Evolution of cosmological perturbations in an RG-driven inflationary scenario

A gauge-invariant, linear cosmological perturbation theory of an almost homogeneous and isotropic universe with dynamically evolving Newton constant G and cosmological constant $\Lambda$ is presented. The equations governing the evolution of the comoving fractional spatial gradients of the matter density, G and $\Lambda$ are thus obtained. Explicit solutions are discussed in cosmologies, featuring an accelerated expansion, where both G and $\Lambda$ vary according to renormalization group equations in the vicinity of an ultraviolet fixed point. Finally, a similar analysis is carried out in the late universe regime described by the part of the renormalization group trajectory close to the gaussian fixed point.Comment: 18 pages; improved discussion and references added; fitted to match published versio

Inflation in asymptotically safe f(R) theory

We discuss the existence of inflationary solutions in a class of renormalization group improved polynomial f(R) theories, which have been studied recently in the context of the asymptotic safety scenario for quantum gravity. These theories seem to possess a nontrivial ultraviolet fixed point, where the dimensionful couplings scale according to their canonical dimensionality. Assuming that the cutoff is proportional to the Hubble parameter, we obtain modified Friedmann equations which admit both power law and exponential solutions. We establish that for sufficiently high order polynomial the solutions are reliable, in the sense that considering still higher order polynomials is very unlikely to change the solution.Comment: Presented at 14th Conference on Recent Developments in Gravity: NEB 14, Ioannina, Greece, 8-11 Jun 201

Renormalisation group improvement of scalar field inflation

We study quantum corrections to Friedmann-Robertson-Walker cosmology with a scalar field under the assumption that the dynamics are subject to renormalisation group improvement. We use the Bianchi identity to relate the renormalisation group scale to the scale factor and obtain the improved cosmological evolution equations. We study the solutions of these equations in the renormalisation group fixed point regime, obtaining the time-dependence of the scalar field strength and the Hubble parameter in specific models with monomial and trinomial quartic scalar field potentials. We find that power-law inflation can be achieved in the renormalisation group fixed point regime with the trinomial potential, but not with the monomial one. We study the transition to the quasi-classical regime, where the quantum corrections to the couplings become small, and find classical dynamics as an attractor solution for late times. We show that the solution found in the renormalisation group fixed point regime is also a cosmological fixed point in the autonomous phase space. We derive the power spectrum of cosmological perturbations and find that the scalar power spectrum is exactly scale-invariant and bounded up to arbitrarily small times, while the tensor perturbations are tilted as appropriate for the background power-law inflation. We specify conditions for the renormalisation group fixed point values of the couplings under which the amplitudes of the cosmological perturbations remain small.Comment: 17 pages; 2 figure

Renormalisation group improvement of the early universe dynamics

Selected applications of the Functional Renormalisation Group Equation technique to the early universe dynamics

Sparks fade with distance: The effect of electric field distribution on global motion perception using different tES techniques

Previous evidence has shown that high-frequency transcranial random noise stimulation (hf-tRNS) reduces motion coherence thresholds when applied with a cephalic montage (i.e., return electrode over Cz). Extracephalic montages, which avoid stimulating regions under the return electrode, have also been used to modulate behavioral performance. In this study, we investigated the effects of different transcranial electrical stimulation (tES) protocols on visual motion discrimination, placing the return electrode on the ipsilateral arm. We assessed the impact of electrode positioning using hf-tRNS, anodal, cathodal transcranial direct current stimulation (tDCS), and Sham stimulation over hMT+, a brain region involved in global motion perception. Motion direction discrimination was measured using random dot kinematograms (RDKs). Given the increased distance between the stimulation and return electrodes in this montage, we expected a smaller reduction in motion discrimination thresholds compared to our previous study. Our results suggest that increasing interelectrode distance alters current flow characteristics - such as current distribution and focality - within the cortical areas under the target electrode, producing different effects. Additionally, no significant effects were observed with the other tES protocols tested. Our findings suggest that change in the interelectrode distance influences current flow characteristics, such as current distribution and focality, within the cortical areas under the target electrode, resulting in differential neuromodulatory effects. These results highlight the importance of stimulation configuration on performance, particularly a potential electric field shift due to the change in the interelectrode distance. Given the widespread application of brain stimulation techniques in clinical and cognitive research, our results can guide future studies carefully considering this further aspect of stimulation montage configurations

Modelling Adaptation to Directional Motion Using the Adelson-Bergen Energy Sensor

Abstract The motion energy sensor has been shown to account for a wide range of physiological and psychophysical results in motion detection and discrimination studies. It has become established as the standard computational model for retinal movement sensing in the human visual system. Adaptation effects have been extensively studied in the psychophysical literature on motion perception, and play a crucial role in theoretical debates, but the current implementation of the energy sensor does not provide directly for modelling adaptation-induced changes in output. We describe an extension of the model to incorporate changes in output due to adaptation. The extended model first computes a space-time representation of the output to a given stimulus, and then a RC gain-control circuit (''leaky integrator'') is applied to the time-dependent output. The output of the extended model shows effects which mirror those observed in psychophysical studies of motion adaptation: a decline in sensor output during stimulation, and changes in the relative of outputs of different sensors following this adaptation

