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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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Electrical Synapses01:28

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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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Overview of Synapses01:25

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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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Second Order systems II01:18

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In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
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Related Experiment Video

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Quantifying Synapses: an Immunocytochemistry-based Assay to Quantify Synapse Number
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RRAM-based synapse devices for neuromorphic systems.

K Moon1, S Lim, J Park

  • 1Pohang University of Science and Technology (POSTECH), Korea. hwanghs@postech.ac.kr.

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|November 15, 2018
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Summary

Developing ideal synapse devices is crucial for on-chip training in artificial neural networks (ANNs). Current RRAM devices show promise but require further advancements for neuromorphic systems, especially for on-chip training capabilities.

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Neuroscience

Background:

  • Hardware artificial neural networks (ANNs) require efficient synapse devices for massive parallel computing and low power consumption.
  • Existing synapse devices, particularly RRAM-based ones, face limitations in scalability, memory characteristics, and operational requirements for neuromorphic systems.

Purpose of the Study:

  • To review and analyze various resistive random-access memory (RRAM) synapse devices for their suitability in neuromorphic systems.
  • To evaluate the impact of different RRAM synapse device characteristics on the performance of ANNs in pattern recognition tasks.
  • To identify the challenges and future directions for developing ideal synapse devices for on-chip training.

Main Methods:

  • Categorization and discussion of various RRAM synapse devices: filamentary switching (HfOx, TaOx, Cu-CBRAM) and analog switching (Pr0.7Ca0.3MnOx, TiOx, HfZrOx).
  • Optimization of potentiation/depression conditions for filamentary devices to enhance linearity and MLC characteristics.
  • Modulation of synapse characteristics by controlling metal electrode reactivity and oxygen concentration.
  • Evaluation of Metal-Ferroelectric-Insulator-Semiconductor (MFIS) FET devices for retention and analog memory properties.
  • Estimation of pattern recognition accuracy (MNIST, CIFAR-10) based on device characteristics.

Main Results:

  • Optimized filamentary RRAM devices show improved conductance linearity and multilevel cell (MLC) characteristics.
  • Interface RRAM offers better MLC characteristics but has limitations in retention and linearity.
  • MFIS FET devices demonstrate good retention and analog memory capabilities due to polarization.
  • Synapse device characteristics directly correlate with the pattern recognition accuracy of ANNs.
  • Current 2-terminal RRAM devices are suitable for off-chip training, but on-chip training requires new 3-terminal devices or novel operational principles.

Conclusions:

  • Achieving ideal synapse devices for simultaneous fulfillment of all neuromorphic requirements necessitates atomic-scale control over oxygen vacancies and metal ions.
  • Further research into 3-terminal devices or devices with new operating principles is essential for enabling on-chip training in hardware ANNs.
  • The development of advanced synapse devices is critical for advancing the capabilities of neuromorphic computing and artificial intelligence.