Talking to biological neurons like a “native” speaker by an artificial neuron

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Science fiction has depicted the vision of cyborg who has a human brain that can seamless connect with machinery body like “Battle Angel Alita”. In principle, this vision can really happen as long as the machinery body can efficiently communicate with the brain. Pursing this goal, technologies to communicate with the brain like Neuralink are emerging. Talking to neurons for understanding and even engineering with the brain is helpful to cure brain diseases, realize consciousness control and develop neuron rehabilitation or even augment human capabilities.

 

Problems identified: Communication modality mismatching

The key role for communication is the language modality. Currently, brain-machine technologies mostly use the electrical signals as the messengers between brain and machine.1 However, the communication in the brain is mainly in a form of chemical language: neurons communicate at junctions — synapses — using neurotransmitters as the messengers. When current technologies are used to interact with live neurons, there is a communication modality mismatch issue. Hence, we are curious whether it is possible to “speak” the same chemical language with the neurons.

Another motivation behind this research is the importance of chemicals in human. On the one hand, the origin of electrical signals is chemical messengers. Explicitly, the electrical signals of neurons originate from the ionic flow across the membrane, and ionic fluctuation is mainly triggered by neurotransmitters. On the other hand, much intelligent information — including memory and emotion — is encoded or conveyed by neurotransmitters. For example, three scientists deciphered that dopamine (DA) is the important chemical messengers in between neurons and regulate the memory function of brain (2000 Nobel Prize in Physiology or Medicine). That means for brain information, electrical signals may be regarded as the tip of the iceberg while chemical signals contain huge important information. Deciphering the chemical signals of the brain can help to precisely interpretate the brain’s complex consciousness. Motivated by the communication problem and importance of neurotransmitter, we have the idea of talking to biological neurons like a “native” speaker.

 

The requirements of bioelectronics to talk to biological neurons

Neurons talk to each other via neurotransmitter as the messengers. To achieve this function, each neuron has the ability of recognizing the neurotransmitter from and releasing neurotransmitter to neighboring neurons. More importantly, neurons talk to each other very smartly. They can adapt their connection to the stimuli strength/frequency (synaptic plasticity), which is regarded as the basis of human memory formation. Hence, an artificial neuron that pretended to be a “native” speaker should at least possess three basic functionalities: neurotransmitter recognition, synaptic plasticity, and neurotransmitter releasor.

There are many researches to empower bioelectronics with the features of neurons. For example, the synaptic plasticity has inspired many neuromorphic devices,2, 3 such as phase-change neurons,4 artificial afferent nerves,5 and artificial nociceptor6. Recently, there are also neurotransmitter responsive bioelectronics based on an organic synaptic transistor7 and soft “NeuroString”8 were reported where neurotransmitter driven oxidation tuned the electrical properties of sensing interfaces. Despite these great achievements, current reported integrated neuromorphic devices can not realize aforementioned “native speaker” goal due to either the limitation to physical/electrical signals or lack of communication loop.

 

Achievements in this work

In this work, we report a chemically mediated artificial neuron which is capable of receiving and adaptively sending the neurotransmitter DA to live neuron. It is a fully integrated sequentially operating system that includes a DA sensor, a memristor for a signal procession, and a DA releasor. The artificial neuron detects dopamine using a carbon-based electrochemical sensor and then processes the sensory signals using a memristor with synaptic plasticity, before stimulating dopamine release through a heat-responsive hydrogel.

We further demonstrate the feasibility of biohybrid interfaces. The system responds to dopamine exocytosis from rat pheochromocytoma cells and also releases dopamine to activate pheochromocytoma cells, forming a chemical communication loop similar to interneurons. To illustrate the potential of the approach, we show that the artificial neuron can trigger the controllable movement of a mouse leg and a robotic hand.

Figure 1. The neurotransmitter-mediated artificial neuron. a, Scheme of biohybrid neuro-interface where artificial neurons chemically communicate with biological neurons, forming a complete neuromorphic communication loop. A biological neuron comprising neurotransmitter recognition, followed by the spiking of action potential, and the triggering of neurotransmitter release. b, An artificial neuron comprising neurotransmitter detection via an electrochemical sensor, sensory signal processing with synaptic plasticity using a resistive switch memristor device, and neurotransmitter releasor based on a hydrogel component.

 

Perspectives

Despite the achievements, there is still a systematic performance gap between the artificial neurons and live neuron, such as response time, power consumption, device dimension and systematic stability. Strategies utilizing nano/bio-materials design and microfabrication should be effective to narrow the performance gap such as biomodification of sensors, downscaling the devices, selecting energy-efficient elements, and conducting device encapsulation.

After bridging the gap of an individual neuron, next step is to build a chemically mediated network to fulfill complex emotional-related activities. The vision is that artificial neuron network can work like biological systems for cyborg construction, consciousness control, and neuromorphic computing. However, realizing this vision is rather challenging and calls for a continuous and intensive interdisciplinary cooperation of researchers from different backgrounds.

 

Reference

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  3. Wan, C.; Cai, P.;  Wang, M.;  Qian, Y.;  Huang, W.; Chen, X., Artificial sensory memory. Adv. Mater. 2020, 32 (15), 1902434.
  4. Tuma, T.; Pantazi, A.;  Le Gallo, M.;  Sebastian, A.; Eleftheriou, E., Stochastic phase-change neurons. Nat. Nanotechnol. 2016, 11 (8), 693-699.
  5. Kim, Y.; Chortos, A.;  Xu, W.;  Liu, Y.;  Oh, J. Y.;  Son, D.;  Kang, J.;  Foudeh, A. M.;  Zhu, C.;  Lee, Y.;  Niu, S.;  Liu, J.;  Pfattner, R.;  Bao, Z.; Lee, T.-W., A bioinspired flexible organic artificial afferent nerve. Science 2018, 360 (6392), 998-1003.
  6. Yoon, J. H.; Wang, Z.;  Kim, K. M.;  Wu, H.;  Ravichandran, V.;  Xia, Q.;  Hwang, C. S.; Yang, J. J., An artificial nociceptor based on a diffusive memristor. Nat. Commun. 2018, 9 (1), 417.
  7. Keene, S. T.; Lubrano, C.;  Kazemzadeh, S.;  Melianas, A.;  Tuchman, Y.;  Polino, G.;  Scognamiglio, P.;  Cinà, L.;  Salleo, A.;  van de Burgt, Y.; Santoro, F., A biohybrid synapse with neurotransmitter-mediated plasticity. Nat. Mater. 2020, 19, 969–973.
  8. Li, J.; Liu, Y.;  Yuan, L.;  Zhang, B.;  Bishop, E. S.;  Wang, K.;  Tang, J.;  Zheng, Y.-Q.;  Xu, W.;  Niu, S.;  Beker, L.;  Li, T. L.;  Chen, G.;  Diyaolu, M.;  Thomas, A.-L.;  Mottini, V.;  Tok, J. B. H.;  Dunn, J. C. Y.;  Cui, B.;  Pașca, S. P.;  Cui, Y.;  Habtezion, A.;  Chen, X.; Bao, Z., A tissue-like neurotransmitter sensor for the brain and gut. Nature 2022, 606 (7912), 94-101.

 

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