Visual Perceptual Learning of Form–Motion Integration: Exploring the Involved Mechanisms with Transfer Effects and the Equivalent Noise Approach

Background: Visual perceptual learning plays a crucial role in shaping our understanding of how the human brain integrates visual cues to construct coherent perceptual experiences. The visual system is continually challenged to integrate a multitude of visual cues, including form and motion, to create a unified representation of the surrounding visual scene. This process involves both the processing of local signals and their integration into a coherent global percept. Over the past several decades, researchers have explored the mechanisms underlying this integration, focusing on concepts such as internal noise and sampling efficiency, which pertain to local and global processing, respectively. Objectives and Methods: In this study, we investigated the influence of visual perceptual learning on non-directional motion processing using dynamic Glass patterns (GPs) and modified Random-Dot Kinematograms (mRDKs). We also explored the mechanisms of learning transfer to different stimuli and tasks. Specifically, we aimed to assess whether visual perceptual learning based on illusory directional motion, triggered by form and motion cues (dynamic GPs), transfers to stimuli that elicit comparable illusory motion, such as mRDKs. Additionally, we examined whether training on form and motion coherence thresholds improves internal noise filtering and sampling efficiency. Results: Our results revealed significant learning effects on the trained task, enhancing the perception of dynamic GPs. Furthermore, there was a substantial learning transfer to the non-trained stimulus (mRDKs) and partial transfer to a different task. The data also showed differences in coherence thresholds between dynamic GPs and mRDKs, with GPs showing lower coherence thresholds than mRDKs. Finally, an interaction between visual stimulus type and session for sampling efficiency revealed that the effect of training session on participants’ performance varied depending on the type of visual stimulus, with dynamic GPs being influenced differently than mRDKs. Conclusion: These findings highlight the complexity of perceptual learning and suggest that the transfer of learning effects may be influenced by the specific characteristics of both the training stimuli and tasks, providing valuable insights for future research in visual processing

Visual perceptual learning of form–motion integration: Exploring the involved mechanisms with transfer effects and the equivalent noise approach

Background: Visual perceptual learning plays a crucial role in shaping our understanding of how the human brain integrates visual cues to construct coherent perceptual experiences. The visual system is continually challenged to integrate a multitude of visual cues, including form and motion, to create a unified representation of the surrounding visual scene. This process involves both the processing of local signals and their integration into a coherent global percept. Over the past several decades, researchers have explored the mechanisms underlying this integration, focusing on concepts such as internal noise and sampling efficiency, which pertain to local and global processing, respectively. Objectives and Methods: In this study, we investigated the influence of visual perceptual learning on non-directional motion processing using dynamic Glass patterns (GPs) and modified Random-Dot Kinematograms (mRDKs). We also explored the mechanisms of learning transfer to different stimuli and tasks. Specifically, we aimed to assess whether visual perceptual learning based on illusory directional motion, triggered by form and motion cues (dynamic GPs), transfers to stimuli that elicit comparable illusory motion, such as mRDKs. Additionally, we examined whether training on form and motion coherence thresholds improves internal noise filtering and sampling efficiency. Results: Our results revealed significant learning effects on the trained task, enhancing the perception of dynamic GPs. Furthermore, there was a substantial learning transfer to the non-trained stimulus (mRDKs) and partial transfer to a different task. The data also showed differences in coherence thresholds between dynamic GPs and mRDKs, with GPs showing lower coherence thresholds than mRDKs. Finally, an interaction between visual stimulus type and session for sampling efficiency revealed that the effect of training session on participants’ performance varied depending on the type of visual stimulus, with dynamic GPs being influenced differently than mRDKs. Conclusion: These findings highlight the complexity of perceptual learning and suggest that the transfer of learning effects may be influenced by the specific characteristics of both the training stimuli and tasks, providing valuable insights for future research in visual processing

Characterization of breast tissues in density and effective atomic number basis via spectral X-ray computed tomography

Differentiation of breast tissues is challenging in X-ray imaging because tissues might share similar or even the same linear attenuation coefficients $\mu$. Spectral computed tomography (CT) allows for more quantitative characterization in terms of tissue density and effective atomic number by exploiting the energy dependence of $\mu$. In this work, 5 mastectomy samples and a phantom with inserts mimicking breast soft tissues were evaluated in a retrospective study. The samples were imaged at three monochromatic energy levels in the range of 24 - 38 keV at 5 mGy per scan using a propagation-based phase-contrast setup at SYRMEP beamline at the Italian national synchrotron Elettra. A custom-made algorithm incorporating CT reconstructions of an arbitrary number of spectral energy channels was developed to extract the density and effective atomic number of adipose, fibro-glandular, pure glandular, tumor, and skin from regions selected by a radiologist. Preliminary results suggest that, via spectral CT, it is possible to enhance tissue differentiation. It was found that adipose, fibro-glandular and tumorous tissues have average effective atomic numbers (5.94 $\pm$ 0.09, 7.03 $\pm$ 0.012, and 7.40 $\pm$ 0.10) and densities (0.90 $\pm$ 0.02, 0.96 $\pm$ 0.02, and 1.07 $\pm$ 0.03 g/cm$^{3}$) and can be better distinguished if both quantitative values are observed together.Comment: 26 pages, 7 figures, submitted to Physics in Medicine and Biolog