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Related Concept Videos

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.
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...
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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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Integration of Synaptic Events01:28

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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...
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The Role of Ion Channels in Neuronal Computation01:19

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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....
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Metal-Semiconductor Junctions01:24

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The contact of metal and semiconductor can lead to the formation of a junction with either Schottky or Ohmic behavior.
Schottky Barriers
Schottky barriers arise when a metal with a work function (Φm) contacts a semiconductor with a different work function (Φs). Initially, electrons transfer until the Fermi levels of the metal and semiconductor align at equilibrium. For instance, if Φm > Φs, the semiconductor Fermi level is higher than the metal's before contact. The...
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Temperature-resilient solid-state organic artificial synapses for neuromorphic computing.

A Melianas1, T J Quill2, G LeCroy2

  • 1Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA. armantas.melianas@stanford.edu asalleo@stanford.edu.

Science Advances
|September 16, 2020
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Summary
This summary is machine-generated.

New electrochemical random-access memories (ECRAMs) offer stable, efficient operation up to 90°C. These devices enable faster artificial neural network (ANN) training for neuromorphic computing applications.

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Science

Background:

  • Resistive memories are crucial for neuromorphic computing but face challenges with linearity and noise.
  • Existing electrochemical random-access memories (ECRAMs) show promise for faster training but lack high-temperature stability.

Purpose of the Study:

  • To develop solid-state ECRAMs that operate stably and efficiently at temperatures up to 90°C.
  • To address the limitations of conventional resistive memories and current ECRAMs for artificial neural network (ANN) accelerators.

Main Methods:

  • Utilizing (semi)conducting polymers and ion gel electrolyte films to create solid-state ECRAMs.
  • Characterizing device performance, including resistance tuning linearity, switching speed, noise, voltage, energy consumption, and endurance at elevated temperatures.

Main Results:

  • Demonstrated stable and nearly temperature-independent ECRAM operation up to 90°C.
  • Achieved linear resistance tuning over a >2× dynamic range, 20-nanosecond switching, and submicrosecond write-read cycling.
  • Exhibited low noise, low-voltage (±1V), low energy (~80 fJ/write), and excellent endurance (>10^9 cycles at 90°C).

Conclusions:

  • Solid-state ECRAMs based on polymers and ion gels provide a viable solution for high-temperature neuromorphic computing.
  • These high-performance ECRAMs represent a significant advancement towards practical implementation in hardware artificial neural networks (ANNs).