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Related Concept Videos

The Role of Ion Channels in Neuronal Computation01:19

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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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Fractional-Order Spiking Bayesian Neural Model for Cognitive Computations.

Vikas Arya1, Daya Krishan Lobiyal2

  • 1School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110067, India. vikas28_scs@jnu.ac.in.

Neuroinformatics
|May 7, 2026
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Summary

This study introduces fractional-order neuron models for biologically plausible Bayesian inference in lifespan prediction. These models enhance cognitive predictions and show strong cortical plausibility.

Keywords:
Bayesian frameworkFractal calculusNeural engineering objectsNeuronal dynamicsProbabilistic computations

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Area of Science:

  • Computational Neuroscience
  • Biophysics
  • Cognitive Science

Background:

  • Bayesian inference offers a framework for cognition under uncertainty.
  • Implementing Bayesian models with biologically plausible neural networks is challenging.
  • Existing models lack long-term memory crucial for complex cognitive tasks.

Purpose of the Study:

  • To develop a biologically plausible Bayesian neural model for lifespan prediction.
  • To integrate fractional-order dynamics into spiking neuron models (Leaky Integrate-and-Fire, Izhikevich).
  • To investigate the impact of neural population size on model performance and biological plausibility.

Main Methods:

  • Integration of fractional derivatives into Leaky Integrate-and-Fire and Izhikevich neuron models.
  • Development of a Bayesian neural model for lifespan prediction using demographic priors.
  • Systematic increase in neural population size to assess effects on prediction accuracy and cortical plausibility.
  • Comparison of model predictions against human and optimal Bayesian predictions.

Main Results:

  • Fractional-order neuron models demonstrated closer alignment with human and optimal Bayesian predictions.
  • The large-scale fractional-order Izhikevich model exhibited robust convergence and high cortical plausibility.
  • Increased neural population size improved biological plausibility and Bayesian optimality.
  • Fractional dynamics enhance representational capacity and introduce necessary long-term memory.

Conclusions:

  • Fractal neural dynamics play a key role in probabilistic cognition.
  • Biologically inspired spiking neuron models can effectively approximate Bayesian inference.
  • This approach bridges theoretical Bayesian models with realistic neural computation, offering pathways for efficient learning, prediction, and adaptation.