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In this paper we present a probabilistic fast model for performance assessment of geothermal doublets for direct heat applications. It is a simple yet versatile and multipurpose tool. It can be well applied in better understanding the sensitivity of performance to key subsurface parameters and depth trends therein, and for assessing the probability of success for geothermal projects under technical and financial constraints.The underlying algorithms deliver a sensible accuracy given the uncertainties associated with geothermal projects at exploration state. A public release of the software, available under the name of DoubletCalc, is easy to handle and requires a limited set of input parameters. Thanks to an open source code, DoubletCalc can be implemented in other software applications and extended as it has been implemented for the integration into the national geothermal information system in the Netherlands (ThermoGIS, 2011).Apart from its application for site assessments, the tool can be integrated into automated workflows processing faster representations of key aquifer properties and capable to produce indicative maps for predicted doublet power, economic feasibility and prediction of cumulative amount of heat that can be recovered. These capabilities are specifically important for decision support for policymakers while assessing the effects of particular insurance schemes and funding mechanisms.DoubletCalc cannot and is not intended to substitute geologic exploration approaches. As exploration measures, such as seismic surveys are cost intensive, DoubletCalc can be used to focus geothermal exploration on areas and sites where an enhanced probability of success can be expected.

Tools to estimate brain connectivity offer the potential to enhance our understanding of brain functioning. The behavior of neuronal networks, including functional connectivity and induced connectivity changes by external stimuli, can be studied using models of cultured neurons. Cultured neurons tend to be active in groups, and pairs of neurons are said to be functionally connected when their firing patterns show significant synchronicity. Methods to infer functional connections are often based on pair-wise cross-correlation between activity patterns of (small groups of) neurons. However, these methods are not very sensitive to detect inhibitory connections, and they were not designed for use during stimulation. Maximum Entropy (MaxEnt) models may provide a conceptually different method to infer functional connectivity. They have the potential benefit to estimate functional connectivity during stimulation, and to infer excitatory as well as inhibitory connections. MaxEnt models do not involve pairwise comparison, but aim to capture probability distributions of sets of neurons that are synchronously active in discrete time bins. We used electrophysiological recordings from in vitro neuronal cultures on micro electrode arrays to investigate the ability of MaxEnt models to infer functional connectivity. Connectivity estimates provided by MaxEnt models correlated well with those obtained by conditional firing probabilities (CFP), an established cross-correlation based method. In addition, stimulus-induced connectivity changes were detected by MaxEnt models, and were of the same magnitude as those detected by CFP. Thus, MaxEnt models provide a potentially powerful new tool to study functional connectivity in neuronal networks.

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