Mind reading and writing: the future of neurotechnology
Recent advances in neuroscience and technology have made it possible to record from
large assemblies of neurons and to decode their activity to extract information. At the same …
large assemblies of neurons and to decode their activity to extract information. At the same …
[HTML][HTML] An explainable machine learning model to predict and elucidate the compressive behavior of high-performance concrete
Abstract Machine Learning (ML) has made significant progress in several fields, and
materials science is no exception. ML models are popular in the materials science …
materials science is no exception. ML models are popular in the materials science …
Quantifying behavior to solve sensorimotor transformations: advances from worms and flies
AJ Calhoun, M Murthy - Current opinion in neurobiology, 2017 - Elsevier
Highlights•New automated methods permit characterization of the full repertoire of an
animal's behavior.•Quantification of dynamic sensory stimuli in combination with behavioral …
animal's behavior.•Quantification of dynamic sensory stimuli in combination with behavioral …
Improving healthcare operations management with machine learning
OS Pianykh, S Guitron, D Parke, C Zhang… - Nature Machine …, 2020 - nature.com
Healthcare institutions need modern and powerful technology to provide high-quality, cost-
effective care to patients. However, despite the considerable progress in the computerization …
effective care to patients. However, despite the considerable progress in the computerization …
Encoding and decoding neuronal dynamics: Methodological framework to uncover the algorithms of cognition
JR King, A Gramfort - 2018 - hal.science
A central challenge to cognitive neuroscience consists in decomposing complex brain
signals into an interpretable sequence of operations-an algorithm-which ultimately accounts …
signals into an interpretable sequence of operations-an algorithm-which ultimately accounts …
Omitted variable bias in GLMs of neural spiking activity
IH Stevenson - Neural computation, 2018 - direct.mit.edu
Generalized linear models (GLMs) have a wide range of applications in systems
neuroscience describing the encoding of stimulus and behavioral variables, as well as the …
neuroscience describing the encoding of stimulus and behavioral variables, as well as the …
Brain-state invariant thalamo-cortical coordination revealed by non-linear encoders
G Viejo, T Cortier, A Peyrache - PLoS computational biology, 2018 - journals.plos.org
Understanding how neurons cooperate to integrate sensory inputs and guide behavior is a
fundamental problem in neuroscience. A large body of methods have been developed to …
fundamental problem in neuroscience. A large body of methods have been developed to …
[PDF][PDF] Targeted comodulation supports flexible and accurate decoding in V1
Sensory-guided behavior requires reliable encoding of stimulus information in neural
responses, and task-specific decoding through selective combination of these responses …
responses, and task-specific decoding through selective combination of these responses …
Targeted V1 comodulation supports task-adaptive sensory decisions
Sensory-guided behavior requires reliable encoding of stimulus information in neural
populations, and flexible, task-specific readout. The former has been studied extensively, but …
populations, and flexible, task-specific readout. The former has been studied extensively, but …
Learning and inferring representations of data in neural networks
J Livezey - 2017 - escholarship.org
Finding useful representations of data in order to facilitate scientific knowledge generation is
a ubiquitous concept across disciplines. Until the development of machine learning and …
a ubiquitous concept across disciplines. Until the development of machine learning and …