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Related Concept Videos

The Synapse02:47

The Synapse

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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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Neuronal Communication01:28

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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...
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Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
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Synaptic Signaling01:09

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Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
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Related Experiment Video

Updated: Feb 25, 2026

DetectSyn: A Rapid, Unbiased Fluorescent Method to Detect Changes in Synapse Density
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A Fast Algorithm for Analysis of Molecular Communication in Artificial Synapse.

Bilgesu A Bilgin, Ozgur B Akan

    IEEE Transactions on Nanobioscience
    |July 26, 2017
    PubMed
    Summary

    This study introduces a deterministic algorithm for analyzing molecular communications in closed artificial synapses, offering a computationally efficient alternative to Monte Carlo methods for designing intra-body devices.

    Area of Science:

    • Biophysics
    • Computational Neuroscience
    • Nanotechnology

    Background:

    • Biological synapses (BSs) are fundamental for neural function.
    • Molecular Communications (MCs) are being explored for bio-integrated devices.
    • Artificial synapses (ASs) offer potential for novel applications, including intra-body communication.

    Purpose of the Study:

    • To analyze MC in a closed AS model, distinct from open BSs.
    • To develop a deterministic algorithm for simulating AS behavior, replacing Monte Carlo methods.
    • To compare the performance and characteristics of AS-based MC with BS-based MC.

    Main Methods:

    • Development of a deterministic algorithm to calculate expected values of parameters like receptor state evolution.
    • Validation of the algorithm by comparing its results with ensemble-averaged Monte Carlo simulations.

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  • Analysis of MC properties in a closed AS with elevated transmitter uptake.

    • Main Results:

    • The deterministic algorithm closely matches Monte Carlo results with significantly reduced computational cost.
    • MC in a closed AS exhibits properties similar to BSs, with quantal size and receptor density driving synaptic plasticity.
    • Closed AS models show slower decaying receptor state transients compared to BSs due to prolonged transmitter clearance.

    Conclusions:

    • The developed deterministic algorithm is a computationally efficient tool for optimizing MC device design, including AS.
    • Closed ASs are viable for intra-body applications due to their stability and environmental independence.
    • Differences in transmitter clearance mechanisms lead to distinct dynamic properties between AS and BS.