Gender, Professional Networks, and Subjective Career Success within Early Academic Science Careers: The Role of Gender Composition in Inside and Outside Departmental Support Networks

The present study analyzed (a) gender differences in the gender composition (i.e., the proportion of male to female contacts) of professional support networks inside and outside an individual’s academic department and (b) how these differences in gender composition relate to subjective career success (i.e., perceived career success and perceived external marketability). Results showed that the networks’ gender composition is associated with subjective career success. Men’s networks consist of a higher proportion of male to female supporters, which, in turn, was positively related to subjective career success. Additional analyses revealed that the findings could not be accounted for by alternative factors, such as network size, networking behaviors, and career ambition.

Postgraduate training program

BACKGROUND: Faculties face the permanent challenge to design training programs with well-balanced educational outcomes, and to offer various organised and individual learning opportunities. AIM: To apply our original model to a postgraduate training program in rheumatology in general, and to various learning experiences in particular, in order to analyse the balance between different educational objectives. METHODS: Learning times of various educational activities were reported by the junior staff as targeted learners. The suitability of different learning experiences to achieve cognitive, affective and psychomotor learning objectives was estimated. Learning points with respect to efficacy were calculated by multiplication of the estimated learning times by the perceived appropriateness of the educational strategies. RESULTS: Out of 780 hours of professional learning per year (17.7 hours/week), 37.7% of the time was spent under individual supervision of senior staff, 24.4% in organised structured learning, 22.6% in self-studies, and 15.3% in organised patient-oriented learning. The balance between the different types of learning objectives was appropriate for the overall program, but not for each particular learning experience. Acquisition of factual knowledge and problem solving was readily aimed for during organised teaching sessions of different formats, and by personal targeted reading. Attitudes, skills and competencies, as well as behavioural and performance changes were mostly learned during caring for patients under interactive supervision by experts. CONCLUSION: We encourage other faculties to apply this approach to any other curriculum of undergraduate education, postgraduate training or continuous professional development in order to foster the development of well-balanced learning experiences.

Matching Recall and Storage in Sequence Learning with Spiking Neural Networks

Storing and recalling spiking sequences is a general problem the brain needs to solve. It is, however, unclear what type of biologically plausible learning rule is suited to learn a wide class of spatiotemporal activity patterns in a robust way. Here we consider a recurrent network of stochastic spiking neurons composed of both visible and hidden neurons. We derive a generic learning rule that is matched to the neural dynamics by minimizing an upper bound on the Kullback–Leibler divergence from the target distribution to the model distribution. The derived learning rule is consistent with spike-timing dependent plasticity in that a presynaptic spike preceding a postsynaptic spike elicits potentiation while otherwise depression emerges. Furthermore, the learning rule for synapses that target visible neurons can be matched to the recently proposed voltage-triplet rule. The learning rule for synapses that target hidden neurons is modulated by a global factor, which shares properties with astrocytes and gives rise to testable predictions.

Improving memory in elderly adults by instructing them in professional actors' learning strategies

This study examined a new type of cognitive intervention. For four weeks, participants (ages 65 to 82) were instructed in professional acting techniques, followed by rehearsal and performance of theatrical scenes. Although the training was not targeted in any way to the tasks used in pre- and post-testing, participants produced significantly higher recall and recognition scores after the intervention. It is suggested that the cognitive effort involved in analyzing and adopting theatrical characters' motivations (and then experiencing those characters' mental/emotional states during performance) is responsible for the observed improvement. A secondary strand of this study showed that participants who were given annotated scripts in which the implied goals of the characters were made explicit demonstrated significantly faster access to the stored material, as measured by a computer latency task.

Combining Wireless Sensor Networks and Machine Learning for Flash Flood Nowcasting

This paper addresses an investigation with machine learning (ML) classification techniques to assist in the problem of flash flood now casting. We have been attempting to build a Wireless Sensor Network (WSN) to collect measurements from a river located in an urban area. The machine learning classification methods were investigated with the aim of allowing flash flood now casting, which in turn allows the WSN to give alerts to the local population. We have evaluated several types of ML taking account of the different now casting stages (i.e. Number of future time steps to forecast). We have also evaluated different data representation to be used as input of the ML techniques. The results show that different data representation can lead to results significantly better for different stages of now casting.

Gradient estimation in dendritic reinforcement learning

We study synaptic plasticity in a complex neuronal cell model where NMDA-spikes can arise in certain dendritic zones. In the context of reinforcement learning, two kinds of plasticity rules are derived, zone reinforcement (ZR) and cell reinforcement (CR), which both optimize the expected reward by stochastic gradient ascent. For ZR, the synaptic plasticity response to the external reward signal is modulated exclusively by quantities which are local to the NMDA-spike initiation zone in which the synapse is situated. CR, in addition, uses nonlocal feedback from the soma of the cell, provided by mechanisms such as the backpropagating action potential. Simulation results show that, compared to ZR, the use of nonlocal feedback in CR can drastically enhance learning performance. We suggest that the availability of nonlocal feedback for learning is a key advantage of complex neurons over networks of simple point neurons, which have previously been found to be largely equivalent with regard to computational capability.

