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Related Concept Videos

Neural Circuits01:25

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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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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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Visual System01:26

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
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Biologically grounded neocortex computational primitives implemented on neuromorphic hardware improve vision

Asim Iqbal1, Hassan Mahmood1, Greg J Stuart2,3

  • 1Tibbling Technologies, Seattle, WA 98052-5727.

Proceedings of the National Academy of Sciences of the United States of America
|October 7, 2025
PubMed
Summary

Researchers created a biophysically realistic model of brain circuits to develop neuro-inspired AI (NeuroAI). This model, a soft winner-take-all (sWTA) circuit, improved AI performance and generalization in tasks like image classification.

Keywords:
IBM’s TrueNorth neuromorphic chipNeuroAIbiophysics of neocortical computationbrain-inspired computingwinner-take-all

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Neuroscience

Background:

  • Advancing neuro-inspired AI (NeuroAI) requires understanding brain computation and implementing it in hardware and deep learning.
  • Neocortical microcircuits, particularly in the mouse primary visual cortex, utilize diverse interneuron classes for complex dynamics.

Purpose of the Study:

  • To model neocortical microcircuits using an experimentally constrained, biophysically realistic approach.
  • To investigate the role of four interneuron classes (Parvalbumin, Somatostatin, vasoactive intestinal peptide, LAMP5) in implementing soft winner-take-all (sWTA) circuit dynamics.
  • To bridge biological computation with neuromorphic hardware and deep learning architectures.

Main Methods:

  • Developed a conductance-based network model of mouse visual cortex layers 2-3, grounded in in vitro physiology.
  • Implemented a competitive-cooperative motif to achieve sWTA dynamics, enabling selective input amplification and suppression.
  • Mapped the sWTA motif onto IBM's TrueNorth neuromorphic chip using a gain-matching strategy.
  • Integrated the sWTA circuit as a preprocessing filter within a Vision Transformer architecture.

Main Results:

  • The sWTA circuit demonstrated gain modulation, signal restoration, and context-dependent multistability.
  • Neuromorphic implementation revealed correspondences between cell-type roles and hardware primitives.
  • Sparse sWTA modules facilitated persistent up-states and working memory approximation.
  • Embedding the sWTA filter in a Vision Transformer enhanced out-of-distribution generalization by up to 20% and reduced training compute.

Conclusions:

  • The study provides a roadmap for integrating biophysically grounded cortical computation into NeuroAI systems.
  • The sWTA circuit serves as a key motif for enhancing AI performance and efficiency.
  • This work demonstrates the potential of combining neuroscience principles with AI for practical advancements.