2,159 research outputs found
The Coherence Field in the Field Perturbation Theory of Superconductivity
We re-examine the Nambu-Gorkov perturbation theory of superconductivity on
the basis of the Bogoliubov-Valatin quasi-particles. We show that two different
fields (and two additional analogous fields) may be constructed, and that the
Nambu field is only one of them. For the other field- the coherence field- the
interaction is given by means of two interaction vertices that are based on the
Pauli matrices tau1 and tau3. Consequently, the Hartree integral for the
off-diagonal pairing self-energy may be finite, and in some cases large. We
interpret the results in terms of conventional superconductivity, and also
discuss briefly possible implications to HTSC
Do stochastic inhomogeneities affect dark-energy precision measurements?
The effect of a stochastic background of cosmological perturbations on the
luminosity-redshift relation is computed to second order through a recently
proposed covariant and gauge-invariant light-cone averaging procedure. The
resulting expressions are free from both ultraviolet and infrared divergences,
implying that such perturbations cannot mimic a sizable fraction of dark
energy. Different averages are estimated and depend on the particular function
of the luminosity distance being averaged. The energy flux, being minimally
affected by perturbations at large z, is proposed as the best choice for
precision estimates of dark-energy parameters. Nonetheless, its irreducible
(stochastic) variance induces statistical errors on \Omega_{\Lambda}(z)
typically lying in the few-percent range.Comment: 5 pages, 3 figures. Comments and references added. Typos corrected.
Version accepted for publication in Phys. Rev. Let
Supervised Associative Learning in Spiking Neural Network
In this paper, we propose a simple supervised associative learning approach for spiking neural networks. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses dependent on both spike emission rates and spike timings. As results of learning, the network is able to associate not just familiar stimuli but also novel stimuli observed through synchronised activity within the same subpopulation and between two associated subpopulations
Staying afloat on Neurath's boat - Heuristics for sequential causal learning
Causal models are key to flexible and efficient exploitation of the environment. However, learning causal structure is hard, with massive spaces of possible models, hard-to-compute marginals and the need to integrate diverse evidence over many instances. We report on two experiments in which participants learnt about probabilistic causal systems involving three and
four variables from sequences of interventions. Participants were broadly successful, albeit exhibiting sequential dependence and floundering under high background noise. We capture their behavior with a simple model, based on the “Neurath’s ship” metaphor for scientific progress, that neither maintains a probability distribution, nor computes exact likelihoods
Dynamical Phase Transitions In Driven Integrate-And-Fire Neurons
We explore the dynamics of an integrate-and-fire neuron with an oscillatory
stimulus. The frustration due to the competition between the neuron's natural
firing period and that of the oscillatory rhythm, leads to a rich structure of
asymptotic phase locking patterns and ordering dynamics. The phase transitions
between these states can be classified as either tangent or discontinuous
bifurcations, each with its own characteristic scaling laws. The discontinuous
bifurcations exhibit a new kind of phase transition that may be viewed as
intermediate between continuous and first order, while tangent bifurcations
behave like continuous transitions with a diverging coherence scale.Comment: 4 pages, 5 figure
Nonlinear interactions with an ultrahigh flux of broadband entangled photons
We experimentally demonstrate sum-frequency generation (SFG) with entangled
photon-pairs, generating as many as 40,000 SFG photons per second, visible even
to the naked eye. The nonclassical nature of the interaction is exhibited by a
linear intensity-dependence of the nonlinear process. The key element in our
scheme is the generation of an ultrahigh flux of entangled photons while
maintaining their nonclassical properties. This is made possible by generating
the down-converted photons as broadband as possible, orders of magnitude wider
than the pump. This approach is readily applicable for other nonlinear
interactions, and may be applicable for various quantum-measurement tasks.Comment: 4 pages, 2 figures, Accepted to Phys. Rev. Let
Neuronal assembly dynamics in supervised and unsupervised learning scenarios
The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions
Can distributed delays perfectly stabilize dynamical networks?
Signal transmission delays tend to destabilize dynamical networks leading to
oscillation, but their dispersion contributes oppositely toward stabilization.
We analyze an integro-differential equation that describes the collective
dynamics of a neural network with distributed signal delays. With the gamma
distributed delays less dispersed than exponential distribution, the system
exhibits reentrant phenomena, in which the stability is once lost but then
recovered as the mean delay is increased. With delays dispersed more highly
than exponential, the system never destabilizes.Comment: 4pages 5figure
Should radioiodine now be first line treatment for Graves' disease?
Background
Radioiodine represents a cost-effective treatment option for Graves’ disease. In the UK, it is traditionally reserved for patients who relapse after initial thionamide therapy. In a change from current practice, the new guidelines of the National Institute for Health and Care Excellence (NICE) recommends that radioiodine should now be first line therapy for Graves’ disease. However, the safety of radioiodine with respect to long-term mortality risk has been the subject of recent debate. This analysis examines evidence from treatment related mortality studies in hyperthyroidism and discusses their implications for future Graves’ disease treatment strategies.
Main body
Some studies have suggested an excess mortality in radioiodine treated cohorts compared to the background population. In particular, a recent observational study reported a modest increase in cancer-related mortality in hyperthyroid patients exposed to radioiodine. The interpretation of these studies is however constrained by study designs that lacked thionamide control groups or information on thyroid status and so could not distinguish the effect of treatment from disease. Two studies have shown survival advantages of radioiodine over thionamide therapy, but these benefits were only seen when radioiodine was successful in controlling hyperthyroidism. Notably, increased mortality was associated with uncontrolled hyperthyroidism irrespective of therapy modality.
Conclusions
Early radioiodine treatment will potentially reduce mortality and should be offered to patients with severe disease. However, thionamides are still suitable for patients with milder disease, contraindications to radioiodine, or individuals who choose to avoid permanent hypothyroidism. Ultimately, a patient individualised approach that prioritises early and sustained control of hyperthyroidism will improve long-term outcomes regardless of the therapy modality used
Combined model-free and model-sensitive reinforcement learning in non-human primates
Contemporary reinforcement learning (RL) theory suggests
that potential choices can be evaluated by strategies that may
or may not be sensitive to the computational structure of tasks.
A paradigmatic model-free (MF) strategy simply repeats actions
that have been rewarded in the past; by contrast, modelsensitive
(MS) strategies exploit richer information associated
with knowledge of task dynamics. MF and MS strategies should
typically be combined, because they have complementary statistical
and computational strengths; however, this tradeoff between
MF/MS RL has mostly only been demonstrated in humans,
often with only modest numbers of trials. We trained
rhesus monkeys to perform a two-stage decision task designed
to elicit and discriminate the use of MF and MS methods. A descriptive
analysis of choice behaviour revealed directly that the
structure of the task (of MS importance) and the reward history
(of MF and MS importance) significantly influenced both
choice and response vigour. A detailed, trial-by-trial computational
analysis confirmed that choices were made according to a
combination of strategies, with a dominant influence of a particular
form of model sensitivity that persisted over weeks of testing.
The residuals from this model necessitated development of a
new combined RL model which incorporates a particular credit
assignment weighting procedure. Finally, response vigor exhibited
a subtly different collection ofMFand MS influences. These
results provide new illumination onto RL behavioural processes
in non-human primates
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