The continuous exchange between the neuroscience and neuroengineering communities that took place over the past decades has uncovered a multitude of technological solutions to interface with the brain. In this framework, a fascinating approach relies on the integration of multiple activation and monitoring capabilities in the same implantable neural probe to better study the multifaceted nature of neural signaling and related functions in the deep brain regions. We highlight current challenges and perspectives on technological developments that could potentially enable the integration of multiple functionalities on optical fiber-based non-planar implantable neurophotonics probes.
Recent strides in data-driven and deep learning methods have empowered image and wavefront reconstruction in such environments. This breakthrough finds promising roles in biomedical applications like image transmission and holography. Yet, the reconstructed image quality relies on deep learning model effectiveness in understanding transmission mechanisms. In our presentation, we propose two enhancements. First, employs a novel deep learning architecture inspired by light physics, showcasing enhanced image reconstruction quality and broad problem generalization. The second one is an optical method which boosts data variance through holographic encoding, enabling multi-channel image transmission and improved data fusion via deep learning.
An emerging trend at the forefront of optical neural interfaces leverages the optical properties of photonic nanostructures to modulate light delivery and collection patterns in deep brain regions. This perspective article surveys the early works that have spearheaded this promising strategy, and discusses its promise towards the establishment of a class of augmented nano-neurophotonic probes.
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