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Related Concept Videos

Graded Potential01:19

Graded Potential

Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or calcium...
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The Resting Membrane Potential01:21

The Resting Membrane Potential

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Resting Membrane Potential01:24

Resting Membrane Potential

The relative difference in electrical charge, or voltage, between the inside and the outside of a cell membrane, is called the membrane potential. It is generated by differences in permeability of the membrane to various ions and the concentrations of these ions across the membrane.
The Inside of a Neuron is More Negative
The membrane potential of a cell can be measured by inserting a microelectrode into a cell and comparing the charge to a reference electrode in the extracellular fluid. The...
Action Potential01:14

Action Potential

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...
Action Potential01:14

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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...

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A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Estimation of time-dependent input from neuronal membrane potential.

Ryota Kobayashi1, Shigeru Shinomoto, Petr Lansky

  • 1Department of Human and Computer Intelligence, Ritsumeikan University, Shiga, Japan. kobayashi@cns.ci.ritsumei.ac.jp

Neural Computation
|September 17, 2011
PubMed
Summary

This study presents a new method to estimate input firing rates in neurons using membrane potential data. The developed algorithm reliably estimates both continuous and discontinuous input signals, even with sudden changes.

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Biophysics

Background:

  • Neuronal input signals are crucial for brain function.
  • Estimating these signals from recorded membrane potential is challenging.
  • Common models like the Ornstein-Uhlenbeck process are used for neuron dynamics.

Purpose of the Study:

  • To develop a method for estimating time-varying input firing rates from intracellularly recorded membrane potential.
  • To utilize the Ornstein-Uhlenbeck stochastic process for modeling neuron dynamics.
  • To provide a way to infer unmeasurable input firing rates.

Main Methods:

  • Formulated the estimation problem as a state-space model assuming slow variation of moments.
  • Developed an empirical Bayes algorithm to estimate the mean and variance of input current.
  • Applied the method to simulated data with constant, sinusoidal, and jump signals.

Main Results:

  • The method accurately estimates input firing rates from membrane potential.
  • Performance is comparable to maximum likelihood for constant signals.
  • The algorithm successfully estimates continuous and discontinuous signals, demonstrating robustness with jumps.

Conclusions:

  • The proposed empirical Bayes method reliably estimates total input firing rates.
  • This technique offers a valuable tool for analyzing neuronal input signals.
  • The method is robust and applicable to various signal types, including those with discontinuities.