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Related Concept Videos

Neural Circuits01:25

Neural Circuits

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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 Role of Ion Channels in Neuronal Computation01:19

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A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
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Emerging Artificial Neuron Devices for Probabilistic Computing.

Zong-Xiao Li1,2, Xiao-Ying Geng1,3, Jingrui Wang1,4

  • 1Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China.

Frontiers in Neuroscience
|August 23, 2021
PubMed
Summary
This summary is machine-generated.

Emerging stochastic artificial neurons (SANs) offer inherent randomness, overcoming silicon limitations for more brain-like artificial intelligence. These novel devices pave the way for advanced probabilistic computing.

Keywords:
artificial neuronsbrain-inspired computingmemristive devicesstochastic electronicsstochastic neurons

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Area of Science:

  • Neuroscience and Artificial Intelligence
  • Materials Science for Computing

Background:

  • Artificial intelligence (AI) has advanced in various sectors, but mimicking human brain functions like imagination and inference remains challenging.
  • Silicon-based probabilistic computing, using restricted Boltzmann machines and Bayesian inference, struggles to replicate the true stochasticity of biological neurons due to artificial noise.
  • Biological neurons exhibit inherent noise crucial for high-level cognitive functions.

Purpose of the Study:

  • To review recent advancements in emerging stochastic artificial neurons (SANs) for probabilistic computing.
  • To compare the capabilities of silicon-based neurons with novel emerging devices.
  • To discuss the potential of SANs in making machine learning more akin to the human brain.

Main Methods:

  • Review of biological neurons, traditional neuron models, and silicon neurons.
  • Detailed examination of the working mechanisms of various emerging stochastic artificial neurons (SANs).
  • Comparative analysis of the advantages and disadvantages of silicon-based versus emerging neuron technologies.

Main Results:

  • Emerging devices like memristors and ferroelectric field-effect transistors exhibit inherent stochasticity.
  • These devices can generate uncertain non-linear output spikes, mimicking biological neuron behavior.
  • SANs show promise in achieving true stochasticity, a key challenge for current silicon electronics.

Conclusions:

  • Stochastic artificial neurons based on emerging devices are a promising avenue for advancing probabilistic computing.
  • SANs offer a potential pathway to bridge the gap between current AI and the cognitive capabilities of the human brain.
  • Further research into SANs is crucial for developing next-generation AI systems.