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

Low-dimensional maps encoding dynamics in entorhinal cortex and hippocampus.

Dmitri D Pervouchine1, Theoden I Netoff, Horacio G Rotstein

  • 1Department of Mathematics and Statistics and Center for BioDynamics, Boston University, Boston, MA 02215, USA. dp@math.bu.edu

Neural Computation
|September 27, 2006
PubMed
Summary
This summary is machine-generated.

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

Gamma suppression correlates with thalamic stimulation therapeutic response in intractable epilepsy.

Epilepsia·2026
Same author

A computationally efficient adaptive phase response curve estimator for real-time closed-loop neuromodulation.

Journal of neural engineering·2026
Same author

Novel examples of NMD escape through alternative intronic polyadenylation.

NAR genomics and bioinformatics·2026
Same author

Distinct cell-type contributions and network topography of theta-nested gamma oscillations in the medial entorhinal cortex.

bioRxiv : the preprint server for biology·2026
Same author

Metric validation for detection of delayed and directed coupling.

Journal of neural engineering·2026
Same author

Cellular mechanisms of entorhinal cortex spontaneous and navigation-related grid cell oscillations.

Reviews in the neurosciences·2026
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

The hyperpolarization-activated current I(h) influences network synchronization. Fast-spiking interneurons can desynchronize stellate cells but synchronize O-LM cells, depending on I(h) properties.

Area of Science:

  • Computational Neuroscience
  • Neurophysiology

Background:

  • Cells generating theta oscillations often possess the hyperpolarization-activated current I(h).
  • Theta oscillations are crucial for cognitive functions like memory and navigation.
  • Understanding neuronal network synchronization is key to deciphering brain function.

Purpose of the Study:

  • To investigate the synchronization properties of stellate and O-LM cells in networks with fast-spiking (FS) interneurons.
  • To elucidate the critical role of the hyperpolarization-activated current I(h) in mediating these synchronization effects.
  • To analyze the impact of FS interneurons on the network dynamics of excitatory stellate cells and inhibitory O-LM cells.

Main Methods:

  • Utilized computational models and dynamic clamp experiments.

Related Experiment Videos

  • Employed spike time response curve (STRC) methods, extended to three-cell networks.
  • Investigated both deterministic systems and the effects of noise on phase relationships.

    • Main Results:

    • Fast-spiking (FS) interneurons desynchronized stellate cells (entorhinal cortex) but synchronized O-LM cells (hippocampus).
    • These synchronization and desynchronization effects were critically dependent on the hyperpolarization-activated current I(h).
    • Dynamic clamp experiments with biophysical stellate cells and in silico FS cells corroborated the analytical framework.

    Conclusions:

    • The presence and properties of I(h) are pivotal in determining how FS interneurons modulate network synchronization.
    • Network structure and cell-specific properties (excitation vs. inhibition, synaptic kinetics) dictate synchronization outcomes.
    • The study provides a framework for understanding how specific neuronal populations and their intrinsic properties contribute to network oscillations.