The November 2016 UWIN seminar features a pair of short talks by Ione Fine and Josh Smith:
- “From pulse to percept: Modeling the perceptual experience of bionic vision”: Ione Fine, Professor, Department of Psychology, University of Washington
- “Battery-free wireless cameras: A platform for neurally inspired information processing research?”: Josh Smith, Associate Professor, Departments of Computer Science & Engineering and Electrical Engineering, University of Washington
The seminar is on Wednesday, November 9th, at 3:30pm in Health Sciences Building K-069. Click here for a map of the Health Sciences Building. The K-wing is west of the Rotunda Cafe (I-Court).
“From pulse to percept: modeling the perceptual experience of bionic vision” (Ione Fine):
The field of bionic vision is making rapid progress; with three electronic implants approved for patients, and several others in development. However it is still very unclear what patients implanted with retinal prosthetics actually ‘see’. Here we present a full simulation of perceptual experience produced by the Second Sight Medical products epiretinal prosthesis, thereby creating a ‘virtual patient’. We show that the performance of this virtual patient is closely matched to data from real patients. Simulations such as these are likely to be critical for providing realistic estimates of prosthetic vision, providing regulatory bodies with guidance into what sort of visual tests are appropriate for evaluating prosthetic performance, and improving current and future technology.
“Battery-free wireless cameras: A platform for neurally inspired information processing research?” (Josh Smith):
I will present our lab’s most recent work on battery-free wireless cameras, which can perform simple image processing operations using local computation and which communicate using backscatter, a very low power wireless technique. Visual processing can also be studied in biological systems. Neural information processing is in general more energy efficient than conventional micro-electronic information processing. Using this platform, we can potentially make task-level comparisons of the energy efficiency of neural and microelectronic visual information processing. Taking inspiration from biology, can we find more energy efficient architectures for micro-electronic information processing?