High brightness, highly directional organic light-emitting diodes as light sources for future light-amplifying prosthetics in the optogenetic management of vision loss

Funding: Engineering and Physical Sciences Research Council (Grant Number(s): EP/R010595/1). National Science Foundation (Grant Number(s): 1706207). Defense Sciences Office, DARPA (Grant Number(s): N66001-17-C-4012). Leverhulme Trust (Grant Number(s): RPG-2017-231). Alexander von Humboldt-Stiftung (Grant Number(s): Humboldt Professur). National Research Foundation of Korea (GrantNumber(s): 2017R1A6A3A03012331). China Sponsorship Council.Optogenetic control of retinal cells transduced with light-sensitive channelrhodopsins can enable restoration of visual perception in patients with vision loss. However, a light intensity orders of magnitude higher than ambient light conditions is required to achieve robust cell activation. Relatively bulky wearable light amplifiers are currently used to deliver sufficient photon flux (>1016 photons/cm2/s in a ±10° emission cone) at a suitable wavelength (e.g., 600 nm for channelrhodopsin ChrimsonR). Here, ultrahigh brightness organic light-emitting diodes (OLEDs) with highly directional emission are developed, with the ultimate aim of providing high-resolution optogenetic control of thousands of retinal cells in parallel from a compact device. The orange-emitting phosphorescent OLEDs use doped charge transport layers, generate narrowband emission peaking at 600 nm, and achieve a luminance of 684 000 cd m–2 at 15 V forward bias. In addition, tandem-stack OLEDs with a luminance of 1 152 000 cd m–2 and doubled quantum efficiency are demonstrated, which greatly reduces electrical and thermal stress in these devices. At the photon flux required to trigger robust neuron firing in genetically modified retinal cells and when using heat sinking and realistic duty cycles (20% at 12.5 Hz), the tandem-stack OLEDs therefore show a greatly improved half-brightness lifetime of 800 h.Publisher PDFPeer reviewe

Performance Comparison of Time-Step-Driven versus Event-Driven Neural State Update Approaches in SpiNNaker

The SpiNNaker chip is a multi-core processor optimized for neuromorphic applications. Many SpiNNaker chips are assembled to make a highly parallel million core platform. This system can be used for simulation of a large number of neurons in real-time. SpiNNaker is using a general purpose ARM processor that gives a high amount of flexibility to implement different methods for processing spikes. Various libraries and packages are provided to translate a high-level description of Spiking Neural Networks (SNN) to low-level machine language that can be used in the ARM processors. In this paper, we introduce and compare three different methods to implement this intermediate layer of abstraction. We have examined the advantages of each method by various criteria, which can be useful for professional users to choose between them. All the codes that are used in this paper are available for academic propose.EU H2020 grant 644096 ECOMODEEU H2020 grant 687299 NEURAM3Ministry of Economy and Competitivity (Spain) / European Regional Development Fund TEC2015-63884-C2-1-P (COGNET