Andreas Hoenselaar

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Understanding the mechanisms of learning requires characterizing how the response properties of individual neurons and interactions across populations of neurons change over time. To study learning in vivo, we need the ability to track an electrophysiological signature that uniquely identifies each recorded neuron for extended periods of time. We have(More)
The rise of big data in modern research poses serious challenges for data management: Large and intricate datasets from diverse instrumentation must be precisely aligned, annotated, and processed in a variety of ways to extract new insights. While high levels of data integrity are expected, research teams have diverse backgrounds, are geographically(More)
The forced swimming test of rats or mice is a frequently used behavioral test to evaluate compounds for antidepressant activity in vivo. The aim of this study was to replace the human observer, needed to score and analyze the behavior of animals, by a fully automated method. For this purpose, in a first step from a video recording of each rat, an activity(More)
This paper gives a constructive proof for the existence of correlated equilibria in multi-player games and shows how elements of the proof can be used for the computation of correlated equilibria in polynomial time for an important subset of succinct games. None of this work is novel, it merely aims at a more detailed and approachable description of the(More)
This work is concerned with the applicability of mutual information as a measure of relevance in neural coding. We give a detailed overview of common approaches to the application of information-theoretic measures in neuroscience. In order to create an environment that allows for the analysis of mutual information, we generate synthetic spike trains based(More)
Tolias AS, Ecker AS, Siapas AG, Hoenselaar A, Keliris GA, Logothetis NK. Recording chronically from the same neurons in awake, behaving primates. J Neurophysiol 98: 3780–3790, 2007. First published October 17, 2007; doi:10.1152/jn.00260.2007. Understanding the mechanisms of learning requires characterizing how the response properties of individual neurons(More)
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