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 Concept Videos

The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.5K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
3.5K
Neuronal Communication01:28

Neuronal Communication

2.4K
Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
2.4K
Integration of Synaptic Events01:28

Integration of Synaptic Events

3.0K
Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
3.0K
Action Potential01:14

Action Potential

10.1K
Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...
10.1K
Electrochemical Gradient and Channel Proteins: An Overview01:21

Electrochemical Gradient and Channel Proteins: An Overview

3.9K
An electrochemical gradient is a fundamental concept in biology and chemistry. It regulates the movement of ions across cell membranes. This movement is influenced by two factors:
The electrical gradient: The electrical gradient across cell membranes refers to the difference in electric charge between the inside and outside of a cell.  This difference drives the movement of ions towards or away from the cells. For instance, if the inside of the cell is more negatively charged relative to...
3.9K
Electrical Synapses01:28

Electrical Synapses

9.7K
Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
9.7K

You might also read

Related Articles

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

Sort by
Same author

THOI: An efficient and accessible library for computing higher-order interactions enhanced by batch-processing.

PloS one·2026
Same author

Convergent transcriptomic and connectomic controllers of information integration and its anaesthetic breakdown across mammalian brains.

Nature human behaviour·2026
Same author

Transcranial direct current stimulation modulates primate brain dynamics across states of consciousness.

eLife·2025
Same author

A computational approach to evaluate how molecular mechanisms impact large-scale brain activity.

Nature computational science·2025
Same author

Comprehensive profiling of anaesthetised brain dynamics across phylogeny.

bioRxiv : the preprint server for biology·2025
Same author

Benchmarking macaque brain gene expression for horizontal and vertical translation.

Science advances·2025

Related Experiment Video

Updated: Nov 27, 2025

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

11.8K

Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics.

Rodrigo Cofré1, Cesar Maldonado2, Bruno Cessac3

  • 1CIMFAV-Ingemat, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2340000, Chile.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

Thermodynamic Formalism, a mathematical framework, uses principles like Maximum Entropy for statistical inference in complex systems. It offers a powerful tool for theoretical neuroscience to connect neural dynamics with action potential statistics.

Keywords:
Thermodynamic Formalismergodic theoryfree energy and pressurelarge deviationslinear responsemaximum entropy principleneuronal networks dynamics

More Related Videos

Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses
08:34

Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses

Published on: May 9, 2021

2.9K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.6K

Related Experiment Videos

Last Updated: Nov 27, 2025

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

11.8K
Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses
08:34

Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses

Published on: May 9, 2021

2.9K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.6K

Area of Science:

  • Theoretical Neuroscience
  • Statistical Physics
  • Complex Systems

Background:

  • Thermodynamic Formalism offers a mathematical framework for dynamical systems.
  • It utilizes variational principles, akin to Maximum Entropy, for statistical inference.
  • Gibbs measures are employed to represent collective behavior in complex systems.

Purpose of the Study:

  • To review the application of Thermodynamic Formalism in theoretical neuroscience.
  • To explore its utility in linking neuronal dynamics and action potential statistics.
  • To identify perspectives and open problems addressable by this framework.

Main Methods:

  • Application of Thermodynamic Formalism principles.
  • Statistical analysis of neuronal dynamics and action potentials.
  • Integration of experimental data and mathematical models.

Main Results:

  • Demonstration of Thermodynamic Formalism as a conceptual and operational tool.
  • Facilitation of the connection between neural dynamics and spike train statistics.
  • Identification of a robust framework for analyzing complex neural systems.

Conclusions:

  • Thermodynamic Formalism provides a powerful approach for theoretical neuroscience.
  • It enables a deeper understanding of the relationship between neuronal activity and system-level behavior.
  • Future research can leverage this formalism to address outstanding questions in neuroscience.