Development of Korea Neuroethics Guidelines

Background@#Advances in neuroscience and neurotechnology provide great benefits to humans though unknown challenges may arise. We should address these challenges using new standards as well as existing ones. Novel standards should include ethical, legal, and social aspects which would be appropriate for advancing neuroscience and technology.Therefore, the Korea Neuroethics Guidelines were developed by stakeholders related to neuroscience and neurotechnology, including experts, policy makers, and the public in the Republic of Korea.Method: The guidelines were drafted by neuroethics experts, were disclosed at a public hearing, and were subsequently revised by opinions of various stakeholders. @*Results@#The guidelines are composed of twelve issues; humanity or human dignity, individual personality and identity, social justice, safety, sociocultural prejudice and public communication, misuse of technology, responsibility for the use of neuroscience and technology, specificity according to the purpose of using neurotechnology, autonomy, privacy and personal information, research, and enhancement. @*Conclusion@#Although the guidelines may require a more detailed discussion after future advances in neuroscience and technology or changes in socio-cultural milieu, the development of the Korea Neuroethics Guidelines is a milestone for the scientific community and society in general for the ongoing development in neuroscience and neurotechnology.

Modeling Long-term Spike Frequency Adaptation in SA-I Afferent Neurons Using an Izhikevich-based Biological Neuron Model

To develop a biomimetic artificial tactile sensing system capable of detecting sustained mechanical touch, we propose a novel biological neuron model (BNM) for slowly adapting type I (SA-I) afferent neurons. The proposed BNM is designed by modifying the Izhikevich model to incorporate long-term spike frequency adaptation. Adjusting the parameters renders the Izhikevich model describing various neuronal firing patterns. We also search for optimal parameter values for the proposed BNM to describe firing patterns of biological SA-I afferent neurons in response to sustained pressure longer than 1-second. We obtain the firing data of SA-I afferent neurons for six different mechanical pressure ranging from 0.1 mN to 300 mN from the ex-vivo experiment on SA-I afferent neurons in rodents. Upon finding the optimal parameters, we generate spike trains using the proposed BNM and compare the resulting spike trains to those of biological SA-I afferent neurons using the spike distance metrics. We verify that the proposed BNM can generate spike trains showing long-term adaptation, which is not achievable by other conventional models. Our new model may offer an essential function to artificial tactile sensing technology to perceive sustained mechanical touch.

A Computational Modeling Reveals That Strength of Inhibitory Input, E/I Balance, and Distance of Excitatory Input Modulate Thalamocortical Bursting Properties

The thalamus is a brain structure known to modulate sensory information before relaying to the cortex. The unique ability of a thalamocortical (TC) neuron to switch between the high frequency burst firing and single spike tonic firing has been implicated to have a key role in sensory modulation including pain. Of the two firing modes, burst firing, especially maintaining certain burst firing properties, was suggested to be critical in controlling nociceptive behaviors. Therefore, understanding the factors that influence burst firing properties would offer important insight into understanding sensory modulation. Using computational modeling, we investigated how the balance of excitatory and inhibitory inputs into a TC neuron influence TC bursting properties. We found that intensity of inhibitory inputs and the timing of excitatory input delivery control the dynamics of bursting properties. Then, to reflect a more realistic model, excitatory inputs delivered at different dendritic locations—proximal, intermediate, or distal—of a TC neuron were also investigated. Interestingly, excitatory input delivered into a distal dendrite, despite the furthest distance, had the strongest influence in shaping burst firing properties, suggesting that not all inputs equally contribute to modulating TC bursting properties. Overall, the results provide computational insights in understanding the detailed mechanism of the factors influencing temporal pattern of thalamic bursts.

Implantable Neural Probes for Brain-Machine Interfaces – Current Developments and Future Prospects

A Brain-Machine interface (BMI) allows for direct communication between the brain and machines. Neural probes for recording neural signals are among the essential components of a BMI system. In this report, we review research regarding implantable neural probes and their applications to BMIs. We first discuss conventional neural probes such as the tetrode, Utah array, Michigan probe, and electroencephalography (ECoG), following which we cover advancements in next-generation neural probes. These next-generation probes are associated with improvements in electrical properties, mechanical durability, biocompatibility, and offer a high degree of freedom in practical settings. Specifically, we focus on three key topics: (1) novel implantable neural probes that decrease the level of invasiveness without sacrificing performance, (2) multi-modal neural probes that measure both electrical and optical signals, (3) and neural probes developed using advanced materials. Because safety and precision are critical for practical applications of BMI systems, future studies should aim to enhance these properties when developing next-generation neural probes.