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Der wissenschaftliche Arbeitsmarkt in Deutschland entwickelt sich zusehends zu einem „winner-take-all“-Markt. Dieser These geht der vorliegende Beitrag anhand der Darstellung der quantitativen Veränderungen auf dem wissenschaftlichen Arbeitsmarkt und den Daten einer qualitativen, empirischen Untersuchung zu den Karriereperspektiven des wissenschaftlichen Mittelbaus nach. Es wird argumentiert, dass der Staat über die Simulation von Markteffekten und sein Nachfragemonopol auf dem wissenschaftlichen Quasi-Markt die Konzentration von Gewinnen und das Überangebot an Akteuren, die um diese Gewinne konkurrieren, wissenschaftspolitisch induziert und verschärft hat. Zudem wird gezeigt, dass die Einschätzung der individuellen Karriereperspektiven von promovierten Wissenschaftlerinnen und Wissenschaftlern ohne Professur mit den sozialen Strukturen korreliert, in welche die Akteure eingebettet sind, insbesondere mit beruflicher Förderung und Unterstützung aus dem privaten Bereich. The academic labor market in Germany is becoming a “winner-take-all”-market. That is the argument that this paper develops using data about the changes on the labor market for scientists in Germany and from a qualitative empirical analysis about the career prospects of non-tenured scientists. It is argued that the state has induced or intensified the concentration of benefits and an oversupply of actors that compete for these benefits through the simulation of market effects and a monopoly over the demand for academic output. Moreover it is shown that the assessment of individual career prospects correlates with scientists’ embeddedness in social structures, especially the support provided by their partners and professional mentors.

Zusammenfassung Wie entstehen Erfolgskonzentrationen? Während das „Winner-take-all“-Phänomen bisher als Konzentrationsprozess auf der Nachfrageseite durch massenhaft gleichförmige Kaufentscheidungen der Konsumenten begriffen wurde, sind Bedingungen und Konstellationen auf der Anbieterseite wenig berücksichtigt worden. In diesem Beitrag werden sechs Ansätze diskutiert, die das Potenzial einer soziologischen Erklärung des Winner-take-all-Phänomens ausloten. Jeder der Ansätze versucht, Erfolgsungleichheiten aus den sozialen Strukturen heraus zu erklären, in die die Akteure auf dem Arbeitsmarkt eingebettet sind. Der Beitrag versteht sich als erster Zugang zu einem in der Soziologie zwar noch wenig erforschten, doch wichtigen Phänomen sozialer Ungleichheit und soll den Raum für zukünftige empirische Studien öffnen.

This paper examines the relative importance of platform quality, indirect network effects, and consumer expectations on the success of entrants in platform-based markets. We develop a theoretical model and find that an entrant's success depends on the strength of indirect network effects and on the consumers' discount factor for future applications. We then illustrate the model's applicability by examining Xbox's entry into the video game industry. We find that Xbox had a small quality advantage over the incumbent, PlayStation 2, and the strength of indirect network effects and the consumers' discount factor, while statistically significant, fall in the region where PlayStation 2' s position is unsustainable.

Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge. In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems. The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks, with short-term and long-term plasticity. The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities. In addition to the analog circuits, the device comprises also asynchronous digital logic circuits for setting different synapse and neuron properties as well as different network configurations. This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm(2), and consumes approximately 4 mW for typical experiments, for example involving attractor networks. Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential. By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities.

Because the literature on platform competition emphasizes the role of network effects, it prescribes rapidly expanding a network of platform users and complementary applications to capture entire markets. We challenge the unconditional logic of a winner-take-all (WTA) approach by empirically analyzing the dominant strategies used to build and position platform systems in the U.S. video game industry. We show that when platform firms pursue two popular WTA strategies concurrently and with equal intensity (growing the number and variety of applications while also securing a larger fraction of those applications with exclusivity agreements), it diminishes the benefits of each strategy to the point that it lowers platform performance. We also show that a differentiation strategy based on distinctive positioning improves a platform's performance only when a platform system is highly distinctive relative to its rivals. Our results suggest that platform competition is shaped by important strategic trade-offs and that the WTA approach will not be universally successful.

