{'fraction_non_alphanumeric': 0.01856763925729443, 'fraction_numerical': 0.0, 'mean_word_length': 5.6643109540636045, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Smart Autonomous Electro-Optic Platforms Enabling Innovative Brain Therapies

The future of brain research lies in the application of new technologies drawing from the latest developments in biology, physics and engineering to advance our understanding of how this complex organ processes, integrates and transfers information. Among these, optogenetics is a groundbreaking technology that allows using light to selectively activate neurons in the cortex of transgenic animals, usually mice, to observe its effect in large biological networks. A new research paradigm drawing from these advances consists of synchronizing optogenetic stimulation with electrophysiology recordings, to close the loop and to regulate the neural microcircuits, or to repair them. Such an approach holds promise to accelerate the development of new therapeutics against brain diseases by enabling entirely new experimental research scenarios with freely behaving animal models. As a result, the development of advanced wireless microelectronic implantable systems to elicit, extract and process brain data in real time has become a source of significant interest. This paper reviews the design challenges and the state-of-the-art technology in this field. We present the design of a complete electro-optic device for preforming optogenetics and multichannel electrophysiology in a closed-loop (CL) system with live neurons. We cover the design of the different CMOS integrated building blocks involved in this system to perform photostimulation and multichannel neural recording in parallel. We describe advanced hardware strategies to perform action potential (AP) detection, neural data compression and AP sorting in real-time, over several parallel recording channels for enabling real-time CL neural control. Finally, we present CL experimental results obtained in vivo with an electro-optic prototype.