Using neuromorphic computing in prediction of GABA concentration – a pilot study

TL;DR

This study explores using neuromorphic computing to predict GABA concentration in the brain with energy-efficient machine learning models.

Contribution

The novel use of neuromorphic computing and spiking neural networks for real-time GABA concentration prediction is introduced.

Findings

CNN models showed high accuracy in predicting GABA concentration from dielectric data.

Spiking neural networks demonstrated energy efficiency and real-time processing capabilities.

Tkinter was used to create a user-friendly interface for data transfer between the neuromorphic chip and measurement system.

Abstract

Neuromorphic computing has the potential to facilitate detection of GABA concentration levels in the brain, and offers energy-efficient, real-time machine learning processing possibilities. To study whether neuromorphic computing can be used for GABA concentration detection, dielectric relaxation spectroscopy was used to acquire permittivity data of different concentrations of GABA solution. Thereafter, two different machine learning models were compared (Feedforward neural network (FFNN) and convolutional neural network (CNN)) for accuracy in prediction of GABA concentration from dielectric properties. The CNN model was then converted to spiking Neural Networks (SNNs), which showed promising results for energy efficiency and real-time processing capabilities. The system incorporates Tkinter, a Python interface to the Tcl/Tk GUI toolkit for seamless data transfer between the neuromorphic chip and the measurement system, ensuring flexibility and scalability in a user-friendly system.

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