Neuron as a reward-modulated combinatorial switch and a model of learning behavior

This paper proposes a neuronal circuitry layout and synaptic plasticity principles that allow the (pyramidal) neuron to act as a "combinatorial switch". Namely, the neuron learns to be more prone to generate spikes given those combinations of firing input neurons for which a previous spiking of the neuron had been followed by a positive global reward signal. The reward signal may be mediated by certain modulatory hormones or neurotransmitters, e.g., the dopamine. More generally, a trial-and-error learning paradigm is suggested in which a global reward signal triggers long-term enhancement or weakening of a neuron's spiking response to the preceding neuronal input firing pattern. Thus, rewards provide a feedback pathway that informs neurons whether their spiking was beneficial or detrimental for a particular input combination. The neuron's ability to discern specific combinations of firing input neurons is achieved through a random or predetermined spatial distribution of input synapses on dendrites that creates synaptic clusters that represent various permutations of input neurons. The corresponding dendritic segments, or the enclosed individual spines, are capable of being particularly excited, due to local sigmoidal thresholding involving voltage-gated channel conductances, if the segment's excitatory and absence of inhibitory inputs are temporally coincident. Such nonlinear excitation corresponds to a particular firing combination of input neurons, and it is posited that the excitation strength encodes the combinatorial memory and is regulated by long-term plasticity mechanisms. It is also suggested that the spine calcium influx that may result from the spatiotemporal synaptic input coincidence may cause the spine head actin filaments to undergo mechanical (muscle-like) contraction, with the ensuing cytoskeletal deformation transmitted to the axon initial segment where it may...Comment: Version 5: added computer code in the ancillary files sectio

Modern Concepts of Financial and Non-Financial Motivation of Service Industries Staff

In modern conditions the questions of personnel management, including motivation, acquire new meaning. Particularly given the problems relevant to the service sector, where at the beginning of the XXI century employing more than 60% of the workforce in developed countries. These circumstances determine the need for a modern concept of material and immaterial motivation of service industries. Such factors determine the need for the development modern concept of material and immaterial motivation of service industries staff. To obtain indicated objective during research analyzed the existing concepts and paradigm of staff motivation with highlighting their advantages and disadvantages. The results obtained allowed to establish that scientific and expert community does not have the unified approach to the classification and identification of the most effective ones. Special attention is given to modern developments and approaches to the motivation problem. This fact caused the structure of follow studies, including three interlinked vectors: analysis of the essential content of the fundamental concepts in the field of staff motivation; defining features of employee motivation at the enterprises sphere of services; introduction to the key successful international practices which apply by service companies. In general, the results obtained enabled the author’s model of the modern concept of material and non-material motivation at the enterprises the service sector and the corresponding mechanism for the implementation

Forecasting Popularity of Videos using Social Media

This paper presents a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media. Social-Forecast explicitly considers the dynamically changing and evolving propagation patterns of videos in social media when making popularity forecasts, thereby being situation and context aware. Social-Forecast aims to maximize the forecast reward, which is defined as a tradeoff between the popularity prediction accuracy and the timeliness with which a prediction is issued. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the prediction performance loss of Social-Forecast as compared to that obtained by an omniscient oracle and prove that the bound is sublinear in the number of video arrivals, thereby guaranteeing its short-term performance as well as its asymptotic convergence to the optimal performance. In addition, we conduct extensive experiments using real-world data traces collected from the videos shared in RenRen, one of the largest online social networks in China. These experiments show that our proposed method outperforms existing view-based approaches for popularity prediction (which are not context-aware) by more than 30% in terms of prediction rewards

Distributed Cognitive RAT Selection in 5G Heterogeneous Networks: A Machine Learning Approach

The leading role of the HetNet (Heterogeneous Networks) strategy as the key Radio Access Network (RAN) architecture for future 5G networks poses serious challenges to the current cell selection mechanisms used in cellular networks. The max-SINR algorithm, although effective historically for performing the most essential networking function of wireless networks, is inefficient at best and obsolete at worst in 5G HetNets. The foreseen embarrassment of riches and diversified propagation characteristics of network attachment points spanning multiple Radio Access Technologies (RAT) requires novel and creative context-aware system designs. The association and routing decisions, in the context of single-RAT or multi-RAT connections, need to be optimized to efficiently exploit the benefits of the architecture. However, the high computational complexity required for multi-parametric optimization of utility functions, the difficulty of modeling and solving Markov Decision Processes, the lack of guarantees of stability of Game Theory algorithms, and the rigidness of simpler methods like Cell Range Expansion and operator policies managed by the Access Network Discovery and Selection Function (ANDSF), makes neither of these state-of-the-art approaches a favorite. This Thesis proposes a framework that relies on Machine Learning techniques at the terminal device-level for Cognitive RAT Selection. The use of cognition allows the terminal device to learn both a multi-parametric state model and effective decision policies, based on the experience of the device itself. This implies that a terminal, after observing its environment during a learning period, may formulate a system characterization and optimize its own association decisions without any external intervention. In our proposal, this is achieved through clustering of appropriately defined feature vectors for building a system state model, supervised classification to obtain the current system state, and reinforcement learning for learning good policies. This Thesis describes the above framework in detail and recommends adaptations based on the experimentation with the X-means, k-Nearest Neighbors, and Q-learning algorithms, the building blocks of the solution. The network performance of the proposed framework is evaluated in a multi-agent environment implemented in MATLAB where it is compared with alternative RAT selection mechanisms

Strategic Interaction and Conventions

The scope of the paper is the literature that employs coordination games to study social norms and conventions from the viewpoint of game theory and cognitive psychology. We claim that those two alternative approaches are complementary, as they provide different insights to explain how people converge to a unique system of self-fulfilling expectations in presence of multiple, equally viable, conventions. While game theory explains the emergence of conventions relying on efficiency and risk considerations, the psychological view is more concerned with frame and labeling effects. The interaction between these alternative (and, sometimes, competing) effects leads to the result that coordination failures may well occur and, even when coordination takes place, there is no guarantee that the convention eventually established will be the most efficient.conventions, social norms, behavioral game theory

Strategic Interaction and Conventions

The scope of the paper is to review the literature that employs coordination games to study social norms and conventions from the viewpoint of game theory and cognitive psychology. We claim that those two alternative approaches are complementary, as they provide different insights to explain how people converge to a unique system of self-fulfilling expectations in presence of multiple, equally viable, conventions. While game theory explains the emergence of conventions relying on efficiency and risk considerations, the psychological view is more concerned with frame and labeling effects. The interaction between these alternative (and, sometimes, competing) effects leads to the result that coordination failures may well occur and, even when coordination takes place, there is no guarantee that the convention eventually established will be the most efficient.Behavioral Game Theory, conventions, social norms