Many neural sensing technologies have been developed for scientific or therapeutic purposes. With the advancement in semiconductor or flexible technology, high-density intra-cortical micro-electrode arrays (MEAs) enable electrical sensing with sub-millisecond temporal resolution and 10’s of µm spatial resolution. They have become the most widely adopted method in neuroscience experiments and neuro-therapeutics at the moment. However, one of the key challenges yet to overcome, is the lack of a high-bandwidth, miniature and energy-efficient wireless telemetry, which allows neural sensors to be chronically implanted without infection risks. Such high-fidelity neural sensors produce a huge amount of data which need to be wirelessly transferred across layers of tissue without introducing heating, but miniature neural implants have very limited resource to support energy-consuming data transmission. Inspired from biological neurons, the energy consumption for processing and transportation of information can be significantly reduced, if only the changes are processed with a “spike” format and propagated using “ions”. Furthermore, if neural computation, .e.g., neural classification, can be performed locally on the implant, in real-time, it can help to reduce now only energy consumption and implant form factor, but also reduce latency for closed-loop neuromodulation. This requires careful partition between communication and computation. In this talk, we will discuss how we can be inspired from the way neurons communicate to develop a spiking wireless telemetry and on-implant neural classification for future implantable neural interfaces.
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