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Nonlinear integration across the spa...

Seyed Boloori, Alireza.

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Nonlinear integration across the spatiotemporal receptive-field: Tactile feature-selectivity of neurons in the primary somatosensory cortex.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Nonlinear integration across the spatiotemporal receptive-field: Tactile feature-selectivity of neurons in the primary somatosensory cortex./
Author:	Seyed Boloori, Alireza.
Description:	149 p.
Notes:	Adviser: Garrett B. Stanley.
Contained By:	Dissertation Abstracts International68-02B.
Subject:	Biology, Neuroscience. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3251312

Nonlinear integration across the spatiotemporal receptive-field: Tactile feature-selectivity of neurons in the primary somatosensory cortex.
Seyed Boloori, Alireza.

Nonlinear integration across the spatiotemporal receptive-field: Tactile feature-selectivity of neurons in the primary somatosensory cortex. - 149 p.

Adviser: Garrett B. Stanley.

Thesis (Ph.D.)--Harvard University, 2007.

As organisms, our perceptions of the sensory world are mediated through neural activity at multiple stages within our brains. Broadly speaking, sensory neuroscience deals with two main lines of questioning: the encoding process quantifies how features of a sensory stimulus cause sequences of action-potentials evoked by a neuron, which are stereotyped fluctuations of its membrane potential. In contrast, in decoding we ask how to obtain an optimal estimate of a sensory stimulus through observations of neural action potentials.Subjects--Topical Terms:

1017680
Biology, Neuroscience.

Nonlinear integration across the spatiotemporal receptive-field: Tactile feature-selectivity of neurons in the primary somatosensory cortex.
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008 110921s2007 eng d
035 $a (UMI)AAI3251312
035 $a AAI3251312
040 $a UMI $c UMI
100 1 $a Seyed Boloori, Alireza. $3 1293465
245 1 0 $a Nonlinear integration across the spatiotemporal receptive-field: Tactile feature-selectivity of neurons in the primary somatosensory cortex.
300 $a 149 p.
500 $a Adviser: Garrett B. Stanley.
500 $a Source: Dissertation Abstracts International, Volume: 68-02, Section: B, page: 0803.
502 $a Thesis (Ph.D.)--Harvard University, 2007.
520 $a As organisms, our perceptions of the sensory world are mediated through neural activity at multiple stages within our brains. Broadly speaking, sensory neuroscience deals with two main lines of questioning: the encoding process quantifies how features of a sensory stimulus cause sequences of action-potentials evoked by a neuron, which are stereotyped fluctuations of its membrane potential. In contrast, in decoding we ask how to obtain an optimal estimate of a sensory stimulus through observations of neural action potentials.
520 $a We used the rat whisker (vibrissa) pathway, a high-acuity tactile sensory system, as an experimental model with which to answer both of these questions. During in-vivo experiments with anesthetized animals, we recorded single-neuron activity in the layer-IV of the primary somatosensory cortex (S1) in response to controlled deflections of one or two vibrissa.
520 $a Characterization of the encoding pathway involved two steps; firstly, we showed that S1 neurons encode deflection transients through phasic increases in their firing rates. Increases in the deflection angular velocity led to corresponding increases in magnitude, shortening of latency, and slight increases in the temporal precision of the response. Secondly, we showed that neural responses were strongly shaped by the timescale of suppression evoked by the neural pathway. The nonlinear dynamics of response suppression were predictable from simpler measurements made in the laboratory. We subsequently combined velocity-tuning and the history-dependence of S1 responses to create a Markov response model. This model, a novel contribution, accurately predicted measured responses to deflection patterns inspired by the velocity and temporal structures of naturalistic stimuli.
520 $a We subsequently used this model to (1) optimally detect neural responses, and (2) compute estimates of the sensory stimulus using a Bayesian decoding framework. Despite the significant role of response dynamics in shaping the activity evoked by different kinematic and behavioral parameters; texture-specific information were recoverable by an ideal-observer of the neural response. Together, these results characterize important principles by which a tactile sensory pathway encodes stimuli, and identify the factors that limit the amount of recoverable sensory information. The paradigm developed here is sufficiently general to be applicable to other sensory pathways.
590 $a School code: 0084.
650 4 $a Biology, Neuroscience. $3 1017680
650 4 $a Engineering, General. $3 1020744
690 $a 0317
690 $a 0537
710 2 0 $a Harvard University. $3 528741
773 0 $t Dissertation Abstracts International $g 68-02B.
790 $a 0084
790 1 0 $a Stanley, Garrett B., $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3251312