News Release

A metal-organic framework neuron with dopamine perception

Peer-Reviewed Publication

Science China Press

The MOF neuron

image: 

Based on the DA mediation, some sophisticated neuronal functions, including integration-and-firing, synaptic facilitation-induced spike broadening and DA-tunable spiking number and width, were mimicked by the MOF neuron.

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Credit: ©Science China Press

The intricate operation of the human brain has inspired the development of various innovative neuromorphic devices. Neurons are fundamental units of the human nervous system that process and transmit information by using chemical synapses via spikes or action potentials. The generation and characteristics of the spikes are synergistically regulated by voltage, ions and neurotransmitters in a liquid environment. To achieve complex information encoding, neurons exhibit diverse firing behaviors, as indicated by the spikes with a wide range of shapes, frequencies and patterns. Therefore, to ensure the authenticity of bionic neural components, it is necessary to simulate the chemical synaptic transmission mechanism involving ions and neurotransmitters, as well as reproduce the dynamic tunable characteristics of spikes.

Existing solid-state neurons, based on silicon or metal-oxide semiconductors, fail to work in aqueous environments, restraining their neurotransmitter/ion-modulated neural features. More realistic aqueously compatible neurons can be achieved by using organic neurons based on mixed ion–electron conducting polymers. However, the challenges for the development of organic neurons are concerns about structural and performance requirements, making the laborious design and synthesis of functional polymers a bottleneck to new discoveries.

Metal–organic frameworks (MOFs) constitute a class of porous crystalline materials assembled from metal ionic centers and organic molecular linkers through strong bonds. Attracted by its rich chemistry, high porosity, volumetric capacitance, unique memristive properties, etc., MOFs have recently gained attention in the field of aqueous neuromorphic devices. Inspired by their unique properties, a team led by Wei-Wei Zhao at Nanjing University has developed a MOF neuron with the perception of dopamine (DA), a key neurotransmitter in the brain. Published in National Science Review, the innovation bridged the gap between solid-state devices and biological systems by operating in aqueous environment.

The team used a semiconductive MOF Ni3(HITP)2, deposited onto a patterned substrate to fabricate a MOF transistor, which was further integrated with a microcontroller unit and external circuits to construct an artificial neuron. The unique feature of MOF neurons lies in their signal transmission mechanism analogous to that of biological neurons. By utilizing DA, the researcher demonstrated the following key capabilities:

Synaptic plasticity: The device exhibited behaviors resembling short-term memory, such as paired-pulse facilitation and depression—functions essential for learning and memory processes in the brain.

Integrate-and fire dynamics: The artificial neuron accumulated input signals until reaching a threshold, at which point it generated a spike—mimicking the information-processing mechanism of the real neurons.

Spike tuning: The concentration of DA directly modulated both number and width of spikes. For instance, higher DA levels resulted in an increased number of spikes and broader spike widths.

Beyond mimicking biological behavior, the MOF neuron demonstrated practical utility. By integrating the MOF neuron with a robotic hand, the team achieved precise control over the hand’s movements by DA-tunable spikes. Specifically, elevated DA concentrations enabled faster and more complete contractions in response to input pulses.

Zhao explained, “Biological neurons depend on neurotransmitters like dopamine to modulate signals. Our MOF neuron represents a significant step toward developing artificial systems, offering promising prospects for neuromorphic biosensors and advanced human-machine interfaces.”


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