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Related Concept Videos

Parallel Processing01:20

Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Sensation typically is the process by which the sensory receptors and sense organs detect stimuli from the internal and external environment and transmit this information to the central nervous system for processing.
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Sensory Perception: Organization of the Somatosensory System01:11

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The somatosensory system is the central and peripheral nervous system component that senses and processes touch, pressure, pain, temperature, and body position or proprioception. The process of sensation takes place at three levels:
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Motor and Sensory Areas of the Cortex01:14

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The cerebral cortex, the brain's outermost layer, is pivotal in processing complex cognitive tasks, emotions, and various sensory inputs and executing voluntary motor activities. This intricate structure is divided into three primary functional areas: the motor areas, sensory areas, and association areas.
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Somatosensation01:33

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The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.
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Vision01:24

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
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Attention modeled as information in learning multisensory integration.

Johannes Bauer1, Sven Magg1, Stefan Wermter1

  • 1University of Hamburg, Department of Informatics, Knowledge Technology, WTM, Vogt-Kölln-Straße 30, 22527 Hamburg, Germany.

Neural Networks : the Official Journal of the International Neural Network Society
|February 18, 2015
PubMed
Summary
This summary is machine-generated.

This study shows that basic learning mechanisms can explain how the brain integrates sensory information and how attention influences this process. The model demonstrates self-organized learning of cross-modal stimuli.

Keywords:
AttentionMultisensory integrationSelf-organizationSuperior colliculus

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Area of Science:

  • Computational Neuroscience
  • Cognitive Neuroscience

Background:

  • Top-down cognitive processes modulate bottom-up sensory integration.
  • The superior colliculus (SC) is a key brain region for multisensory integration.

Purpose of the Study:

  • To extend a neural network model of SC multisensory integration with top-down attentional input.
  • To investigate if basic learning mechanisms can explain multisensory integration and attentional modulation.

Main Methods:

  • Developed a neural network model incorporating attentional input.
  • Trained the model on various cross-modal stimuli.
  • Utilized self-organized learning of input statistics and divisive normalization.

Main Results:

  • The model successfully learned to integrate cross-modal stimuli.
  • Reproduces neurons specializing in different sensory combinations.
  • Captures behavioral and neurophysiological aspects of spatial and feature-based attention.

Conclusions:

  • Self-organized learning and divisive normalization mechanisms are sufficient to explain bottom-up multisensory integration.
  • These mechanisms also account for the top-down influence of attention on multisensory integration in the SC.