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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...
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.
Ligand-Gated Ion Channel Receptor: Gating Mechanism01:30

Ligand-Gated Ion Channel Receptor: Gating Mechanism

Ligand-gated ion channels are transmembrane proteins that play a vital role in intercellular communication and functions of the nervous system. They allow the influx of ions across the membrane once the neurotransmitter binds, allowing the subsequent transmission of electrical excitation across the neurons. Other ligand-gated ion channels, like the γ-aminobutyric acid (GABA) receptor, permit anions like chloride into the cells on the binding of the GABA molecule. Their entry into the cell...

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Related Experiment Video

Updated: Jul 2, 2026

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
08:48

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

Orientation-selective aVLSI spiking neurons.

S C Liu1, J Kramer, G Indiveri

  • 1Institute of Neuroinformatics, ETH/UNIZ, Zurich, Switzerland.

Neural Networks : the Official Journal of the International Neural Network Society
|October 23, 2001
PubMed
Summary
This summary is machine-generated.

We developed a novel multi-chip system for exploring spike-based neural processing. This real-time hardware system efficiently models neuronal networks, outperforming traditional computer simulations.

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Last Updated: Jul 2, 2026

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
08:48

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

A Guide to In vivo Single-unit Recording from Optogenetically Identified Cortical Inhibitory Interneurons
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Published on: November 7, 2014

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Area of Science:

  • Neuroscience
  • Computer Engineering
  • Artificial Intelligence

Background:

  • Spike-based information processing models are crucial for understanding neural computation.
  • Existing computational models often face limitations in real-time processing and scalability.

Purpose of the Study:

  • To introduce a programmable multi-chip Very Large Scale Integration (VLSI) neuronal system.
  • To explore spike-based information processing models using novel hardware.

Main Methods:

  • Integration of a silicon retina, a PIC microcontroller, and a transceiver chip.
  • Implementation of integrate-and-fire neurons in a soft winner-take-all architecture.
  • Configurable neuronal properties via virtual connections and an event-driven communication protocol.

Main Results:

  • The system successfully synthesized orientation-tuned neurons using feedforward and feedback models.
  • Analog hardware spiking model performance closely matched experimental observations and digital simulations.
  • Demonstrated real-time computational capabilities independent of network size.

Conclusions:

  • The developed multi-chip VLSI system offers a powerful platform for real-time spike-based neural computation.
  • This hardware approach provides advantages over traditional computer models in speed and scalability.
  • The system effectively approximates cortical microcircuits for advanced neuroscience research.