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Related Concept Videos

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.
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...
Neuronal Communication01:28

Neuronal Communication

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...
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...
Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
There are two types of receptors: ionotropic and metabotropic.
The ionotropic receptor is the membrane protein that has an...
Overview of Synapses01:25

Overview of Synapses

A synapse is a specialized structure where two neurons connect, allowing them to pass an electrical or chemical signal to another neuron. It is the point of communication between neurons. The term "synapse" is derived from the Greek word "synapsis," which means "conjunction." The entire process of neural communication revolves around the synapse. When activated, a neuron releases chemicals known as neurotransmitters into the synapse. These neurotransmitters cross the synapse and bind to...

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

Published on: November 12, 2019

Overview of facts and issues about neural coding by spikes.

Bruno Cessac1, Hélène Paugam-Moisy, Thierry Viéville

  • 1LJAD, Parc de Valrose, Nice, France. bruno.cessac@inln.cnrs.fr

Journal of Physiology, Paris
|November 21, 2009
PubMed
Summary

This study clarifies spike coding in deterministic spiking neuron networks, offering insights into biological plausibility and computational efficiency. Understanding temporal constraints is crucial for accurate modeling and efficient implementation of these neural networks.

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Last Updated: Jun 18, 2026

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

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Published on: March 2, 2015

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

Area of Science:

  • Computational Neuroscience
  • Neural Networks
  • Spike Coding

Background:

  • Spike-timing is a fundamental aspect of neural coding, yet its computational implications remain complex.
  • Spiking neuron networks (SNNs) offer a biologically plausible model for neural computation.
  • Understanding the efficiency and constraints of SNNs is key for both simulation and computation.

Purpose of the Study:

  • To demystify spike coding by reviewing technical facts related to spike timing.
  • To assess the biological plausibility and computational efficiency of modeling with SNNs.
  • To provide a deterministic framework for analyzing SNN dynamics and parameter adjustment.

Main Methods:

  • Focused on a deterministic implementation of SNNs, defining network dynamics via non-stochastic mapping.
  • Analyzed general time constraints within SNNs.
  • Investigated the relationship between continuous signals and spike trains, and SNN parameter adjustment.

Main Results:

  • Derived formulas and concrete numerical values for critical temporal variables in realistic spike trains.
  • Provided a numerical evaluation of factors governing spike train progression.
  • Demonstrated that implementing large-scale SNNs can be a straightforward task within this framework.

Conclusions:

  • Adherence to temporal constraints is vital for meaningful SNN implementation, preventing biologically implausible mechanisms.
  • Continuous calculations may be more suitable than artificial spike generation in certain scenarios.
  • The deterministic framework simplifies the understanding and implementation of SNNs for simulation and computation.