A Bioinspired Artificial Gustatory Neuron for a Neuromorphic Based Electronic Tongue

Area of Science:

• Materials Science
• Neuroscience
• Sensor Technology

Background:

• Conventional electronic tongues (E-tongues) often require significant energy and space.
• Mimicking biological sensory neurons can offer more efficient and scalable solutions.

Purpose of the Study:

• To develop a novel biomimetic neuromorphic sensor for an energy-efficient and scalable electronic tongue.
• To integrate in-sensor neuromorphic functioning for reduced energy and area consumption.

Main Methods:

• Utilized a metal-oxide-semiconductor field-effect transistor (MOSFET) to mimic a biological gustatory neuron.
• Implemented pH-sensitive (Al2O3) and sodium-sensitive (sodium ionophore X) artificial gustatory neurons.
• Demonstrated a sensitivity control function inspired by biological sensory neurons.

Main Results:

• The proposed E-tongue successfully detects ion concentrations and encodes spike signals on the MOSFET.
• Achieved simultaneous detection and in-sensor signal processing, mimicking a spiking neural network (SNN).
• A functional E-tongue capable of classifying two distinct liquids was demonstrated.

Conclusions:

• The novel neuromorphic sensor offers an energy-efficient and scalable approach for electronic tongue applications.
• In-sensor neuromorphic functioning significantly reduces hardware complexity and resource requirements.
• The demonstrated artificial gustatory neurons show practical applicability in liquid classification.