Integrated plasmonic circuitry on a vertical-cavity surface-emitting semiconductor laser platform

Integrated plasmonic sources and detectors are imperative in the practical development of plasmonic circuitry for bio- and chemical sensing, nanoscale optical information processing, as well as transducers for high-density optical data storage. Here we show that vertical-cavity surface-emitting lasers (VCSELs) can be employed as an on-chip, electrically pumped source or detector of plasmonic signals, when operated in forward or reverse bias, respectively. To this end, we experimentally demonstrate surface plasmon polariton excitation, waveguiding, frequency conversion and detection on a VCSEL-based plasmonic platform. The coupling efficiency of the VCSEL emission to waveguided surface plasmon polariton modes has been optimized using asymmetric plasmonic nanostructures. The plasmonic VCSEL platform validated here is a viable solution for practical realizations of plasmonic functionalities for various applications, such as those requiring sub-wavelength field confinement, refractive index sensitivity or optical near-field transduction with electrically driven sources, thus enabling the realization of on-chip optical communication and lab-on-a-chip devices

Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays

An emerging generation of high-density microelectrode arrays (MEAs) is now capable of recording spiking activity simultaneously from thousands of neurons with closely spaced electrodes. Reliable spike detection and analysis in such recordings is challenging due to the large amount of raw data, and the dense sampling of spikes with closely spaced electrodes.Here, we present a highly efficient, online capable spike detection algorithm, and an offline method with improved detection rates, which enables estimation of spatial event locations at a resolution higher than that provided by the array by combining information from multiple electrodes. Data acquired with a 4,096 channel MEA from neuronal cultures and the neonatal retina, as well as synthetic data was used to test and validate these methods.We demonstrate that these algorithms outperform conventional methods due to a better noise estimate and an improved signal-to-noise ratio through combining information from multiple electrodes. Finally, we present a new approach for analyzing population activity based on the characterization of the spatio-temporal event profile, which does not require the isolation of single units.Overall, we show how the improved spatial resolution provided by high density, large scale microelectrode arrays can be reliably exploited to characterize activity from large neural populations and brain circuits