Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Optimal control of neuronal activity.

Jianfeng Feng1, Henry C Tuckwell

  • 1Department of Informatics, Sussex University, Brighton BN1 9QH, United Kingdom.

Physical Review Letters
|August 9, 2003
PubMed
Summary

This study finds optimal control strategies for neuronal spiking activity under random synaptic inputs. The optimal control method depends on the input statistics, ranging from precise delta functions to smooth signals.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cross-subject fMRI-to-Image with Visual-cortex 2D Representation and Pre-Training.

IEEE journal of biomedical and health informatics·2026
Same author

Shifts in the brain sex continuum in major depressive disorder: Evidence for a persistent neurobiological marker.

Journal of affective disorders·2026
Same author

The complement C3-microglial axis in depression of Parkinson's disease: from mechanism to therapeutic intervention.

EBioMedicine·2026
Same author

Sex differences in activations to the sight of faces, scenes, body parts and tools in visual and non-visual cortical regions leading to the human hippocampus.

Biology of sex differences·2026
Same author

A hierarchical multi-scale framework for schizophrenia: integrating symptom networks, functional circuits, and molecular pathways.

Molecular psychiatry·2026
Same author

Latent neural architecture organising shared aesthetic evaluations of visual artworks.

Nature communications·2026

Area of Science:

  • Computational neuroscience
  • Stochastic processes
  • Neural dynamics

Background:

  • Neuronal spiking activity is fundamental to brain function.
  • Understanding and controlling neural responses to synaptic inputs is crucial.
  • Existing models often simplify the complexity of synaptic input statistics.

Purpose of the Study:

  • To determine optimal control signals for neuronal spiking.
  • To analyze how synaptic input characteristics (alpha parameter) influence optimal control.
  • To establish theoretical bounds for controlling neural stochasticity.

Main Methods:

  • Mathematical modeling of neuronal activity using a diffusion process approximation.
  • Analysis of an integrate-and-fire neuron model.
  • Derivation of optimal control signals and variances based on input statistics (alpha).

Main Results:

  • Optimal control strategies vary with synaptic input statistics (alpha).
  • Sub-Poisson inputs (alpha<0.5) yield non-unique delta function controls (bang-bang).
  • Supra-Poisson inputs (alpha>0.5) result in unique, smooth optimal control signals.

Conclusions:

  • The optimal control strategy for neuronal spiking is input-dependent.
  • This research provides a framework for minimizing neural stochasticity.
  • Implementation strategies for optimal control in model neurons are discussed.

Related Experiment Videos