Novel electrode technologies for neural recordings
By Guosong Hong, Charles M. Lieber. Nature Reviews Neuroscience, 2019
594 citations recorded by the source corpus.
Neural recording electrode technologies have contributed considerably to neuroscience by enabling the extracellular detection of low-frequency local field potential oscillations and high-frequency action potentials of single units. Nevertheless, several long-standing limitations exist, including low multiplexity, deleterious chronic immune responses and long-term recording instability. Driven by initiatives encouraging the generation of novel neurotechnologies and the maturation of technologies to fabricate high-density electronics, novel electrode technologies are emerging. Here, we provide an overview of recently developed neural recording electrode technologies with high spatial integration, long-term stability and multiple functionalities. We describe how these emergent neurotechnologies can approach the ultimate goal of illuminating chronic brain activity with minimal disruption of the neural environment, thereby providing unprecedented opportunities for neuroscience research in the future. Here, Hong and Lieber review recent developments in electrode technologies for the recording of single-unit spiking activity. They focus on advances in electrodes with high spatial integration, long-term stability and multifunctional capacities.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/85efd75c11896ff4dd1d1ebfae630ea42303fc57
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In Cellular and Molecular Neuroscience
In Cognitive Neuroscience
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Citations in the corpus
- An Integrated Brain-Machine Interface Platform With Thousands of Channels (2019, unknown): “While rigid metal arrays facilitate penetrating the brain, the size, Young’s modulus and bending stiffness mismatches between stiff probes and brain tissue can drive immune responses that limit the function and longevity of these devices [19, 11].”
- Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence. (2020, background): “Years of research in our community has resulted in many datasets, and with advanced recording techniques, the pace of acquiring new data is gaining momentum (Hong and Lieber, 2019): more”