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Related Concept Videos

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:
The receptor level:
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Parallel Processing01:20

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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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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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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Somatosensory, Motor, and Association Cortex01:23

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The somatosensory cortex in the parietal lobes is crucial for interpreting sensory data such as touch, temperature, and proprioception. The somatosensory cortex, situated in the parietal lobes, plays a vital role in interpreting sensory information like touch, temperature, and proprioception—awareness of body position. This specialized brain region features an organized structure wherein neurons at the top primarily process sensations originating from the lower body. In contrast, those at...
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Sensory receptors play an integral part in comprehending our external and internal environments. They receive diverse stimuli, converting them into the nervous system's electrochemical signals. This conversion occurs as the stimulus alters the sensory neuron's cell membrane potential, instigating the generation of an action potential. This action potential is subsequently transmitted to the central nervous system (CNS), which integrates with other sensory data or higher cognitive...
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Computational principles and models of multisensory integration.

Chandramouli Chandrasekaran1

  • 1Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.

Current Opinion in Neurobiology
|December 6, 2016
PubMed
Summary

Multisensory integration combines information from various senses, enhancing perception and response speed. Computational models and neural network analyses reveal how the brain achieves this complex process.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Multisensory integration is crucial for robust perception, faster responses, and improved learning.
  • Understanding the neural and computational mechanisms underlying multisensory integration is a key challenge in neuroscience.

Purpose of the Study:

  • To review computational models and principles of multisensory integration at behavioral and neural levels.
  • To highlight evidence for distributed neural networks and heterogeneous population coding in multisensory processing.

Main Methods:

  • Discussion of drift-diffusion and Bayesian models for predicting multisensory behavior.
  • Review of neurophysiological and perturbation experiments.
  • Description of dimensionality reduction and recurrent neural network models.

Main Results:

  • Drift-diffusion and Bayesian models effectively predict behavior in multisensory contexts.
  • Evidence supports a distributed, redundant neural network for multisensory integration.
  • Task-relevant variables are encoded in heterogeneous neural populations.

Conclusions:

  • Computational models provide valuable insights into multisensory integration.
  • Multisensory integration involves complex, distributed neural computations.
  • Advanced methods are needed to decipher heterogeneous neural population dynamics.