Extracellular stimulation system for the modification of network parameters in cultured neural networks

Este proyecto se centra en el uso de dispositivos de microelectrodos MEAs (Multi Electrode Arrays) de última generación para el estudio y la manipulación de redes neuronales en cultivo. Chips MEA, con 26400 electrodos situados en una superficie de 3.85x2.10mm^2, fueron utilizados para registrar la actividad eléctrica de dos cultivos de neuronas corticales disociadas obtenidas de embriones de rata. En las mismas plataformas MEA, se implementó un protocolo de estimulación en bucle cerrado, de manera que se pudieran enviar pulsos eléctricos de estimulación a determinados electrodos en respues a potenciales de acción detectados en otro electrodo. Uno de los cultivos de neuronas fue sometido al protocolo de estimulación en bucle cerrado mientras que el segundo cultivo fue utilizado como control. Se desarrollaron diferentes métodos con el fin de hacer una caracterización funcional de los cultivos. El análisis funcional de los registros obtenidos en los experimentos indican que la estimulación en bucle cerrado provocó perdidas significativas y generalizadas de actividad y conectividad en la red neuronal en cultivo

The potential of microelectrode arrays and microelectronics for biomedical research and diagnostics

Planar microelectrode arrays (MEAs) are devices that can be used in biomedical and basic in vitro research to provide extracellular electrophysiological information about biological systems at high spatial and temporal resolution. Complementary metal oxide semiconductor (CMOS) is a technology with which MEAs can be produced on a microscale featuring high spatial resolution and excellent signal-to-noise characteristics. CMOS MEAs are specialized for the analysis of complete electrogenic cellular networks at the cellular or subcellular level in dissociated cultures, organotypic cultures, and acute tissue slices; they can also function as biosensors to detect biochemical events. Models of disease or the response of cellular networks to pharmacological compounds can be studied in vitro, allowing one to investigate pathologies, such as cardiac arrhythmias, memory impairment due to Alzheimer's disease, or vision impairment caused by ganglion cell degeneration in the retin

Single-Cell Electrical Stimulation Using CMOS-Based High-Density Microelectrode Arrays

Non-invasive electrical stimulation can be used to study and control neural activity in the brain or to alleviate somatosensory dysfunctions. One intriguing prospect is to precisely stimulate individual targeted neurons. Here, we investigated single-neuron current and voltage stimulation in vitro using high-density microelectrode arrays featuring 26,400 bidirectional electrodes at a pitch of 17.5 μm and an electrode area of 5 × 9 μm2. We determined optimal waveforms, amplitudes and durations for both stimulation modes. Owing to the high spatial resolution of our arrays and the close proximity of the electrodes to the respective neurons, we were able to stimulate the axon initial segments (AIS) with charges of less than 2 pC. This resulted in minimal artifact production and reliable readout of stimulation efficiency directly at the soma of the stimulated cell. Stimulation signals as low as 70 mV or 100 nA, with pulse durations as short as 18 μs, yielded measurable action potential initiation and propagation. We found that the required stimulation signal amplitudes decreased with cell growth and development and that stimulation efficiency did not improve at higher electric fields generated by simultaneous multi-electrode stimulation

How to Detect an Astrophysical Nanohertz Gravitational-Wave Background

Analysis of pulsar timing data have provided evidence for a stochastic gravitational wave background in the nHz frequency band. The most plausible source of such a background is the superposition of signals from millions of supermassive black hole binaries. The standard statistical techniques used to search for such a background and assess its significance make several simplifying assumptions, namely: i) Gaussianity; ii) isotropy; and most often iii) a power-law spectrum. However, a stochastic background from a finite collection of binaries does not exactly satisfy any of these assumptions. To understand the effect of these assumptions, we test standard analysis techniques on a large collection of realistic simulated datasets. The dataset length, observing schedule, and noise levels were chosen to emulate the NANOGrav 15-year dataset. Simulated signals from millions of binaries drawn from models based on the Illustris cosmological hydrodynamical simulation were added to the data. We find that the standard statistical methods perform remarkably well on these simulated datasets, despite their fundamental assumptions not being strictly met. They are able to achieve a confident detection of the background. However, even for a fixed set of astrophysical parameters, different realizations of the universe result in a large variance in the significance and recovered parameters of the background. We also find that the presence of loud individual binaries can bias the spectral recovery of the background if we do not account for them.Comment: 14 pages, 8 figure

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)