Nicole Rust' 'Joshua I Gold'] An important computational goal of the visual system is ‘representational untangling’ (RU): representing increasingly complex features of visual scenes in an easily decodable format. RU is typically assumed to be achieved in high-level visual cortices via several stages of…
Tagir Akhmetshin, Arkadii Lin, Timur Madzhidov, Alexandre Varnek
Autoencoders represent a promising technique for the inverse quantitative structure-activity relationship (QSAR) task. However, undesirable bias, such as atom ordering, affects the neighbourhood behaviour of autoencoders’ latent space and, consequently, usage of the latent vectors as variables in machine-learning…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Authors not listed
Electrospray ionization (ESI) mass spectrometry is an essential technique for chemical analysis in a range of fields. In ESI, analytes can produce multiple charge states, which must be correctly assigned for identification. Existing approaches to charge state assignment can suffer from limited accuracy and/or poor…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…
Hongyang Dong, Simon D.M. Jacques, Winfried Kockelmann, Stephen W. T. Price + 10 more
Hongyang Dong 3 , Simon D.M. Jacques 1 , Winfried Kockelmann 4 , Stephen W. T. Price 1 , Robert Emberson 5 , Dorota Matras 6,7 , Yaroslav Odarchenko 1 , Vesna Middelkoop 10 , Athanasios Giokaris 1 , Olof Gutowski 8 , Ann-Christin Dippel 8 , Martin v. Zimmermann 8 , Andrew M. Beale 3 , Keith T. Butler 9 , Antonis…
Authors not listed
X-ray diffraction (XRD) is an immediate and powerful characterization technique that provides detailed information on the lattice structure and long-range order in crystalline materials. In recent decades, the quality and quantity of available crystal structure data has exploded, in large part due to the advent of…