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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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Spiking neural P systems with thresholds.

Xiangxiang Zeng1, Xingyi Zhang, Tao Song

  • 1Department of Computer Science, Xiamen University, Xiamen 361005, Fujian, China xzeng@xmu.edu.cn.

Neural Computation
|April 9, 2014
PubMed
Summary
This summary is machine-generated.

Spiking neural P systems with thresholds (SNPT systems) were introduced, offering new computational models. The firing mechanism significantly impacts their computation power, distinguishing Turing computable and semilinear sets.

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Area of Science:

  • * Computational Intelligence
  • * Theoretical Computer Science
  • * Biologically Inspired Computing

Background:

  • * Spiking neural P systems (SNPS) are distributed computing models inspired by biological neurons.
  • * Traditional SNPS models feature neurons firing only when their potential equals a threshold.
  • * This study introduces spiking neural P systems with thresholds (SNPT systems) where neurons fire at or above a threshold.

Discussion:

  • * Two SNPT system variants were analyzed based on their neuron firing potential consumption mechanisms.
  • * The first variant features potential consumption upon firing, dependent on the applied rule.
  • * The second variant resets neuron potential to zero after firing.

Key Insights:

  • * SNPT systems with potential consumption upon firing can compute all Turing computable sets.
  • * SNPT systems with potential reset after firing characterize semilinear sets.
  • * The neuron firing mechanism critically influences the computational power of SNPT systems.

Outlook:

  • * This research resolves an open problem concerning the computational capabilities of SNPS variants.
  • * Further investigation into the impact of neuron firing dynamics on computational models is warranted.
  • * Exploring applications of SNPT systems in complex computational tasks could be a future direction.