Integrated Calendar

Retinal degenerative diseases lead to blindness due to loss of the “image capturing” photoreceptors, while neurons in the “image-processing” inner retinal layers are relatively well preserved. Information can be reintroduced into the visual system using electrical stimulation of the surviving inner retinal neurons. Some electronic retinal prosthetic systems have been already approved for clinical use, but they provide low resolution and involve very difficult implantation procedures. We developed a photovoltaic subretinal prosthesis which converts light into pulsed electric current, stimulating the nearby inner retinal neurons. Visual information is projected onto the retina from video goggles using pulsed nearinfrared (~880nm) light. This design avoids the use of bulky electronics and wiring, thereby greatly reducing the surgical complexity. Optical activation of the photovoltaic pixels allows scaling the implants to thousands of electrodes. In preclinical studies, we found that prosthetic vision with subretinal implants preserves many features of natural vision, including flicker fusion at high frequencies (>20 Hz), adaptation to static images, antagonistic center-surround organization and nonlinear summation of subunits in receptive fields, providing high spatial resolution. Results of the clinical trial with our implants (PRIMA, Pixium Vision) having 100µm pixels, as well as preclinical measurements with 75 and 55µm pixels, confirm that spatial resolution of prosthetic vision can reach the sampling density limit. For a broad acceptance of this technology by patients who lost central vision due to age-related macular degeneration, visual acuity should exceed 20/100, which requires pixels smaller than 25µm. I will describe the fundamental limitations in electro-neural interfaces and 3-dimensional configurations which should enable such a high spatial resolution. Ease of implantation of these wireless arrays, combined with high resolution opens the door to highly functional restoration of sight.

Sensory input reaching the brain from bilateral and offset channels is nonetheless perceived as unified. This unity could be explained by simultaneous projections to both hemispheres, or inter-hemispheric information transfer between sensory cortical maps. Odor input, however, is not topographically organized, nor does it project bilaterally, making olfactory perceptual unity enigmatic. Here we report a circuit that interconnects mirror-symmetric isofunctional output cells between the mouse olfactory bulbs. Connected neurons respond to similar odors from ipsi- and contra-nostrils, whereas unconnected neurons do not respond to odors from the contralateral nostril. This circuit enables sharing of odor information across hemispheres in the absence of a cortical topographical organization, suggesting that olfactory glomerular maps are the equivalent of cortical sensory maps found in other senses.

Atomically-resolved imaging of materials has become the mainstay of modern materials science, as enabled by advent of aberration corrected scanning transmission electron microscopy (STEM). However, the wealth of quantitative information contained in the fine details of atomic structure or spectra remains largely unexplored. In this talk, I will present the new opportunities enabled by physics-informed big data and machine learning technologies to extract physical information from static and dynamic STEM images. The deep learning models trained on theoretically simulated images or labeled library data demonstrate extremely high efficiency in extracting atomic coordinates and trajectories, converting massive volumes of statistical and dynamic data into structural descriptors. I further present a method to take advantage of atomic-scale observations of chemical and structural fluctuations and use them to build a generative model (including near-neighbor interactions) that can be used to predict the phase diagram of the system in a finite temperature and composition space. Similar approach is applied to probe the kinetics of solid-state reactions on a single defect level and defect formation in solids via atomic-scale observations. Finally, synergy of deep learning image analytics and real-time feedback further allows harnessing beam-induced atomic and bond dynamics to enable direct atom-by-atom fabrication. Examples of direct atomic motion over mesoscopic distances, engineered doping at selected lattice site, and assembly of multiatomic structures will be demonstrated. These advances position STEM towards transition from purely imaging tool for atomic-scale laboratory of electronic, phonon, and quantum phenomena in atomically-engineered structures.