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Olfaction01:25

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The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...
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Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms
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A Multimodal Humidity Adaptive Optical Neuron Based on a MoWS2/VOx Heterojunction for Vision and Respiratory

Abdul Momin Syed1, Dhananjay D Kumbhar1, Hanrui Li1

  • 1Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.

Advanced Materials (Deerfield Beach, Fla.)
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Summary
This summary is machine-generated.

A new multimodal in-memory computing system uses a novel memristor to process multisensory signals efficiently. This bio-inspired approach enables faster, low-power computing and shows potential for artificial sensory neurons and healthcare applications.

Keywords:
crossbar arrayheterojunctionhigh on/off ratiohumidityin‐memory computingmemristormultimodalvanadium oxide

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Computing has evolved towards in-sensor processing for enhanced efficiency.
  • Multimodal in-memory computing performs computations within memory devices, reducing data transfer.
  • Conventional chips face limitations in data conversion and transmission for multisensory signals.

Purpose of the Study:

  • To introduce a bio-inspired multimodal in-memory computing system for real-time, low-power processing of multisensory signals.
  • To develop a novel multimodal memristor capable of emulating synaptic behavior.
  • To explore the potential of this system in healthcare applications like respiratory detection.

Main Methods:

  • Fabrication and characterization of a novel Cu/MoWS2/VOx/Pt based multimodal memristor.
  • Demonstration of electrical, photonic, and humidity-mediated synaptic learning.
  • Integration of humidity sensing with a convolutional neural network (CNN) for vision clarity enhancement.

Main Results:

  • The memristor exhibited a high ON/OFF ratio (10^8) at ultralow operating voltages (±0.2V).
  • Tunable conductance modulation was achieved through humidity changes, emulating biological synaptic transmission.
  • Successful demonstration of humidity sensing for respiratory detection and data augmentation in CNNs.

Conclusions:

  • The developed multimodal memristor offers a novel platform for artificial sensory neurons.
  • This technology enables efficient, low-power processing of multisensory data.
  • Significant implications for non-contact human-computer interaction and intelligent systems, including healthcare.