Abstract: The development of electronic devices such as microelectrode arrays (MEAs), used to record extracellularly simultaneous electrical activity of large populations of neurons is blooming. To enhance the quality of the recordings, the use of electrode made o f conducting polymer such as PEDOT has recently emerged for optimizing the performance of microelectrodes due to its mixed ionic electronic conduction, biocompatibility and low impedance. However, the extent to which these new interfaces can help the alg orithmic pipelines of spike sorting, i.e. turning extracellular potentials into individual spike trains remains unexplored. To address this issue, we checked if the physical positions of the neurons could be reliably inferred from extracellular electrica l recordings obtained by MEAs, and thus be exploited by downstreams spike sorting algorithms. To do so, we combine high resolution images of neuronal tissues and dense recordings performed via high performant electropolymerized electrodes based MEAs. Fir stly, we report the use of EDOT electropolymerization to tune post-fabrication material and geometrical parameters of passive microelectrodes. The process optimizes the cell/electrode interface by decreasing its impedance and improving its affinity with neurons: results demonstrate a better biocompatibility and improved signal-to-noise ratio (SNR) (up to 40 dB). Thanks to the higher SNR, we were able to detect more cells in comparison with gold electrodes from the same neural network by using spike sort ing. Hence, the higher number of cells detected will lead into more accurate analysis of the localization of the active neurons on top of MEA. Secondly, by using these high performant MEAs, we investigated the possibility to accurately estimate the posit ions of the neurons solely from extracellular recordings by studying the correlation between electrical activity (obtained via spike sorting), optical imaging (Fluorescent) and Scanning Electron Microscopy (SEM) of neural networks cultured on MEAs. By us ing the SpykingCircus software to spike sort the extracellular recordings, we estimated the positions of the neurons either by using the center of mass of their electrical signatures, or by inferring the positions assuming cells would behave as monopoles . By superposition of the fluorescent and the SEM images, we compared the observed physical positions of the neurons with the ones predicted by the two aforementioned methods. This approach showed the high accuracy of the monopole hypothesis compared to the center of mass. In this work, we showed how the use of a material engineering technique for optimizing state of art MEAs can enhance the quality of the recordings. Hence, the correlation of these high quality recordings with optical imaging paves the way towards new algorithmic possibilities for spike sorting algorithms that could make use of a more reliable estimation of neuronal positions.