Towards Self-Learning Radio-Based Localization Systems

Location-awareness indoors will be an inseparable feature of mobile services/applications in future wireless networks. Its current ubiquitous availability is still obstructed by technological challenges and privacy issues. We propose an innovative approach towards the concept of indoor positioning with main goal to develop a system that is self-learning and able to adapt to various radio propagation environments. The approach combines estimation of propagation conditions, subsequent appropriate channel modelling and optimisation feedback to the used positioning algorithm. Main advantages of the proposal are decreased system set-up effort, automatic re-calibration and increased precision.

Plasticity in the adult language system: a longitudinal electrophysiological study on second language learning

Event-related potentials (ERPs) were used to trace changes in brain activity related to progress in second language learning. Twelve English-speaking exchange students learning German in Switzerland were recruited. ERPs to visually presented single words from the subjects' native language (English), second language (German) and an unknown language (Romansh) were measured before (day 1) and after (day 2) 5 months of intense German language learning. When comparing ERPs to German words from day 1 and day 2, we found topographic differences between 396 and 540 ms. These differences could be interpreted as a latency shift indicating faster processing of German words on day 2. Source analysis indicated that the topographic differences were accounted for by shorter activation of left inferior frontal gyrus (IFG) on day 2. In ERPs to English words, we found Global Field Power differences between 472 and 644 ms. This may due to memory traces related to English words being less easily activated on day 2. Alternatively, it might reflect the fact that--with German words becoming familiar on day 2--English words loose their oddball character and thus produce a weaker P300-like effect on day 2. In ERPs to Romansh words, no differences were observed. Our results reflect plasticity in the neuronal networks underlying second language acquisition. They indicate that with a higher level of second language proficiency, second language word processing is faster and requires shorter frontal activation. Thus, our results suggest that the reduced IFG activation found in previous fMRI studies might not reflect a generally lower activation but rather a shorter duration of activity.

Learning real-world stimuli in a neural network with spike-driven synaptic dynamics

We present a model of spike-driven synaptic plasticity inspired by experimental observations and motivated by the desire to build an electronic hardware device that can learn to classify complex stimuli in a semisupervised fashion. During training, patterns of activity are sequentially imposed on the input neurons, and an additional instructor signal drives the output neurons toward the desired activity. The network is made of integrate-and-fire neurons with constant leak and a floor. The synapses are bistable, and they are modified by the arrival of presynaptic spikes. The sign of the change is determined by both the depolarization and the state of a variable that integrates the postsynaptic action potentials. Following the training phase, the instructor signal is removed, and the output neurons are driven purely by the activity of the input neurons weighted by the plastic synapses. In the absence of stimulation, the synapses preserve their internal state indefinitely. Memories are also very robust to the disruptive action of spontaneous activity. A network of 2000 input neurons is shown to be able to classify correctly a large number (thousands) of highly overlapping patterns (300 classes of preprocessed Latex characters, 30 patterns per class, and a subset of the NIST characters data set) and to generalize with performances that are better than or comparable to those of artificial neural networks. Finally we show that the synaptic dynamics is compatible with many of the experimental observations on the induction of long-term modifications (spike-timing-dependent plasticity and its dependence on both the postsynaptic depolarization and the frequency of pre- and postsynaptic neurons).

Forget about theory - practice is all? - Student teachers' learning in practicum

In 1999, all student teachers at secondary I level at the University of Bern who had to undertake an internship were asked to participate in a study on learning processes during practicum: 150 students and their mentors in three types of practicum participated—introductory practicum (after the first half‐year of studies), intermediate practicum (after two years of studies) and final practicum (after three years of studies). At the end of the practicum, student teachers and mentors completed questionnaires on preparing, teaching and post‐processing lessons. All student teachers, additionally, rated their professional skills and aspects of personality (attitudes towards pupils, self‐assuredness and well‐being) before and after the practicum. Forty‐six student teachers wrote daily semi‐structured diaries about essential learning situations during their practicum. Results indicate that in each practicum students improved significantly in preparing, conducting and post‐processing lessons. The mentors rated these changes as being greater than did the student teachers. From the perspective of the student teachers their general teaching skills also improved, and their attitudes toward pupils became more open. Furthermore, during practicum their self‐esteem and subjective well‐being increased. Diary data confirmed that there are no differences between different levels of practicum in terms of learning outcomes, but give some first insight into different ways of learning during internship.

Learning processes in student teaching: Analyses from a study using learning diaries

The present study seeks to obtain deeper insight into the learning processes in practical training in primary teacher education in Upper Austria. Based on the offer-and-use model of instruction, 230 diary entries of 46 student teachers (28 students in their third semester, 18 students in their fifth semester) were analysed with legard to the learning topics, learning sourcesJ and Ìealning processes involved in practical training. The results show a variety of learning forms, ranging from the unreflective imitation of school mentors' practices to active knowledge construction. In addition, they illustrate that the available learning offers were suboptimally utilized by stuclent teachers who failed to work systernatically and continuously on their professional development.