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Related Concept Videos

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...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

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.
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...

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Related Experiment Video

Updated: May 20, 2026

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

Predicting single-neuron activity in locally connected networks.

Feraz Azhar1, William S Anderson

  • 1Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. fazhar1@jhmi.edu

Neural Computation
|August 1, 2012
PubMed
Summary
This summary is machine-generated.

Neuronal populations exhibit coordinated activity, allowing prediction of single-neuron spikes using ensemble spiking histories. This finding suggests broader implications for understanding locally connected neural networks.

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A Computer-assisted Multi-electrode Patch-clamp System
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Area of Science:

  • Computational neuroscience
  • Systems neuroscience
  • Neural coding

Background:

  • Advancing experimental techniques enable simultaneous multi-unit recordings, renewing interest in neuronal population activity.
  • Nearby neurons demonstrate coordinated responses during spontaneous activity and external stimulation.
  • Prior research links coordinated neuronal activity in sensorimotor cortex to behavioral prediction.

Purpose of the Study:

  • To investigate the predictability of single-neuron activity using ensemble spiking histories in a computational model.
  • To explore whether coordinated activity in neural networks can predict future neuronal states.
  • To examine this phenomenon within a computational model of cortical architecture.

Main Methods:

  • Utilized a point process model analogous to experimental observations.
  • Simulated a computational model of cortex with realistic architecture and random background inputs.
  • Analyzed spiking histories of single neurons and randomly sampled ensembles of nearby neurons.

Main Results:

  • The computational model successfully predicted the future state of single neurons.
  • Predictability was achieved by incorporating the neuron's own spiking history and those of nearby ensembles.
  • The model exhibited bursting episodes under two distinct connectivity schemes.

Conclusions:

  • Coordinated activity in neuronal populations, characterized by ensemble spiking histories, can predict individual neuron behavior.
  • The findings suggest that baseline predictability is a characteristic feature of locally connected neural networks.
  • This study provides computational support for the role of local network interactions in neural coding.