The Experts below are selected from a list of 152652 Experts worldwide ranked by ideXlab platform
Paul H E Tiesinga - One of the best experts on this subject based on the ideXlab platform.
-
finding the Event Structure of neuronal spike trains
Neural Computation, 2011Co-Authors: Vincent J Toups, Jeanmarc Fellous, Peter J Thomas, Terrence J Sejnowski, Paul H E TiesingaAbstract:Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed Events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times (Fellous, Tiesinga, Thomas, & Sejnowski, 2004). Finding Events in data with realistic spiking statistics is challenging because Events belonging to different spike patterns may overlap. We propose a method for finding spiking Events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near-synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.
-
finding the Event Structure of neuronal spike trains
BMC Neuroscience, 2011Co-Authors: Vincent J Toups, Jeanmarc Fellous, Peter J Thomas, Terrence J Sejnowski, Paul H E TiesingaAbstract:Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed Events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times [1]. Finding Events in data with realistic spiking statistics is challenging because Events belonging to different spike patterns may overlap. We propose a method for finding spiking Events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.
Fey Parrill - One of the best experts on this subject based on the ideXlab platform.
-
viewpoint in speech gesture integration linguistic Structure discourse Structure and Event Structure
Language and Cognitive Processes, 2010Co-Authors: Fey ParrillAbstract:We examine a corpus of narrative data to determine which types of Events evoke character viewpoint gestures, and which evoke observer viewpoint gestures. We consider early claims made by McNeill (1992) that character viewpoint tends to occur with transitive utterances and utterances that are causally central to the narrative. We argue that the Structure of the Event itself must be taken into account: there are some Events that cannot plausibly evoke both types of gesture. We show that linguistic Structure (transitivity), Event Structure (visuo-spatial and motoric properties), and discourse Structure all play a role. We apply these findings to a recent model of embodied language production, the Gestures as Simulated Action framework.
-
Viewpoint in speech–gesture integration: Linguistic Structure, discourse Structure, and Event Structure
Language and Cognitive Processes, 2010Co-Authors: Fey ParrillAbstract:We examine a corpus of narrative data to determine which types of Events evoke character viewpoint gestures, and which evoke observer viewpoint gestures. We consider early claims made by McNeill (1992) that character viewpoint tends to occur with transitive utterances and utterances that are causally central to the narrative. We argue that the Structure of the Event itself must be taken into account: there are some Events that cannot plausibly evoke both types of gesture. We show that linguistic Structure (transitivity), Event Structure (visuo-spatial and motoric properties), and discourse Structure all play a role. We apply these findings to a recent model of embodied language production, the Gestures as Simulated Action framework.
Terrence J Sejnowski - One of the best experts on this subject based on the ideXlab platform.
-
finding the Event Structure of neuronal spike trains
Neural Computation, 2011Co-Authors: Vincent J Toups, Jeanmarc Fellous, Peter J Thomas, Terrence J Sejnowski, Paul H E TiesingaAbstract:Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed Events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times (Fellous, Tiesinga, Thomas, & Sejnowski, 2004). Finding Events in data with realistic spiking statistics is challenging because Events belonging to different spike patterns may overlap. We propose a method for finding spiking Events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near-synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.
-
finding the Event Structure of neuronal spike trains
BMC Neuroscience, 2011Co-Authors: Vincent J Toups, Jeanmarc Fellous, Peter J Thomas, Terrence J Sejnowski, Paul H E TiesingaAbstract:Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed Events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times [1]. Finding Events in data with realistic spiking statistics is challenging because Events belonging to different spike patterns may overlap. We propose a method for finding spiking Events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.
Peter J Thomas - One of the best experts on this subject based on the ideXlab platform.
-
finding the Event Structure of neuronal spike trains
Neural Computation, 2011Co-Authors: Vincent J Toups, Jeanmarc Fellous, Peter J Thomas, Terrence J Sejnowski, Paul H E TiesingaAbstract:Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed Events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times (Fellous, Tiesinga, Thomas, & Sejnowski, 2004). Finding Events in data with realistic spiking statistics is challenging because Events belonging to different spike patterns may overlap. We propose a method for finding spiking Events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near-synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.
-
finding the Event Structure of neuronal spike trains
BMC Neuroscience, 2011Co-Authors: Vincent J Toups, Jeanmarc Fellous, Peter J Thomas, Terrence J Sejnowski, Paul H E TiesingaAbstract:Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed Events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times [1]. Finding Events in data with realistic spiking statistics is challenging because Events belonging to different spike patterns may overlap. We propose a method for finding spiking Events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.
Vincent J Toups - One of the best experts on this subject based on the ideXlab platform.
-
finding the Event Structure of neuronal spike trains
Neural Computation, 2011Co-Authors: Vincent J Toups, Jeanmarc Fellous, Peter J Thomas, Terrence J Sejnowski, Paul H E TiesingaAbstract:Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed Events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times (Fellous, Tiesinga, Thomas, & Sejnowski, 2004). Finding Events in data with realistic spiking statistics is challenging because Events belonging to different spike patterns may overlap. We propose a method for finding spiking Events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near-synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.
-
finding the Event Structure of neuronal spike trains
BMC Neuroscience, 2011Co-Authors: Vincent J Toups, Jeanmarc Fellous, Peter J Thomas, Terrence J Sejnowski, Paul H E TiesingaAbstract:Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed Events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times [1]. Finding Events in data with realistic spiking statistics is challenging because Events belonging to different spike patterns may overlap. We propose a method for finding spiking Events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.