Aaron rutten9/4/2023 ![]() ![]() The device can be delivered through a percutaneous catheter and leverages magnetoelectric materials to receive data and power through tissue via a digitally programmable 1 mm × 0.8 mm system-on-a-chip. ![]() Here we report the design and in vivo proof-of-concept testing of an endovascular wireless and battery-free millimetric implant for the stimulation of specific peripheral nerves that are difficult to reach via traditional surgeries. However, for many nerve targets, this requires invasive surgeries and the implantation of bulky devices (about a few centimetres in at least one dimension). Implantable bioelectronic devices for the simulation of peripheral nerves could be used to treat disorders that are resistant to traditional pharmacological therapies. DFINE can both enhance future neurotechnology and facilitate investigations across diverse domains of neuroscience. Further, despite enabling flexible inference unlike prior neural network models of population activity, DFINE also better predicts the behavior and neural activity, and better captures the latent neural manifold structure. We show that DFINE achieves flexible nonlinear inference across diverse behaviors and brain regions. ![]() We address this challenge by developing DFINE, a new neural network that separates the model into dynamic and manifold latent factors, such that the dynamics can be modeled in tractable form. A major unaddressed challenge is to model this nonlinear structure, but in a manner that allows for flexible inference, whether causally, non-causally, or in the presence of missing neural observations. These activity patterns are noisy observations of lower-dimensional latent factors and their nonlinear dynamical structure. Inferring complex spatiotemporal dynamics in neural population activity is critical for investigating neural mechanisms and developing neurotechnology. ![]()
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