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The Synapse02:47

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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 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.
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Quantifying Synapses: an Immunocytochemistry-based Assay to Quantify Synapse Number
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Neuromorphic computing with multi-memristive synapses.

Irem Boybat1,2, Manuel Le Gallo3, S R Nandakumar3,4

  • 1IBM Research - Zurich, Säumerstrasse 4, 8803, Rüschlikon, Switzerland. ibo@zurich.ibm.com.

Nature Communications
|June 30, 2018
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Summary
This summary is machine-generated.

This study introduces a novel multi-memristive synaptic architecture to overcome challenges in neuromorphic computing. The new design enables precise control of synaptic weights, paving the way for more accurate and energy-efficient artificial intelligence systems.

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Area of Science:

  • Neuromorphic Engineering
  • Artificial Intelligence
  • Materials Science

Background:

  • Neuromorphic computing aims to create next-generation intelligent systems.
  • Memristive devices are proposed for synaptic weights in artificial neural networks.
  • Precise control of memristor conductance is a key challenge for network accuracy.

Purpose of the Study:

  • To present a multi-memristive synaptic architecture for improved neuromorphic computing.
  • To address the challenge of precise synaptic weight modulation in artificial neural networks.
  • To demonstrate the effectiveness of the proposed architecture for both spiking and non-spiking neural networks.

Main Methods:

  • Developed a multi-memristive synaptic architecture with a global counter-based arbitration scheme.
  • Focused on phase change memory devices and developed a comprehensive device model.
  • Conducted simulations for spiking and non-spiking neural networks and performed large-scale experiments.

Main Results:

  • Demonstrated the effectiveness of the multi-memristive architecture via simulations.
  • Achieved successful unsupervised learning of temporal correlations using a spiking neural network with over a million phase change memory devices.
  • Showcased the potential for precise conductance modulation over a wide dynamic range.

Conclusions:

  • The proposed multi-memristive synaptic architecture is a significant step towards large-scale, energy-efficient neuromorphic computing.
  • The efficient arbitration scheme effectively manages device conductance for high network accuracy.
  • Experimental validation confirms the viability of the approach for real-world applications in artificial intelligence.