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Related Concept Videos

Muscle Stimulation Frequency01:22

Muscle Stimulation Frequency

The contraction strength of muscles is regulated by motor neurons, which modulate the frequency of action potentials dispatched to the motor units based on the body's requirements. This process of varying the muscle stimulation frequency allows muscles to contract with a force that is precisely tailored to the needs of the moment, whether lifting a feather or a heavy box.
Wave summation
At low firing rates, motor neurons induce individual twitch contractions in muscle fibers. These twitches...
Action Potential: Phases of Stimulation01:28

Action Potential: Phases of Stimulation

The action potential is a complex electrical event that occurs in excitable cells, such as neurons and muscle cells. It consists of several distinct phases, each with specific characteristics.
Resting Phase:
In this phase, the cell's membrane is at its resting potential, typically around -70 millivolts (mV) for neurons. Inside the cell, there is a higher concentration of potassium ions (K+) and a lower concentration of sodium ions (Na+). Voltage-gated sodium channels are closed, and...
Integration of Synaptic Events01:28

Integration of Synaptic Events

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...
Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
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...

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Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
07:33

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Published on: June 29, 2018

Event-based minimum-time control of oscillatory neuron models: phase randomization, maximal spike rate increase, and

Per Danzl1, João Hespanha, Jeff Moehlis

  • 1Department of Mechanical Engineering, University of California, Santa Barbara, CA 93106, USA. pdanzl@engineering.ucsb.edu

Biological Cybernetics
|November 14, 2009
PubMed
Summary

We developed a novel feedback control method to randomize the phase of neurons, enhancing their response to noise. This technique also optimizes spike rate and desynchronizes neural populations for better network function.

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Control Theory

Background:

  • Oscillatory neurons are fundamental to neural function.
  • Controlling neural phase is crucial for understanding and manipulating network dynamics.
  • Existing methods for phase control are limited, especially under biological constraints.

Purpose of the Study:

  • To develop an event-based feedback control method for randomizing neuronal asymptotic phase.
  • To achieve phase randomization with a fixed stimulus magnitude constraint in minimum time.
  • To extend the control method for optimizing spike rate and desynchronizing neural populations.

Main Methods:

  • Utilized an event-based feedback control strategy.
  • Employed the minimum-time-optimal Hamilton-Jacobi-Bellman framework for control synthesis.
  • Applied the method to conductance-based Hodgkin-Huxley neuron models.
  • Extended the scheme to networks of globally coupled neurons.

Main Results:

  • Successfully demonstrated phase randomization by driving neurons to their phaseless set.
  • Achieved minimum-time control under fixed stimulus magnitude constraints.
  • Computed optimal feedback control for increased spike rate.
  • Visualized isochrons without traditional calculation.
  • Showed effective desynchronization of globally coupled neural networks.

Conclusions:

  • The developed Hamilton-Jacobi-Bellman framework offers a generalizable method for controlling spiking neuron dynamics.
  • The phase randomization technique enhances neuronal sensitivity to noise and can be applied to network desynchronization.
  • This approach provides novel tools for investigating and manipulating neural network behavior.