Code-specific learning rules improve action selection by populations of spiking neurons

Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

Linkage in narratives: A comparison between monolingual speakers of French and Italian, and early and late French-Italian bilinguals

In the course of language acquisition learners have to deal with the task of producing narrative texts that are coherent across a range of conceptual domains (space, time, entities) -- both within as well as across utterances. The organization of information is analyzed in this study, on the basis of retellings of a silent film, in terms of devices used in the coordination and subordination of events within the narrative sequence. The focus on subordination reflects a core grammatical difference between Italian and French, as Italian is a null-subject language while French is not. The implications of this contrast for information structure include differences in topic management within the sequence of events. The present study investigates in how far Italian-French bilingual speakers acquire the patterns of monolingual speakers of Italian. It compares how early and late bilinguals of these two languages proceed when linking information in narratives in Italian.

Integrated Segmentation of Brain Tumor Images for Radiotherapy and Neurosurgery

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

It’s all about networking! Empirical investigation of social capital formation on social network sites

As Social Network Sites (SNS) permeate our daily routines, the question whether participation results in value for SNS users becomes particularly acute. This study adopts a 'participation-source-outcome' perspective to explore how distinct uses of SNS generate various types of social capital benefits. Building on existing research, extensive qualitative findings and an empirical study with 253 Facebook users, we uncover the process of social capital formation on SNS. We find that even though active communication is an important prerequisite, it is the diversified network structure and the increased social connectedness that are responsible for the attainment of the four benefits of social capital on SNS: emotional support, networking value, horizon broadening and offline participation. Moreover, we propose and validate scales to measure social capital benefits in the novel context of SNS.

I like it because I (‘m) like You – measuring user attitudes towards information on Facebook

Limited in motivation and cognitive ability to process the increasing amount of information on their Newsfeed, users apply heuristic processing to form their attitudes. Rather than extensively analysing the content, they increasingly rely on heuristic cues – such as the amount of comments and likes as well as the level of relationship with the “poster” – to process the incoming information. In the paper we explore what impact these heuristic cues have on the affective and cognitive attitude of users towards the posts on their Newsfeed. We conduct a survey on based on a Facebook application that allows users to evaluate Newsfeed posts in real time. Applying two distinct panel-regression methods we report robust results that indicate that there is a certain relationship primacy effect when users are processing information: only if the level of relationship with the “poster” is low, the impact of comments and likes on the attitude is considered, whereby likes trigger positive, whereas comments – negative evaluations.

Investigation of the Network Construction Behaviour on Social Networking Sites

By enabling connections between individuals, Social Networking Sites, such as Facebook, promise to create significant individual as well as social value. Encouraging connections between users is also crucial for service providers who increasingly rely on social advertising and viral marketing campaigns as important sources of their revenue. Consequently, understanding user’s network construction behavior becomes critical. However, previous studies offer only few scattered insights into this research question. In order to fill this gap, we employ Grounded Theory methodology to derive a comprehensive model of network construction behavior on social networking sites. In the following step we assess two Structural Equation Models to gain refined insights into the motivation to send and accept friendship requests – two network expansion strategies. Based on our findings, we offer recommendations for social network providers.

Identification of cryptic Anopheles mosquito species by molecular protein profiling.

Vector control is the mainstay of malaria control programmes. Successful vector control profoundly relies on accurate information on the target mosquito populations in order to choose the most appropriate intervention for a given mosquito species and to monitor its impact. An impediment to identify mosquito species is the existence of morphologically identical sibling species that play different roles in the transmission of pathogens and parasites. Currently PCR diagnostics are used to distinguish between sibling species. PCR based methods are, however, expensive, time-consuming and their development requires a priori DNA sequence information. Here, we evaluated an inexpensive molecular proteomics approach for Anopheles species: matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). MALDI-TOF MS is a well developed protein profiling tool for the identification of microorganisms but so far has received little attention as a diagnostic tool in entomology. We measured MS spectra from specimens of 32 laboratory colonies and 2 field populations representing 12 Anopheles species including the A. gambiae species complex. An important step in the study was the advancement and implementation of a bioinformatics approach improving the resolution over previously applied cluster analysis. Borrowing tools for linear discriminant analysis from genomics, MALDI-TOF MS accurately identified taxonomically closely related mosquito species, including the separation between the M and S molecular forms of A. gambiae sensu stricto. The approach also classifies specimens from different laboratory colonies; hence proving also very promising for its use in colony authentication as part of quality assurance in laboratory studies. While being exceptionally accurate and robust, MALDI-TOF MS has several advantages over other typing methods, including simple sample preparation and short processing time. As the method does not require DNA sequence information, data can also be reviewed at any later stage for diagnostic or functional patterns without the need for re-designing and re-processing biological material.

Dynamics of governance and control in inter-organizational software development

In recent years, development of information systems (IS) has rapidly changed towards increasing division of labor between firms. Two trends are emerging. First, client companies increasingly outsource software development to external service providers. Second, the formerly oligopolistic enterprise application software industry has started to disintegrate into focal partnership networks – so called platform ecosystems. Despite the increasing prominence of IS outsourcing and platform ecosystems, many of these inter-organizational partnerships fail to achieve expected benefits. Ineffective governance and control frequently plays a pivotal role in producing these failures. While designing effective governance and control mechanisms is always challenging, inter-organizational software development projects are often business-critical and exhibit additional dynamics and uncertainty. As a consequence governance and control have to be adapted over time. The three research projects included in this book provide a better understanding of how and why governance and control can be effectively adapted over time. The implications for successful management of inter-organizational software development projects are highly relevant for theory and practice.

The Software Prototype as Digital Boundary Object – A Revelatory Longitudinal Innovation Case

With the availability of lower cost but highly skilled software development labor from offshore regions, entrepreneurs from developed countries who do not have software development experience can utilize this workforce to develop innovative software products. In order to succeed in offshored innovation projects, the often extreme knowledge boundaries between the onsite entrepreneur and the offshore software development team have to be overcome. Prior research has proposed that boundary objects are critical for bridging such boundaries – if they are appropriately used. Our longitudinal, revelatory case study of a software innovation project is one of the first to explore the role of the software prototype as a digital boundary object. Our study empirically unpacks five use practices that transform the software prototype into a boundary object such that knowledge boundaries are bridged. Our findings provide new theoretical insights for literature on software innovation and boundary objects, and have implications for practice.