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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Related Experiment Video

Updated: Aug 23, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Bioinspired and Low-Power 2D Machine Vision with Adaptive Machine Learning and Forgetting.

Akhil Dodda1, Darsith Jayachandran1, Shiva Subbulakshmi Radhakrishnan1

  • 1Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States.

ACS Nano
|October 28, 2022
PubMed
Summary

This study presents a bioinspired machine vision system using molybdenum disulfide (MoS2) that mimics natural intelligence for efficient learning and adaptation. This novel hardware platform integrates sensing, computing, and storage, significantly reducing energy consumption for artificial intelligence (AI).

Keywords:
Two-dimensional materialsbioinspiredlow-power sensorsmachine visionneuromorphicphototransistor

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Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Neuroscience

Background:

  • Natural intelligence excels at environmental learning and adaptation, with vision playing a key role in primates.
  • Biological neural networks achieve energy-efficient learning and adaptation, incorporating forgetting as a crucial element.
  • Current artificial intelligence (AI) faces a significant energy gap compared to biological intelligence.

Purpose of the Study:

  • To develop a bioinspired machine vision system that mimics neurobiological mechanisms for enhanced AI.
  • To bridge the energy efficiency gap between AI and biological intelligence.
  • To create an "all-in-one" hardware vision platform combining sensing, computing, and storage.

Main Methods:

  • Fabrication of a 2D phototransistor array using large-area monolayer molybdenum disulfide (MoS2).
  • Integration with an analog, nonvolatile, and programmable memory gate-stack.
  • Demonstration of dynamic learning, relearning, and adaptability under noisy illumination.

Main Results:

  • The system exhibits dynamic learning and relearning capabilities from visual stimuli.
  • Achieved learning adaptability under noisy illumination conditions with minimal energy expenditure.
  • The "all-in-one" platform overcomes the von Neumann bottleneck and eliminates the need for peripheral circuits.

Conclusions:

  • Bioinspired machine vision systems can accelerate AI development by mimicking biological learning and forgetting.
  • The demonstrated MoS2-based system offers a highly energy-efficient and adaptable solution for artificial vision.
  • This integrated hardware platform represents a significant advancement beyond conventional complementary metal-oxide-semiconductor (CMOS) technology.