Sensors in many applications must select the largest element in a sequence of currents. This can be performed in an analog way by the Winner-Take-All (WTA) circuit. This paper considers the classic version of the WTA Lazzaro circuit, working with MOS devices in a subthreshold regime. Since the separation of the gainer by analytically computable “decision levels” has recently been introduced, this paper aims to numerically verify and discuss these levels and their dependence on circuit and device parameters. For VT, the threshold voltage of MOS devices, which is primarily responsible for differences between components (mismatch), its relationship with the output voltages is theoretically demonstrated and numerically checked.

We consider collective decision making when society consists of groups endowed with voting weights. Each group chooses an internal rule that specifies the allocation of its weight to alternatives as a function of its members' preferences. Under fairly general conditions, we show that the winner-take-all rule is a dominant strategy, while the equilibrium is Pareto dominated, highlighting the dilemma structure between optimality for each group and for the whole society. We also develop a technique for asymptotic analysis and show Pareto dominance of the proportional rule.

The Winner-Take-All circuit is an important part of the competition layer in the competitive neural network. Its main function is to compare the size of the output of the nodes after the weighted summation of all input vectors, and select the node with the largest output to output high power level, while other nodes output low level, that is, to find the node with the largest output. According to the characteristics of the Winner-Take-All circuit in the competitive neural network, the simulation of the Winner-Take-All circuit is carried out by the PSPICE simulation software. The physical test results show that, like the simulation diagram of the Winner-Take-All circuit, it conforms to the logic truth table, which further confirms the rationality and correctness of the Winner-Take-All circuit. Hardware realization of Winner-Take-All circuit as an important component of competitive layer in competitive neural networks has important research significance.

Gamma oscillations are believed to underlie cognitive processes by shaping the formation of transient neuronal partnerships on a millisecond scale. These oscillations are coupled to the phase of breathing cycles in several brain areas, possibly reflecting local computations driven by sensory inputs sampled at each breath. Here, we investigated the mechanisms and functions of gamma oscillations in the piriform (olfactory) cortex of awake mice to understand their dependence on breathing and how they relate to local spiking activity. Mechanistically, we find that respiration drives gamma oscillations in the piriform cortex, which correlate with local feedback inhibition and result from recurrent connections between local excitatory and inhibitory neuronal populations. Moreover, respiration-driven gamma oscillations are triggered by the activation of mitral/tufted cells in the olfactory bulb and are abolished during ketamine/xylazine anesthesia. Functionally, we demonstrate that they locally segregate neuronal assemblies through a winner-take-all computation leading to sparse odor coding during each breathing cycle. Our results shed new light on the mechanisms of gamma oscillations, bridging computation, cognition, and physiology. The cerebral cortex is the most recently evolved region of the mammalian brain. There, millions of neurons can synchronize their activity to create brain waves, a series of electric rhythms associated with various cognitive functions. Gamma waves, for example, are thought to be linked to brain processes which require distributed networks of neurons to communicate and integrate information. These waves were first discovered in the 1940s by researchers investigating brain areas involved in olfaction, and they are thought to be important for detecting and recognizing smells. Yet, scientists still do not understand how these waves are generated or what role they play in sensing odors. To investigate these questions, González et al. used a battery of computational approaches to analyze a large dataset of brain activity from awake mice. This revealed that, in the cortical region dedicated to olfaction, gamma waves arose each time the animals completed a breathing cycle – that is, after they had sampled the air by breathing in. Each breath was followed by certain neurons relaying olfactory information to the cortex to activate complex cell networks; this included circuits of cells known as feedback interneurons, which can switch off weakly activated neurons, including ones that participated in activating them in the first place. The respiration-driven gamma waves derived from this ‘feedback inhibition’ mechanism. Further work then examined the role of the waves in olfaction. Smell identification relies on each odor activating a unique set of cortical neurons. The analyses showed that gamma waves acted to select and amplify the best set of neurons for representing the odor sensed during a sniff, and to quieten less relevant neurons. Loss of smell is associated with many conditions which affect the brain, such as Alzheimer’s disease or COVID-19. By shedding light on the neuronal mechanisms that underpin olfaction, the work by González et al. could help to better understand how these impairments emerge, and how the brain processes other types of complex information.