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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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What is an Electrochemical Gradient?01:26

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Adenosine triphosphate, or ATP, is considered the primary energy source in cells. However, energy can also be stored in the electrochemical gradient of an ion across the plasma membrane, which is determined by two factors: its chemical and electrical gradients.
The chemical gradient relies on differences in the abundance of a substance on the outside versus the inside of a cell and flows from areas of high to low ion concentration. In contrast, the electrical gradient revolves around an...
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Electrical Synapses01:28

Electrical Synapses

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Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
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Chemical Synapses01:26

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Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
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Chemical Synapses

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Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
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Power01:08

Power

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The concept of work involves force and displacement; meanwhile, the work-energy theorem relates the net work done on a body to the difference in its kinetic energy, calculated between two points on its trajectory. While none of these quantities or relations involves time explicitly, we know that the time available to accomplish work is often just as important as the amount of work itself. For example, sprinters in a race may have achieved the same velocity at the finish, therefore,...
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Updated: Feb 7, 2026

Fabrication of Gate-tunable Graphene Devices for Scanning Tunneling Microscopy Studies with Coulomb Impurities
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Low-Power, Electrochemically Tunable Graphene Synapses for Neuromorphic Computing.

Mohammad Taghi Sharbati1, Yanhao Du1, Jorge Torres1

  • 1Department of Electrical and Computer Engineering, The University of Pittsburgh, Pittsburgh, PA, 15261, USA.

Advanced Materials (Deerfield Beach, Fla.)
|July 24, 2018
PubMed
Summary

Researchers developed an electrochemical graphene synapse for brain-inspired computing. This novel synapse offers high energy efficiency and analog behavior, overcoming limitations of current digital and memristor technologies for neuromorphic applications.

Keywords:
artificial synapseelectrochemical intercalationgrapheneneuromorphic computing

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Digital computing struggles with energy-intensive emulation of analog neural behaviors.
  • Existing neuromorphic approaches using complementary metal-oxide-semiconductor (CMOS) devices are unsustainable.
  • Emerging memristor devices for neuromorphic computing face challenges like nonlinearity and write noise.

Purpose of the Study:

  • To present a novel electrochemical graphene synapse for efficient and analog neuromorphic computing.
  • To overcome the limitations of current digital and memristor-based approaches.
  • To demonstrate essential neuronal functions using the developed graphene synapse.

Main Methods:

  • Fabrication of an electrochemical synapse utilizing graphene.
  • Modulation of graphene's electrical conductance via lithium-ion concentration.
  • Characterization of synaptic properties including energy efficiency, analog tunability, endurance, retention, and linearity.

Main Results:

  • Achieved high energy efficiency (<500 fJ per switching event).
  • Demonstrated >250 nonvolatile analog states with good endurance and retention.
  • Successfully replicated essential neuronal functions like excitatory/inhibitory synapses and spike-timing-dependent plasticity with repeatability.
  • Observed a linear and symmetric resistance response.

Conclusions:

  • The electrochemical graphene synapse offers a promising, energy-efficient, and scalable solution for neuromorphic computing.
  • This approach overcomes key limitations of existing digital and memristor technologies.
  • The demonstrated synaptic functions pave the way for advanced brain-inspired computing architectures.