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A biomimetic neural encoder for spiking neural network.

Shiva Subbulakshmi Radhakrishnan1, Amritanand Sebastian1, Aaryan Oberoi1

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|April 10, 2021
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Researchers developed a novel biomimetic device using a dual-gated MoS2 field-effect transistor (FET) to convert analog signals into spike trains for spiking neural networks (SNNs). This device achieves efficient and precise neural encoding with low energy consumption, crucial for neuromorphic computing.

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Area of Science:

  • Neuromorphic Engineering
  • Materials Science
  • Computational Neuroscience

Background:

  • Spiking neural networks (SNNs) offer advantages in energy efficiency and processing speed over traditional artificial neural networks (ANNs).
  • Implementing SNNs in neuromorphic hardware requires effective neural encoders to translate stimuli into biologically plausible spike trains.
  • Existing solid-state transducers are insufficient for the demands of advanced neural encoding in neuromorphic systems.

Purpose of the Study:

  • To develop a biomimetic device capable of encoding analog signals into stochastic spike trains for SNNs.
  • To demonstrate the device's ability to implement various neural encoding algorithms.
  • To evaluate the device's performance in terms of dynamic range, encoding precision, and energy efficiency.

Main Methods:

  • Fabrication of a dual-gated molybdenum disulfide (MoS2) field-effect transistor (FET) device.
  • Utilizing the MoS2 FET to perform rate-based, spike timing-based, and spike count-based neural encoding.
  • Assessing the dynamic range and encoding precision of the developed neural encoder.
  • Measuring the energy consumption per spike during the encoding process.
  • Testing the device's performance on the MNIST dataset for encoding and subsequent SNN inference.

Main Results:

  • The MoS2 FET device successfully encoded analog signals into stochastic spike trains using multiple neural encoding algorithms.
  • Demonstrated effective capture of dynamic range and encoding precision in neural signal processing.
  • Achieved a frugal encoding energy consumption of approximately 1-5 pJ/spike.
  • Successfully encoded the MNIST dataset in approximately 200 timesteps.
  • Enabled more than 91% accurate inference using a trained SNN with the encoded data.

Conclusions:

  • The developed biomimetic MoS2 FET serves as a promising hardware neural encoder for neuromorphic computing.
  • The device efficiently translates analog stimuli into biologically plausible spike trains with high precision and low energy cost.
  • This advancement facilitates the practical implementation of SNNs in next-generation, brain-inspired computing hardware.