Yanping Huang

Learn More
Predictive coding is a unifying framework for understanding redundancy reduction and efficient coding in the nervous system. By transmitting only the unpredicted portions of an incoming sensory signal, predictive coding allows the nervous system to reduce redundancy and make full use of the limited dynamic range of neurons. Starting with the hypothesis of(More)
We propose a spiking network model capable of performing both approximate inference and learning for any hidden Markov model. The lower layer sensory neurons detect noisy measurements of hidden world states. The higher layer neurons with recurrent connections infer a posterior distribution over world states from spike trains generated by sensory neurons. We(More)
Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior(More)
BACKGROUND AND OBJECTIVE To demonstrate the feasibility of using a 1,050-nm swept-source optical coherence tomography (SS-OCT) system to achieve noninvasive retinal vasculature imaging in human eyes. MATERIALS AND METHODS Volumetric data sets were acquired using a 1-µm SS-OCT prototype that operated at a 100-kHz A-line rate. A scanning protocol designed(More)
BACKGROUND AND OBJECTIVE To evaluate the central macular microvascular network in patients with macular telangiectasia type 2 (MacTel2) using optical coherence tomography (OCT)-based microangiography (OMAG). PATIENTS AND METHODS Prospective, observational study of patients with MacTel2 evaluated using a swept-source OCT (SS-OCT) prototype. OMAG was(More)
How does the brain combine prior knowledge with sensory evidence when making decisions under uncertainty? Two competing descriptive models have been proposed based on experimental data. The first posits an additive offset to a decision variable, implying a static effect of the prior. However, this model is inconsistent with recent data from a motion(More)
A key problem in neuroscience is understanding how the brain makes decisions under uncertainty. Important insights have been gained using tasks such as the random dots motion discrimination task in which the subject makes decisions based on noisy stimuli. A descriptive model known as the drift diffusion model has previously been used to explain psychometric(More)
In this paper we present ChirpCast, a system for broadcasting network access keys to laptops ultrasonically. This work explores several modulation techniques for sending and receiving data using sound waves through commodity speakers and built-in laptop microphones. Requiring only that laptop users run a small application, the system successfully provides(More)
We report a newly developed multifunctional 1050 nm spectral domain optical coherence tomography (SD-OCT) system working at 147 kHz A-scan rate for posterior eye imaging. It is demonstrated through in-vivo experiments that this system delivers not only superior performance of posterior eye structural imaging but also detailed visualization of(More)