Extracting finite structure from infinite language

This paper presents a novel connectionist memory-rule based model capable of learning the finite-state properties of an input language from a set of positive examples. The model is based upon an unsupervised recurrent self-organizing map [T. McQueen, A. Hopgood, J. Tepper, T. Allen, A recurrent self-organizing map for temporal sequence processing, in: Proceedings of Fourth International Conference in Recent Advances in Soft Computing (RASC2002), Nottingham, 2002] with laterally interconnected neurons. A derivation of functionalequivalence theory [J. Hopcroft, J. Ullman, Introduction to Automata Theory, Languages and Computation, vol. 1, Addison-Wesley, Reading, MA, 1979] is used that allows the model to exploit similarities between the future context of previously memorized sequences and the future context of the current input sequence. This bottom-up learning algorithm binds functionally related neurons together to form states. Results show that the model is able to learn the Reber grammar [A. Cleeremans, D. Schreiber, J. McClelland, Finite state automata and simple recurrent networks, Neural Computation, 1 (1989) 372–381] perfectly from a randomly generated training set and to generalize to sequences beyond the length of those found in the training set

Recursive self-organizing map as a contractive iterative function system

Recently, there has been a considerable research activity in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, the representational capabilities and internal representations of the models are not well understood. We rigorously analyze a generalization of the Self-Organizing Map (SOM) for processing sequential data, Recursive SOM (RecSOM [1]), as a non-autonomous dynamical system consisting off a set of fixed input maps. We show that contractive fixed input maps are likely to produce Markovian organizations of receptive fields o the RecSOM map. We derive bounds on parameter $\beta$ (weighting the importance of importing past information when processing sequences) under which contractiveness of the fixed input maps is guaranteed

Stabilisation of beta and gamma oscillation frequency in the mammalian olfactory bulb

International audienceThe dynamics of the mammalian olfactory bulb (OB) is characterized by local field potential (LFP) oscillations either slow, in the theta range (2-10Hz, tightly linked to the respiratory rhythm), or fast, in the beta (15-30Hz) or gamma (40-90Hz) range. These fast oscillations are known to be modulated by odorant features and animal experience or state, but both their mechanisms and implication in coding are still not well understood. In this study, we used a double canulation protocol to impose artificial breathing rhythms to anesthetized rats while recording the LFP in the OB. We observed that despite the changes in the input air flow parameters (frequency or flow rate), the main characteristics of fast oscillations (duration, frequency or amplitude) were merely constant. We thus made the hypothesis that fast beta and gamma oscillations dynamics are entirely determined by the OB network properties and that external stimulation was only able put the network in a state which permits the generation of one or the other oscillations (they are never present simultaneously)

Look and Feel What and How Recurrent Self-Organizing Maps Learn

International audienceThis paper introduces representations and measurements for revealing the inner self-organization that occurs in a 1D recurrent self-organizing map. Experiments show the incredible richness and robustness of an extremely simple architecture when it extracts hidden states of the HMM that feeds it with ambiguous and noisy inputs

Adaptive Robotic Control Driven by a Versatile Spiking Cerebellar Network

The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.European Union (Human Brain Project) REALNET FP7-ICT270434 CEREBNET FP7-ITN238686 HBP-60410

Assessing the Effects of Responsible Leadership and Ethical Conflict on Behavioral Intention

[[abstract]]This study develops a research model that elaborates how responsible leadership and ethical conflict influence employees from the perspectives of role theory and attachment theory. Its empirical results reveal that turnover intention indirectly relates to ethical conflict and responsible leadership via the mediating mechanisms of organizational identification and organizational uncertainty. At the same time, helping intention indirectly relates to ethical conflict and responsible leadership only through organizational identification. Finally, the managerial implications for international business and research limitations based on the empirical results are discussed.[[notice]]補正完

Gender Equality and Corporate Social Responsibility in the Middle East

This chapter focuses on corporate social responsibility (CSR) in relation to gender equality in the Arab Middle East. It examines the relationship between CSR and gender in the workplace whilst exploring the link between CSR and human resource management (HRM) policies and practices. The chapter first presents some seminal work on gender equality and diversity management, looking at the business case for gender equality within the CSR and HRM contexts, before engaging with relevant work on gender equality in the Arab Middle East. It concludes by offering recommendations on advancing the equality agenda at the macro- and meso-levels, within a framework which recognises the centrality of agency of women, as well as the potential of positive changes through corporations being seen as ‘agents of change’. The chapter advocates for organisational and governmental policies to promote gender equality in the Arab Middle East

A neo-institutional perspective on ethical decision-making

Drawing on neo-institutional theory, this study aims to discern the poorly understood ethical challenges confronted by senior executives in Indian multinational corporations and identify the strategies that they utilize to overcome them. We conducted in-depth interviews with 40 senior executives in Indian multinational corporations to illustrate these challenges and strategies. By embedding our research in contextually relevant characteristics that embody the Indian environment, we identify several institutional- and managerial-level challenges faced by executives. The institutional-level challenges are interpreted as regulative, normative and cognitive shortcomings. We recommend a concerted effort at the institutional and managerial levels by identifying relevant strategies for ethical decision-making. Moreover, we proffer a multi-level model of ethical decision-making and discuss our theoretical contributions and practical implications