Recursive Principal Components Analysis

A recurrent linear network can be trained with Oja's constrained Hebbian learning rule. As a result, the network learns to represent the temporal context associated to its input sequence. The operation performed by the network is a generalization of Principal Components Analysis (PCA) to time-series, called Recursive PCA. The representations learned by the network are adapted to the temporal statistics of the input. Moreover, sequences stored in the network may be retrieved explicitly, in the reverse order of presentation, thus providing a straight-forward neural implementation of a logical stack

CLONES : a closed-loop simulation framework for body, muscles and neurons

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General anaesthetics induce tonic inhibition and modulate the gain of neural populations : a modeling study

International audienceAnaesthetic agents are known to affect extra-synaptic GABAergic receptors[1], which induce tonic inhibitory currents. Since these receptors are very sensitive to small concentrations of agents, they are supposed to play an important role in the underlying neural mechanism of general anaesthesia. Moreover anaesthetic agents modulate the encephalographic activity (EEG) of patients and hence show an effect on neural populations. To understand better the tonic inhibition effect in single neurons on neural populations modulating the EEG, the work considers a neural population in a steady-state and studies numerically and analytically the modulation of its population firing rate and the nonlinear gain with respect to different levels of tonic inhibition. We consider populations of both type-I and type-II neurons. The populations under study are heterogeneous involving distributions of firing thresholds and inhibitory conductances. The tonic inhibition introduces shunting action

Linear Recursive Distributed Representations

Connectionist networks have been criticized for their inability to represent complex structures with systematicity. That is, while they can be trained to represent and manipulate complex objects made of several constituents, they generally fail to generalize to novel combinations of the same constituents. This paper presents a modification of Pollack's Recursive Auto-Associative Memory (RAAM), that addresses this criticism. The network uses linear units and is trained with Oja's rule, in which it generalizes PCA to tree-structured data. Learned representations may be linearly combined, in order to represent new complex structures. This results in unprecedented generalization capabilities. Capacity is orders of magnitude higher than that of a RAAM trained with back-propagation. Moreover, regularities of the training set are preserved in the new formed objects. The formation of new structures displays developmental effects similar to those observed in children when learning to generalize about the argument structure of verbs

Automated Verification of Electrum Wallet

International audienceWe introduce a formal modeling in ASLan++ of the two-factor authentication protocol used by the Electrum Bitcoin wallet. This allows us to perform an automatic analysis of the wallet and show that it is secure for standard scenarios in Dolev Yao model [Dolev 1981]. The result could be derived thanks to some advanced features of the protocol analyzer such as the possibility to specify i) new intruder deduction rules with clauses and ii) non-deducibility constraints

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)

Stable frequency response to varying stimulus intensity in a model of the rat olfactory bulb

In the rat olfactory bulb (OB), fast oscillations of the local field potential (LFP) are observed during the respiratory cycle. Gamma-range oscillations (60Hz) occurat the end of inspiration, followed by beta-range oscillations (15-20Hz) during exhalation. These oscillations are highly stereotyped, and their frequencies are stable under various conditions. Here we investigate the effect of stimulus intensity on activity in the OB. Using a double canulation protocol, we show that, although the frequency of the LFP oscillation does depend on the respiratory cycle, it is relatively independent from the intensity of odorant stimulation. In contrast, we found that the individual firing rate of mitral OB cells changes greatly with the intensity of the stimulation. Using a computer model of the OB, where fast oscillations are generated by the interplay between excitatory mitral/tufted cells, and inhibitory granule cells, we found that the difference between individual and population responses can be explained by the role of sub-threshold oscillations in the MCs. Sub-threshold oscillations of the MCs stabilize the frequency of the population oscillation, and allow their firing rate to vary without affecting the population frequency

Business Ethics: The Promise of Neuroscience

Recent advances in cognitive neuroscience research portend well for furthering understanding of many of the fundamental questions in the field of business ethics, both normative and empirical. This article provides an overview of neuroscience methodology and brain structures, and explores the areas in which neuroscience research has contributed findings of value to business ethics, as well as suggesting areas for future research. Neuroscience research is especially capable of providing insight into individual reactions to ethical issues, while also raising challenging normative questions about the nature of moral responsibility, autonomy, intent, and free will. This article also provides a brief summary of the papers included in this special issue, attesting to the richness of scholarly inquiry linking neuroscience and business ethics. We conclude that neuroscience offers considerable promise to the field of business ethics, but we caution against overpromise

Societal-level versus individual-level predictions of ethical behavior: a 48-society study of collectivism and individualism

Is the societal-level of analysis sufficient today to understand the values of those in the global workforce? Or are individual-level analyses more appropriate for assessing the influence of values on ethical behaviors across country workforces? Using multi-level analyses for a 48-society sample, we test the utility of both the societal-level and individual-level dimensions of collectivism and individualism values for predicting ethical behaviors of business professionals. Our values-based behavioral analysis indicates that values at the individual-level make a more significant contribution to explaining variance in ethical behaviors than do values at the societal-level. Implicitly, our findings question the soundness of using societal-level values measures. Implications for international business research are discussed