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Related Concept Videos

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
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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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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions
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Synapse cell optimization and back-propagation algorithm implementation in a domain wall synapse based crossbar

Divya Kaushik1, Janak Sharda1, Debanjan Bhowmik1

  • 1Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi-110016 India.

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This study enhances on-chip learning for domain wall synapse crossbar neural networks by modifying gradient descent for scalability. This innovation improves accuracy, making these networks competitive for real-world machine learning applications.

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Area of Science:

  • Neuromorphic Engineering
  • Materials Science

Background:

  • On-chip learning in spin-orbit torque driven domain wall synapse based crossbar fully connected neural networks (FCNNs) offers high speed and energy efficiency.
  • Scalability challenges hinder the practical application of these FCNNs compared to conventional training methods.

Purpose of the Study:

  • To address the scalability limitations of on-chip learning in domain wall synapse based FCNNs.
  • To improve the accuracy and applicability of these neuromorphic systems for complex machine learning tasks.

Main Methods:

  • A modified gradient descent algorithm incorporating thresholding units was developed to optimize synapse cells and enhance scalability.
  • A backpropagation algorithm was implemented using an additional crossbar to enable hardware-based training of multi-layered FCNNs.
  • Micromagnetic and SPICE circuit simulations were employed to validate the proposed methods.

Main Results:

  • The proposed modifications successfully optimized synapse cells, achieving a scalable on-chip learning scheme.
  • The inclusion of a hidden layer, enabled by the backpropagation implementation, significantly improved the accuracy of the domain wall synapse based FCNN.
  • Simulations demonstrated superior performance for FCNNs with hidden layers across various machine learning datasets.

Conclusions:

  • The modified gradient descent algorithm with thresholding units effectively addresses the scalability of domain wall synapse based FCNNs.
  • Hardware implementation of backpropagation with an additional crossbar enables efficient training of multi-layered networks, crucial for complex data classification.
  • This work presents a scalable and accurate on-chip learning solution for domain wall synapse based FCNNs, paving the way for practical neuromorphic computing applications.