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Related Concept Videos

Sensory Perception: Organization of the Somatosensory System01:11

Sensory Perception: Organization of the Somatosensory System

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:
The receptor level is the first stage of sensation. It involves the detection of a stimulus by specialized sensory receptors. The stimulus must arrive within the receptor's receptive field. Next, the receptor converts the energy of the stimulus...
Microbial Biosensors01:17

Microbial Biosensors

Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...
Somatosensation01:33

Somatosensation

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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Related Experiment Video

Updated: May 22, 2026

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors
09:59

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors

Published on: June 23, 2018

Neuromorphic Near-Sensor and In-Sensor Computing Enabled by Next-Generation Material-Based Sensors.

Su Yeon Jung1,2, Gwang Ya Kim1, Sejin Kim1

  • 1Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, Republic of Korea.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|May 21, 2026
PubMed
Summary
This summary is machine-generated.

Neuromorphic sensory platforms offer energy-efficient, low-latency processing for real-time environmental data. This review explores near-sensor and in-sensor computing architectures for artificial sensory systems.

Keywords:
artificial sensory systemin‐sensor computingmultisensory perceptionnear‐sensor computingneuromorphic sensor

Related Experiment Videos

Last Updated: May 22, 2026

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors
09:59

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors

Published on: June 23, 2018

Area of Science:

  • Neuroscience and Materials Science
  • Artificial Intelligence and Sensor Technology

Background:

  • Conventional artificial sensory systems face challenges with data transfer overhead and energy inefficiency.
  • Neuromorphic platforms, inspired by biological systems, offer a novel solution for efficient sensory processing.
  • The massive influx of real-time environmental data necessitates advancements in sensory computing.

Purpose of the Study:

  • To comprehensively review the structural evolution and research trends of neuromorphic near-sensor and in-sensor computing.
  • To analyze the fundamental physical mechanisms of artificial neurons and synapses.
  • To discuss sensor operating principles and categorize neuromorphic systems for sensory applications.

Main Methods:

  • Systematic analysis of physical mechanisms for artificial neurons and synapses.
  • Discussion of optical, mechanical, and chemical sensor principles.
  • Categorization of neuromorphic systems into near-sensor and in-sensor computing architectures.
  • Review of research trends tailored to the five human senses.

Main Results:

  • Neuromorphic near-sensor and in-sensor computing architectures are defined and analyzed.
  • The review covers fundamental mechanisms and sensor types relevant to artificial senses.
  • Current research trends in neuromorphic sensory computing are discussed for various applications.
  • Domain-specific bottlenecks are identified for future development.

Conclusions:

  • Neuromorphic computing presents a promising paradigm for energy-efficient, low-latency sensory data processing.
  • Near-sensor and in-sensor architectures offer distinct advantages in data handling and integration.
  • Strategic guidelines are provided for developing next-generation, fully integrated artificial cognitive systems.