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)

High-Throughput Detection and Functional Classification of Retinal Ganglion Cells using High-Density Microelectrode Arrays

This thesis presents a label-free method to detect and func- tionally characterize hundreds of retinal ganglion cells (RGC) simultaneously. The approach relies on measuring the extra- cellular action potentials of RGCs using high-density micro- electrode arrays (HD-MEA) while projecting light stimuli onto the photoreceptor layer of ex vivo retinae. Our HD-MEA system features an electrode density similar to the density of mouse RGCs (~3300 cells/mm2) and can record voltage signals from 1024 electrodes in parallel. This way, we could, in principle, detect all RGCs in a given area of the retina. We developed an automatic spike-sorter that coped with the high dimensionality of the recorded data by grouping the elec- trodes of the array into subsets of nine electrodes and then using standard spike-sorting techniques for each group inde- pendently. Tests with surrogate ground-truth data showed that the developed spike-sorter reliably extracted single-cell spiking activities whilst avoiding labor-intensive manual curation of the results. An existing experimental setup, which had been built for a smaller HD-MEA system, was modified to accommodate a larger recording and light-stimulation area. Most standard light stimuli are not suited for evoking strong and diverse light responses when scaled over areas spanning lateral dimensions of several hundred micrometers and covering hundreds of RGCs. Many RGCs respond preferably to small features within their receptive fields and are inhibited by large-scale contrast and light intensity changes. We developed a method that used stimuli consisting of small objects moving on random trajectories across the stimulation area to infer diverse response characteristics of individual RGCs. This way, we could localize more than twice as many RGCs and their receptive fields in comparison to a standard white-noise stimulus in the same time interval. Furthermore, we were able to distinguish between ON-, OFF-, and ON-OFF- responsive cells, and characterize direction-selective cells. 5 Finally, we were able to use the HD-MEA setup to record activity in human retinae obtained through multi-organ dona- tions. We successfully measured light responses in more than 80% of the recorded RGCs and, thus, demonstrated that the tis- sue was healthy at the time of recording. This finding opens the possibility for further studies, in which we hope to employ the methods presented in this thesis to functionally characterize large numbers of RGCs in human and primate retinae

Effective benchmarking of high-density MEA spike-sorters

ISSN:1662-510