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

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

Multistable attractors in a network of phase oscillators with three-body interaction

Three-body interactions have been found in physics, biology, and sociology. To investigate their effect on dynamical systems, as a first step, we study numerically and theoretically a system of phase oscillators with three-body interaction. As a result, an infinite number of multistable synchronized states appear above a critical coupling strength, while a stable incoherent state always exists for any coupling strength. Owing to the infinite multistability, the degree of synchrony in asymptotic state can vary continuously within some range depending on the initial phase pattern.Comment: 5 pages, 3 figure

The influence of contextual reward statistics on risk preference

Decision theories mandate that organisms should adjust their behaviour in the light of the contextual reward statistics. We tested this notion using a gambling choice task involving distinct contexts with different reward distributions. The best fitting model of subjects' behaviour indicated that the subjective values of options depended on several factors, including a baseline gambling propensity, a gambling preference dependent on reward amount, and a contextual reward adaptation factor. Combining this behavioural model with simultaneous functional magnetic resonance imaging we probed neural responses in three key regions linked to reward and value, namely ventral tegmental area/substantia nigra (VTA/SN), ventromedial prefrontal cortex (vmPFC) and ventral striatum (VST). We show that activity in the VTA/SN reflected contextual reward statistics to the extent that context affected behaviour, activity in the vmPFC represented a value difference between chosen and unchosen options while VST responses reflected a non-linear mapping between the actual objective rewards and their subjective value. The findings highlight a multifaceted basis for choice behaviour with distinct mappings between components of this behaviour and value sensitive brain regions

The Dopaminergic Midbrain Mediates an Effect of Average Reward on Pavlovian Vigor

Dopamine plays a key role in motivation. Phasic dopamine response reflects a reinforcement prediction error (RPE), whereas tonic dopamine activity is postulated to represent an average reward that mediates motivational vigor. However, it has been hard to find evidence concerning the neural encoding of average reward that is uncorrupted by influences of RPEs. We circumvented this difficulty in a novel visual search task where we measured participants' button pressing vigor in a context where information (underlying an RPE) about future average reward was provided well before the average reward itself. Despite no instrumental consequence, participants' pressing force increased for greater current average reward, consistent with a form of Pavlovian effect on motivational vigor. We recorded participants' brain activity during task performance with fMRI. Greater average reward was associated with enhanced activity in dopaminergic midbrain to a degree that correlated with the relationship between average reward and pressing vigor. Interestingly, an opposite pattern was observed in subgenual cingulate cortex, a region implicated in negative mood and motivational inhibition. These findings highlight a crucial role for dopaminergic midbrain in representing aspects of average reward and motivational vigor

How do people learn how to plan?

How does the brain learn how to plan? We reverse-engineer people's underlying learning mechanisms by combining rational process models of cognitive plasticity with recently developed empirical methods that allow us to trace the temporal evolution of people's planning strategies. We find that our Learned Value of Computation model (LVOC) accurately captures people's average learning curve. However, there were also substantial individual differences in metacognitive learning that are best understood in terms of multiple different learning mechanisms -- including strategy selection learning. Furthermore, we observed that LVOC could not fully capture people's ability to adaptively decide when to stop planning. We successfully extended the LVOC model to address these discrepancies. Our models broadly capture people's ability to improve their decision mechanisms and represent a significant step towards reverse-engineering how the brain learns increasingly more effective cognitive strategies through its interaction with the environment

Higgs Chaotic Inflation in Standard Model and NMSSM

We construct a chaotic inflation model in which the Higgs fields play the role of the inflaton in the standard model as well as in the singlet extension of the supersymmetric standard model. The key idea is to allow a non-canonical kinetic term for the Higgs field. The model is a realization of the recently proposed running kinetic inflation, in which the coefficient of the kinetic term grows as the inflaton field. The inflaton potential depends on the structure of the Higgs kinetic term. For instance, the inflaton potential is proportional to phi^2 and phi^{2/3} in the standard model and NMSSM, respectively. It is also possible to have a flatter inflaton potential.Comment: 5 pages. v2:discussion and references adde

Amplitude and phase modulation of time-energy entangled two-photon states

We experimentally demonstrate amplitude and phase modulation of a time-energy entangled two-photon wave function. The entangled photons are produced by spontaneous parametric down-conversion, spectrally dispersed in an prism compressor, modulated in amplitude and/or phase, and detected in coincidence by sum-frequency generation. First, we present a Fourier optical analysis of the optical setup yielding an analytic expression for the resulting field distribution at the exit plane of the shaping apparatus. We then introduce amplitude and/or phase shaping and present results which can only be obtained through a combination of the two. Specifically, we use a shaper-based interferometer to measure the two-photon interference of an almost bandwidth-limited two-photon wave function.Comment: 7 pages, 4 figure