FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2893456507> ?p ?o ?g. }

• W2893456507 endingPage "1102" @default.
• W2893456507 startingPage "1091" @default.
• W2893456507 abstract "Recent advances in analytic methods and high-channel count recordings have raised the possibility of reading out cognitive processes directly from the brain, as opposed to inferring cognitive processes indirectly from behavior. Decoding neural activity has been used to understand decision making by using place cell activity in the hippocampus or value-selective neural responses in orbitofrontal cortex. Decoding could have broad applications for measuring other cognitive processes directly from neural activity, such as attention, working memory and reasoning. An intrinsic difficulty in studying cognitive processes is that they are unobservable states that exist in between observable responses to the sensory environment. Cognitive states must be inferred from indirect behavioral measures. Neuroscience potentially provides the tools necessary to measure cognitive processes directly, but it is challenged on two fronts. First, neuroscientific measures often lack the spatiotemporal resolution to identify the neural computations that underlie a cognitive process. Second, the activity of a single neuron, which is the fundamental building block of neural computation, is too noisy to provide accurate measurements of a cognitive process. In this paper, I examine recent developments in neurophysiological recording and analysis methods that provide a potential solution to these problems. An intrinsic difficulty in studying cognitive processes is that they are unobservable states that exist in between observable responses to the sensory environment. Cognitive states must be inferred from indirect behavioral measures. Neuroscience potentially provides the tools necessary to measure cognitive processes directly, but it is challenged on two fronts. First, neuroscientific measures often lack the spatiotemporal resolution to identify the neural computations that underlie a cognitive process. Second, the activity of a single neuron, which is the fundamental building block of neural computation, is too noisy to provide accurate measurements of a cognitive process. In this paper, I examine recent developments in neurophysiological recording and analysis methods that provide a potential solution to these problems. in a dynamical system, the attractor state or attractor basin, is the subset of the state space towards which objects will tend to move, irrespective of the starting conditions of the object. mathematical tool for finding repeating patterns. It is the correlation between serial observations as a function of the time lag between them. electrical potential recorded from an electrode positioned in neural tissue that reflects the summed electrical activity within approximately 0.5–1 mm of the electrode. It includes both action potentials and subthreshold membrane potentials. spatial location that causes a place neuron to fire whenever the animal is at that location. large-amplitude, short-duration deflections in the local field potential, which are only found in the hippocampus and its neighboring areas. rhythmic oscillation between 4 and 10 Hz that is present in the local field potential throughout the brain. It is particularly prominent in the hippocampus." @default.
• W2893456507 created "2018-10-05" @default.
• W2893456507 creator A5054673367 @default.
• W2893456507 date "2018-12-01" @default.
• W2893456507 modified "2023-10-17" @default.
• W2893456507 title "Decoding Cognitive Processes from Neural Ensembles" @default.
• W2893456507 cites W1497613136 @default.
• W2893456507 cites W1594303435 @default.
• W2893456507 cites W1930034491 @default.
• W2893456507 cites W1971581718 @default.
• W2893456507 cites W1975958074 @default.
• W2893456507 cites W1977592558 @default.
• W2893456507 cites W1978838256 @default.
• W2893456507 cites W1992769122 @default.
• W2893456507 cites W2001488773 @default.
• W2893456507 cites W2005066437 @default.
• W2893456507 cites W2006760951 @default.
• W2893456507 cites W2010396996 @default.
• W2893456507 cites W2014966184 @default.
• W2893456507 cites W2022561239 @default.
• W2893456507 cites W2026148432 @default.
• W2893456507 cites W2032608262 @default.
• W2893456507 cites W2041248708 @default.
• W2893456507 cites W2048142524 @default.
• W2893456507 cites W2049430110 @default.
• W2893456507 cites W2053011627 @default.
• W2893456507 cites W2055642260 @default.
• W2893456507 cites W2058978266 @default.
• W2893456507 cites W2063303712 @default.
• W2893456507 cites W2066835186 @default.
• W2893456507 cites W2066923652 @default.
• W2893456507 cites W2071392547 @default.
• W2893456507 cites W2077707797 @default.
• W2893456507 cites W2081413820 @default.
• W2893456507 cites W2083537735 @default.
• W2893456507 cites W2087704839 @default.
• W2893456507 cites W2093967694 @default.
• W2893456507 cites W2099925882 @default.
• W2893456507 cites W2102726432 @default.
• W2893456507 cites W2112180451 @default.
• W2893456507 cites W2112191288 @default.
• W2893456507 cites W2115107366 @default.
• W2893456507 cites W2117726420 @default.
• W2893456507 cites W2118280967 @default.
• W2893456507 cites W2118615399 @default.
• W2893456507 cites W2126116397 @default.
• W2893456507 cites W2126810579 @default.
• W2893456507 cites W2128472904 @default.
• W2893456507 cites W2135993484 @default.
• W2893456507 cites W2143997647 @default.
• W2893456507 cites W2147479622 @default.
• W2893456507 cites W2149565728 @default.
• W2893456507 cites W2150025966 @default.
• W2893456507 cites W2152848035 @default.
• W2893456507 cites W2153894094 @default.
• W2893456507 cites W2161046428 @default.
• W2893456507 cites W2166091710 @default.
• W2893456507 cites W2167362547 @default.
• W2893456507 cites W2170288531 @default.
• W2893456507 cites W2171367914 @default.
• W2893456507 cites W2192800751 @default.
• W2893456507 cites W2202916324 @default.
• W2893456507 cites W2243428647 @default.
• W2893456507 cites W2262450756 @default.
• W2893456507 cites W2284856356 @default.
• W2893456507 cites W2413302958 @default.
• W2893456507 cites W2419461529 @default.
• W2893456507 cites W2527741908 @default.
• W2893456507 cites W2528108497 @default.
• W2893456507 cites W2594475619 @default.
• W2893456507 cites W2619199014 @default.
• W2893456507 cites W2623012790 @default.
• W2893456507 cites W2731341786 @default.
• W2893456507 cites W2745165449 @default.
• W2893456507 cites W2750722402 @default.
• W2893456507 cites W2767493192 @default.
• W2893456507 cites W2769583531 @default.
• W2893456507 cites W2801534303 @default.
• W2893456507 doi "https://doi.org/10.1016/j.tics.2018.09.002" @default.
• W2893456507 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6240384" @default.
• W2893456507 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30279136" @default.
• W2893456507 hasPublicationYear "2018" @default.
• W2893456507 type Work @default.
• W2893456507 sameAs 2893456507 @default.
• W2893456507 citedByCount "27" @default.
• W2893456507 countsByYear W28934565072018 @default.
• W2893456507 countsByYear W28934565072019 @default.
• W2893456507 countsByYear W28934565072020 @default.
• W2893456507 countsByYear W28934565072021 @default.
• W2893456507 countsByYear W28934565072022 @default.
• W2893456507 countsByYear W28934565072023 @default.
• W2893456507 crossrefType "journal-article" @default.
• W2893456507 hasAuthorship W2893456507A5054673367 @default.
• W2893456507 hasBestOaLocation W28934565072 @default.
• W2893456507 hasConcept C111472728 @default.
• W2893456507 hasConcept C138885662 @default.
• W2893456507 hasConcept C15744967 @default.
• W2893456507 hasConcept C169760540 @default.

• next