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.

Abstract: The development of electronic devices for neurosensing is leading to fundamental discoveries in communication setups for interfacing and computing the brain's electrical activity that is still a demanding task in the 21st century. One of the greatest cha llenges for efficient neurosensing is to ensure that detection/transduction between biochemically rich systems and tools is fully mastered to reliably gather relevant information. In extracellular devices such as microelectrode arrays (MEAs), the discord ance lies at the interface between ions and the electrodes. Engineering chemically/morphologically the electrode's materials by decreasing its impedance, improving its affinity with neurons, and boosting its biocompatibility ensures better cell/electrode interface conditions to find the right materials that detect ionic signals from neurons and transduce them into electronic signals with the lowest information loss. Hence, the use of conducting polymers (PEDOT) has emerged for optimizing the performance of microelectrodes in neurosensing due to its mixed ionic electronic conduction, biocompatibility and low impedance. In parallel to the development of passive microelectrode, organic electrochemical transistors (OECTs) have received lots of attention in the biosensing field since they exhibit high coupling with cells and signal amplification. Notably, the transconductance represents an important parameter that depends on geometrical and material parameters that rules largely OECTs performances in biose nsing. In this direction, we explore the use of EDOT electropolymerization to tune post-fabrication material and geometrical parameters of passive microelectrodes for optimizing the cell/electrode interface by decreasing its impedance and improving its a ffinity with neurons (increasing the resistance "Rseal" that represents the cell/electrode cleft). For electropolymerized PEDOT MEAs, we demonstrate long term and stable extracellular recording of primary cortical neurons with a record signal-to-noise ra tio (SNR) up to 37 dB (with ultra-low noise down to 2.1 μV RMS). Secondly, for active sensing with OECTs, this strategy exploits the concept of adaptive sensing where both transconductance and impedance are tuned simultaneously or independently. This approach shows an improvement of OECTs transconductance by 150-percent, volumetric capacitance by 300-percent, and a reduction in array's variability by 60-percent in comparison with standard spin-coated OECTs. The cytotoxicity of the electropolymerized EDOT was assessed for primary neural cells culture and no detrimental effect of electropolymerized EDOT on cell viability was observed. To extract the impedance and transconductance values for both MEAs and OECTs, we combine DC electrical measurements w ith electrochemical impedance spectroscopy (EIS). To show the cell/electrode morphology and neurite outgrowth to electropolymerized microelectrodes, Scanning Electron Microscopy (SEM) was performed. To correlate the morphological changes of the material with the enhancement of its electrical and electrochemical performances, Atomic Force Microscopy (AFM) in liquid and Raman Spectroscopy were achieved. Finally, in-vitro extracellular recorded signals from entorhinal cortex cultured slices and primary cor tical neurons using both MEAs and OECTs are presented. The key novelty of this technique is to propose a post-fabrication material engineering technique that can be used to optimize both passive (MEAs) and active (OECTs) devices for extracellular recordi ng and promote new exploratory sensing strategies to ensure high quality neurosensing alternatives.