Fabian Kloosterman

Dr. Fabian Kloosterman


Research Focus & Metrics

Academic Career Length

27Years (2000-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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16 April 2021 in Science
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NSNick Steinmetz
Nick Steinmetz
CACagatay Aydin
Cagatay Aydin
ALAnna Lebedeva
Anna Lebedeva
MOMichael Okun
Michael Okun
MPMarius Pachitariu
Marius Pachitariu
THTimothy D. Harris
Timothy D. Harris
Recording many neurons for a long time: The ultimate aim of chronic recordings is to sample from the same neuron over days and weeks. However, this goal has been difficult to achieve for large populations of neurons. Steinmetz et al. describe the development and testing of Neuropixels 2.0. This new electrophysiological recording tool is a miniaturized, high-density probe for both acute and long-term experiments combined with sophisticated software algorithms for fully automatic post hoc computational stabilization. The technique also provides a strategy for extending the number of recorded sites beyond the number of available recording channels. In freely moving animals, extremely large numbers of individual neurons could thus be followed and tracked with the same probe for weeks and occasionally months. Science , this issue p. eabf4588

1 August 2009 in Neuron
Verified
Journal Article
Closed access
TDThomas J. Davidson
Thomas J. Davidson
FKFabian Kloosterman
Fabian Kloosterman
MWMatthew A. Wilson
Matthew A. Wilson

14 October 2015 in eLife
Verified
Journal Article
Open access
SGStephen N Gomperts
Stephen N Gomperts
FKFabian Kloosterman
Fabian Kloosterman
MWMatthew A Wilson
Matthew A Wilson
Spatial learning requires the hippocampus, and the replay of spatial sequences during hippocampal sharp wave-ripple (SPW-R) events of quiet wakefulness and sleep is believed to play a crucial role. To test whether the coordination of VTA reward prediction error signals with these replayed spatial sequences could contribute to this process, we recorded from neuronal ensembles of the hippocampus and VTA as rats performed appetitive spatial tasks and subsequently slept. We found that many reward responsive (RR) VTA neurons coordinated with quiet wakefulness-associated hippocampal SPW-R events that replayed recent experience. In contrast, coordination between RR neurons and SPW-R events in subsequent slow wave sleep was diminished. Together, these results indicate distinct contributions of VTA reinforcement activity associated with hippocampal spatial replay to the processing of wake and SWS-associated spatial memory.

19 October 2017 in Sensors
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Journal Article
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BRBogdan C. Raducanu
Bogdan C. Raducanu
RYRefet F. Yazicioglu
Refet F. Yazicioglu
CLCarolina Mora Lopez
Carolina Mora Lopez
MBMarco Ballini
Marco Ballini
JPJan Putzeys
Jan Putzeys
SMSrinjoy Mitra
Srinjoy Mitra
We present a high electrode density and high channel count CMOS (complementary metal-oxide-semiconductor) active neural probe containing 1344 neuron sized recording pixels (20 µm × 20 µm) and 12 reference pixels (20 µm × 80 µm), densely packed on a 50 µm thick, 100 µm wide, and 8 mm long shank. The active electrodes or pixels consist of dedicated in-situ circuits for signal source amplification, which are directly located under each electrode. The probe supports the simultaneous recording of all 1356 electrodes with sufficient signal to noise ratio for typical neuroscience applications. For enhanced performance, further noise reduction can be achieved while using half of the electrodes (678). Both of these numbers considerably surpass the state-of-the art active neural probes in both electrode count and number of recording channels. The measured input referred noise in the action potential band is 12.4 µVrms, while using 678 electrodes, with just 3 µW power dissipation per pixel and 45 µW per read-out channel (including data transmission).

1 January 2014 in Journal of Neurophysiology
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FKFabian Kloosterman
Fabian Kloosterman
SLStuart P. Layton
Stuart P. Layton
ZCZhe Chen
Zhe Chen
MWMatthew A. Wilson
Matthew A. Wilson
A fundamental task in neuroscience is to understand how neural ensembles represent information. Population decoding is a useful tool to extract information from neuronal populations based on the ensemble spiking activity. We propose a novel Bayesian decoding paradigm to decode unsorted spikes in the rat hippocampus. Our approach uses a direct mapping between spike waveform features and covariates of interest and avoids accumulation of spike sorting errors. Our decoding paradigm is nonparametric, encoding model-free for representing stimuli, and extracts information from all available spikes and their waveform features. We apply the proposed Bayesian decoding algorithm to a position reconstruction task for freely behaving rats based on tetrode recordings of rat hippocampal neuronal activity. Our detailed decoding analyses demonstrate that our approach is efficient and better utilizes the available information in the nonsortable hash than the standard sorting-based decoding algorithm. Our approach can be adapted to an online encoding/decoding framework for applications that require real-time decoding, such as brain-machine interfaces.

20 April 2009 in Journal of Visualized Experiments
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Journal Article
Open access
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FKFabian Kloosterman
Fabian Kloosterman
TDThomas J. Davidson
Thomas J. Davidson
SGStephen N. Gomperts
Stephen N. Gomperts
SLStuart P. Layton
Stuart P. Layton
GHGregory Hale
Gregory Hale
MWMatthew A. Wilson
Matthew A. Wilson
In this protocol we demonstrate how to fabricate a micro-drive array for chronic electrophysiological recordings in rats.