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Related Concept Videos

Sensation01:21

Sensation

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Sensory receptors are specialized neurons that respond to specific types of external stimuli, initiating the process known as sensation. This occurs when sensory input, such as light entering the eye, is detected by these receptors, causing chemical changes in the cells of the retina. These cells then convert the sensory stimulus into action potentials that are transmitted to the central nervous system, a process termed transduction.
Absolute thresholds can quantify the sensitivity of sensory...
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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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Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...
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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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Somatosensation

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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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Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
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Maximal Dependence Capturing as a Principle of Sensory Processing.

Rishabh Raj1, Dar Dahlen1, Kyle Duyck1

  • 1Stowers Institute for Medical Research, Kansas City, MO, United States.

Frontiers in Computational Neuroscience
|April 11, 2022
PubMed
Summary

The brain achieves object recognition despite noisy sensory input by using maximal dependence capturing (MDC). This new principle explains how neurons create consistent object representations without deep learning or prior exposure to corrupted data.

Keywords:
computational modelinggrandmother cellinvariant representationobject recognition (OR)redundancy capturingredundancy reductionsparse codingsparse recovery (SR)

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • The brain processes noisy and incomplete sensory information to recognize objects consistently.
  • Current hierarchical models struggle to achieve invariant object representations despite accurately capturing input structures.
  • Existing frameworks face theoretical and experimental inconsistencies in explaining robust object recognition.

Purpose of the Study:

  • To propose a new principle for neural object encoding that overcomes limitations of current frameworks.
  • To introduce the Maximal Dependence Capturing (MDC) principle for creating invariant object representations.
  • To provide a unifying principle for sensory processing and neural computation.

Main Methods:

  • Developed a computational framework based on dimension expansion and sparse coding.
  • Implemented the Maximal Dependence Capturing (MDC) principle where neurons capture information-rich structural components.
  • Assessed the framework's ability to achieve consistent object representations under various noisy conditions.

Main Results:

  • The proposed framework achieves consistent object identity representations even with occlusion, corruption, or high noise.
  • The Maximal Dependence Capturing (MDC) principle enables robust object recognition without learning corrupted forms or using deep networks.
  • The framework successfully explains various observed receptive field properties of neurons.

Conclusions:

  • Maximal Dependence Capturing (MDC) offers a unifying principle for sensory processing and neural object representation.
  • This principle provides a more effective mechanism for achieving invariant object recognition compared to traditional hierarchical models.
  • The computational framework demonstrates the feasibility of MDC in explaining neural robustness to sensory variability.