TY - JOUR AU - Fiáth, Richárd AU - Márton, AL AU - Mátyás, Ferenc AU - Pinke, Domonkos Péter AU - Márton, Gergely AU - Tóth, Kinga AU - Ulbert, István TI - Slow insertion of silicon probes improves the quality of acute neuronal recordings JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 9 PY - 2019 IS - 1 PG - 17 SN - 2045-2322 DO - 10.1038/s41598-018-36816-z UR - https://m2.mtmt.hu/api/publication/30322282 ID - 30322282 LA - English DB - MTMT ER - TY - JOUR AU - Fiáth, Richárd AU - Raducanu, BC AU - Musa, S AU - Andrei, A AU - Lopez, CM AU - van Hoof, C AU - Ruther, P AU - Aarts, A AU - Horváth, Domonkos AU - Ulbert, István TI - A silicon-based neural probe with densely-packed low-impedance titanium nitride microelectrodes for ultrahigh-resolution in vivo recordings JF - BIOSENSORS & BIOELECTRONICS J2 - BIOSENS BIOELECTRON VL - 106 PY - 2018 SP - 86 EP - 92 PG - 7 SN - 0956-5663 DO - 10.1016/j.bios.2018.01.060 UR - https://m2.mtmt.hu/api/publication/3326632 ID - 3326632 N1 - Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2, Budapest, H-1117, Hungary Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/A, Budapest, H-1083, Hungary Interuniversity Microelectronics Center (IMEC), Kapeldreef 75, Heverlee, B-3001, Belgium Electrical Engineering Department (ESAT), KU Leuven, Kasteelpark Arenberg 10, Leuven, B-3001, Belgium Microsystem Materials Laboratory, Department of Microsystems Engineering (IMTEK), University of Freiburg, Georges-Koehler-Allee 103, Freiburg, D-79110, Germany BrainLinks-BrainTools Cluster of Excellence at the University of Freiburg, Georges-Koehler-Allee 80, Freiburg, D-79110, Germany ATLAS Neuroengineering, Kapeldreef 75, Leuven, B-3000, Belgium Cited By :23 Export Date: 23 February 2021 CODEN: BBIOE Correspondence Address: Fiáth, R.; Institute of Cognitive Neuroscience and Psychology, Magyar tudósok körútja 2, Hungary; email: fiath.richard@ttk.mta.hu AB - In this study, we developed and validated a single-shank silicon-based neural probe with 128 closely-packed microelectrodes suitable for high-resolution extracellular recordings. The 8-mm-long, 100-mu m-wide and 50-mu m-thick implantable shank of the probe fabricated using a 013-mu m complementary metal-oxide-semiconductor (CMOS) metallization technology contains square-shaped (20 x 20 mu m(2)), low-impedance (similar to 50 k Omega at 1 kHz) recording sites made of rough and porous titanium nitride which are arranged in a 32 x 4 dense array with an inter-electrode pitch of 22.5 mu m. The electrophysiological performance of the probe was tested in in vivo experiments by implanting it acutely into neocortical areas of anesthetized animals (rats, mice and cats). We recorded local field potentials, single- and multi-unit activity with superior quality from all layers of the neocortex of the three animal models, even after reusing the probe in multiple (> 10) experiments. The low-impedance electrodes monitored spiking activity with high signal-to-noise ratio; the peak-to-peak amplitude of extracellularly recorded action potentials of well-separable neurons ranged from 0.1 mV up to 1.1 mV. The high spatial sampling of neuronal activity made it possible to detect action potentials of the same neuron on multiple, adjacent recording sites, allowing a more reliable single unit isolation and the investigation of the spatio-temporal dynamics of extracellular action potential waveforms in greater detail. Moreover, the probe was developed with the specific goal to use it as a tool for the validation of electrophysiological data recorded with high-channel-count, high-density neural probes comprising integrated CMOS circuitry. LA - English DB - MTMT ER - TY - JOUR AU - Márton, Gergely AU - Orbán, Gábor AU - Kiss, Marcell AU - Fiáth, Richárd AU - Pongrácz, Anita AU - Ulbert, István TI - A Multimodal, SU-8-Platinum - Polyimide Microelectrode Array for Chronic In Vivo Neurophysiology JF - PLOS ONE J2 - PLOS ONE VL - 10 PY - 2015 IS - 12 PG - 16 SN - 1932-6203 DO - 10.1371/journal.pone.0145307 UR - https://m2.mtmt.hu/api/publication/3010856 ID - 3010856 N1 - Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2, Budapest, H-1117, Hungary Department of Microtechnology, Institute for Technical Physics and Materials Science, Centre for Energy Research, Konkoly Thege M. út. 29-33, Budapest, H-1121, Hungary School of Ph. D. Studies, Semmelweis University, Ü lloi út 26, Budapest, H-1085, Hungary Department of Electron Devices, Budapest University of Technology and Economics, Magyar tudósok körútja 2, Budapest, H-1117, Hungary Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/a, Budapest, H-1083, Hungary Cited By :29 Export Date: 30 October 2024 CODEN: POLNC Correspondence Address: Márton, G.; Institute of Cognitive Neuroscience and Psychology, Magyar tudósok körútja 2, Hungary; email: marton.gergely@ttk.mta.hu LA - English DB - MTMT ER - TY - JOUR AU - Quiroga, RQ AU - Nádasdy, Zoltán AU - Ben-Shaul, Y TI - Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering JF - NEURAL COMPUTATION J2 - NEURAL COMPUT VL - 16 PY - 2004 IS - 8 SP - 1661 EP - 1687 PG - 27 SN - 0899-7667 DO - 10.1162/089976604774201631 UR - https://m2.mtmt.hu/api/publication/1960292 ID - 1960292 N1 - Z9: 188 AB - This study introduces a new method for detecting and sorting spikes from multiunit recordings. The method combines the wavelet transform, which localizes distinctive spike features, with superparamagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions. Moreover, an improved method for setting amplitude thresholds for spike detection is proposed. We describe several criteria for implementation that render the algorithm unsupervised and fast. The algorithm is compared to other conventional methods using several simulated data sets whose characteristics closely resemble those of in vivo recordings. For these data sets, we found that the proposed algorithm outperformed conventional methods. LA - English DB - MTMT ER -