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Related Concept Videos

Perception01:28

Perception

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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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Parallel Processing01:20

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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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Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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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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Related Experiment Video

Updated: Jan 11, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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Exploiting neuro-inspired dynamic sparsity for energy-efficient intelligent perception.

Sheng Zhou1, Chang Gao2, Tobi Delbruck1

  • 1Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.

Nature Communications
|November 11, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) models face high energy costs. This perspective explores brain-inspired dynamic sparsity to create more energy-efficient AI for perception tasks.

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Deep neural networks (DNNs) enable efficient online processing of sensory signals at the edge.
  • Growing DNN size leads to escalating computational costs and energy consumption, hindering deployment and upscaling.

Purpose of the Study:

  • To present a neuro-inspired vision for boosting AI energy efficiency in perception.
  • To leverage brain-like dynamic sparsity to address computational and energy challenges in AI.

Main Methods:

  • Categorizing various forms of dynamic sparsity rooted in data redundancy.
  • Discussing strategies for enhancing and exploiting dynamic sparsity through algorithm-hardware co-design.

Main Results:

  • Dynamic sparsity offers a promising approach to reduce energy consumption in AI perception.
  • Algorithm-hardware co-design is crucial for effectively implementing sparsity-aware AI.

Conclusions:

  • Neuro-inspired dynamic sparsity can significantly improve the energy efficiency of AI for perception.
  • Addressing technological, architectural, and algorithmic challenges is key to realizing the full potential of this approach.