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Thomas Pfeiffer

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers
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3

AAMAS Conference 2024 Conference Paper

Decision Market Based Learning for Multi-agent Contextual Bandit Problems

  • Wenlong Wang
  • Thomas Pfeiffer

Information is often stored in a distributed and proprietary form, and agents who own this information are often self-interested and require incentives to reveal it. Suitable mechanisms are required to elicit and aggregate such distributed information for decisionmaking. In this study, we use simulations to investigate the use of decision markets as mechanisms in a multi-agent learning system to aggregate distributed information for decision-making in a contextual bandit problem.

YNIMG Journal 2014 Journal Article

Optimized temporally deconvolved Ca 2+ imaging allows identification of spatiotemporal activity patterns of CA1 hippocampal ensembles

  • Thomas Pfeiffer
  • Andreas Draguhn
  • Susanne Reichinnek
  • Martin Both

Hippocampal activity is characterized by the coordinated firing of a subset of neurons. Such neuronal ensembles can either be driven by external stimuli to form new memory traces or be reactivated by intrinsic mechanisms to reactivate and consolidate old memories. Hippocampal network oscillations orchestrate this coherent activity. One key question is how the topology, i. e. the functional connectivity of neuronal networks supports their desired function. Recently, this has been addressed by characterizing the intrinsic properties for the highly recurrently connected CA3 region using organotypic slice cultures and Ca2+ imaging. In the present study, we aimed to determine the properties of CA1 hippocampal ensembles at high temporal and multiple single cell resolution. Thus, we performed Ca2+ imaging using the chemical fluorescent Ca2+ indicator Oregon Green BAPTA 1-AM. To achieve most physiological conditions, we used acute hippocampal slices that were recorded in a so-called interface chamber. To faithfully reconstruct firing patterns of multiple neurons in the field of view, we optimized deconvolution-based detection of action potential associated Ca2+ events. Our approach outperformed currently available detection algorithms by its sensitivity and robustness. In combination with advanced network analysis, we found that acute hippocampal slices contain a median of 11 CA1 neuronal ensembles with a median size of 4 neurons. This apparently low number of neurons is likely due to the confocal imaging acquisition and therefore yields a lower limit. The distribution of ensemble sizes was compatible with a scale-free topology, as far as can be judged from data with small cell numbers. Interestingly, cells were more tightly clustered in large ensembles than in smaller groups. Together, our data show that spatiotemporal activity patterns of hippocampal neuronal ensembles can be reliably detected with deconvolution-based imaging techniques in mouse hippocampal slices. The here presented techniques are fully applicable to similar studies of distributed optical measurements of neuronal activity (in vivo), where signal-to-noise ratio is critical.

AAAI Conference 2012 Conference Paper

Adaptive Polling for Information Aggregation

  • Thomas Pfeiffer
  • Xi Gao
  • Yiling Chen
  • Andrew Mao
  • David Rand

The flourishing of online labor markets such as Amazon Mechanical Turk (MTurk) makes it easy to recruit many workers for solving small tasks. We study whether information elicitation and aggregation over a combinatorial space can be achieved by integrating small pieces of potentially imprecise information, gathered from a large number of workers through simple, one-shot interactions in an online labor market. We consider the setting of predicting the ranking of n competing candidates, each having a hidden underlying strength parameter. At each step, our method estimates the strength parameters from the collected pairwise comparison data and adaptively chooses another pairwise comparison question for the next recruited worker. Through an MTurk experiment, we show that the adaptive method effectively elicits and aggregates information, outperforming a naı̈ve method using a random pairwise comparison question at each step.

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