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Related Concept Videos

Chemical Synapses01:26

Chemical Synapses

Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...
Chemical Synapses01:26

Chemical Synapses

Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...
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...
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...
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...
The Synapse02:47

The Synapse

Neurons communicate with one another by passing on their electrical signals to other neurons. A synapse is the location where two neurons meet to exchange signals. At the synapse, the neuron that sends the signal is called the presynaptic cell, while the neuron that receives the message is called the postsynaptic cell. Note that most neurons can be both presynaptic and postsynaptic, as they both transmit and receive information.

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Related Experiment Video

Updated: May 16, 2026

Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo
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Published on: March 31, 2016

Effects of synaptic connectivity on liquid state machine performance.

Han Ju1, Jian-Xin Xu, Edmund Chong

  • 1Program for Neuroscience and Behavioral Disorders, Duke-NUS Graduate Medical School, Singapore.

Neural Networks : the Official Journal of the International Neural Network Society
|December 13, 2012
PubMed
Summary
This summary is machine-generated.

Optimizing Liquid State Machine (LSM) parameters, like synaptic strength and connectivity, is key for efficient real-time computing. A genetic algorithm approach evolves LSMs, achieving high accuracy with reduced complexity and biologically inspired synaptic distributions.

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Last Updated: May 16, 2026

Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo
10:19

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Published on: March 31, 2016

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10:52

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Published on: April 23, 2019

Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Machine learning

Background:

  • The Liquid State Machine (LSM) is a biologically inspired neural network model.
  • LSMs utilize spiking neurons and dynamic synapses for real-time processing of time-varying data.
  • LSM performance is sensitive to parameter settings, particularly synaptic properties.

Purpose of the Study:

  • To investigate the impact of synaptic strength distribution and connectivity on LSM performance.
  • To optimize LSM parameters for efficient kernel function implementation.
  • To develop an automated method for designing efficient LSMs.

Main Methods:

  • Studied LSM performance across various synaptic connectivity models.
  • Analyzed the relationship between synaptic weight distribution, number of synapses, and performance.
  • Employed a genetic algorithm to evolve LSM synaptic structures from minimal configurations.

Main Results:

  • Optimal LSM performance requires balancing synaptic weight magnitude and synapse count.
  • Increased variance in synaptic weights generally improves performance on benchmark tasks.
  • The genetic algorithm successfully evolved LSMs with minimal synapses and high accuracy.

Conclusions:

  • Synaptic parameter optimization is crucial for effective LSM design.
  • Genetic algorithms offer an efficient method for optimizing LSMs, reducing computational load.
  • Evolved LSM synaptic distributions resemble those observed in biological cortical circuitry.