Search research articles

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

Spaces and sequences in the hippocampus: a homological perspective.

Journal of computational neuroscience·2026

Same author

Spaces and sequences in the hippocampus: a homological perspective.

bioRxiv : the preprint server for biology·2025

Same author

Theta oscillons in behaving rats.

ArXiv·2024

Same author

Theta oscillons in behaving rats.

bioRxiv : the preprint server for biology·2024

Same author

Altered patterning of neural activity in a tauopathy mouse model.

bioRxiv : the preprint server for biology·2024

Same author

Discrete Structure of the Brain Rhythms.

Scientific reports·2019

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Oct 15, 2025

Recording Spatially Restricted Oscillations in the Hippocampus of Behaving Mice

Recording Spatially Restricted Oscillations in the Hippocampus of Behaving Mice

Published on: July 1, 2018

Spatial representability of neuronal activity.

D Akhtiamov¹, A G Cohn^2,3,4,5,6, Y Dabaghian⁷

¹Einstein institute of Mathematics, The Hebrew University, Jerusalem, 9190401, Israel.

Scientific Reports

|October 26, 2021

Summary

This study introduces a theoretical framework using topological data analysis to predict neuronal firing fields, moving beyond empirical discovery. This approach offers a consistent method for understanding neural representations of space.

More Related Videos

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

Neural Activity Propagation in an Unfolded Hippocampal Preparation with a Penetrating Micro-electrode Array

Neural Activity Propagation in an Unfolded Hippocampal Preparation with a Penetrating Micro-electrode Array

Published on: March 27, 2015

Related Experiment Videos

Last Updated: Oct 15, 2025

Recording Spatially Restricted Oscillations in the Hippocampus of Behaving Mice

Recording Spatially Restricted Oscillations in the Hippocampus of Behaving Mice

Published on: July 1, 2018

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

Neural Activity Propagation in an Unfolded Hippocampal Preparation with a Penetrating Micro-electrode Array

Neural Activity Propagation in an Unfolded Hippocampal Preparation with a Penetrating Micro-electrode Array

Published on: March 27, 2015

Area of Science:

Computational Neuroscience
Neuroscience
Topology

Background:

Neuronal firing fields, such as those in hippocampal place cells, are typically discovered empirically.
These fields represent regions in space or configuration spaces that elicit neural responses.
Existing methods rely on trial-and-error discovery, lacking a theoretical foundation.

Purpose of the Study:

To theoretically establish the existence and properties of neuronal firing fields.
To develop a consistent framework for interpreting neural spiking activity.
To verify neuronal responses against a coherent spatial representation.

Main Methods:

Utilizing topological data analysis of neuronal spiking activity.
Applying the Leray criterion powered by persistent homology theory.
Employing Eckhoff conditions and Region Connection Calculus.

Main Results:

Demonstrated theoretical prediction of neuronal firing fields.
Provided a method to verify consistency of neural responses with spatial representations.
Established a link between topological features and neural activity patterns.

Conclusions:

Topological analysis offers a powerful theoretical approach to understanding neuronal firing fields.
This method moves beyond empirical discovery, providing a more robust interpretation of neural data.
The framework supports a unified understanding of how neurons represent space.