The Sensory-Cognitive Interplay: Insights into Neural Mechanisms and Circuits

Senses are our interface for acting in the external world. Consequently, sensory-motor information grounds and drives our higher cognitive processes. At the same time, we are impinged by a multitude of sensory inputs with variable saliency. It is therefore crucial that the process- ing of sensory inputs and motor signals is modulated by cognitive and executive mechanisms such as expectation, memory, attention, emotion, planning, monitoring. This is needed to highlight sensory information that is currently rel- evant for task goals, and to adapt motor control and behav- ior accordingly. The strict intertwining of sensory, motor, and cognitive functions is evidenced in aging and in neuro- logical disorders. Indeed, sensory-motor dysfunctions of- ten accompany higher-level dysfunctions in older popula- tions [1] and in neurological subjects (e.g., in dyslexia, at- tention deficit hyperactivity disorders, or autism spectrum disorders) [2,3] [...

A Neural Mass Model to Simulate Different Rhythms in a Cortical Region

An original neural mass model of a cortical region has been used to investigate the origin of EEG rhythms. The model consists of four interconnected neural populations: pyramidal cells, excitatory interneurons and inhibitory interneurons with slow and fast synaptic kinetics, GABAA, slow and GABAA,fast respectively. A new aspect, not present in previous versions, consists in the inclusion of a self-loop among GABAA,fast interneurons. The connectivity parameters among neural populations have been changed in order to reproduce different EEG rhythms. Moreover, two cortical regions have been connected by using different typologies of long range connections. Results show that the model of a single cortical region is able to simulate the occurrence of multiple power spectral density (PSD) peaks; in particular the new inhibitory loop seems to have a critical role in the activation in gamma (γ) band, in agreement with experimental studies. Moreover the effect of different kinds of connections between two regions has been investigated, suggesting that long range connections toward GABAA,fast interneurons have a major impact than connections toward pyramidal cells. The model can be of value to gain a deeper insight into mechanisms involved in the generation of γ rhythms and to provide better understanding of cortical EEG spectra

X-Ray Emission from the Warm Hot Intergalactic Medium

The number of detected baryons in the Universe at z<0.5 is much smaller than predicted by standard big bang nucleosynthesis and by the detailed observation of the Lyman alpha forest at red-shift z=2. Hydrodynamical simulations indicate that a large fraction of the baryons today is expected to be in a ``warm-hot'' (10^5-10^7K) filamentary gas, distributed in the intergalactic medium. This gas, if it exists, should be observable only in the soft X-ray and UV bands. Using the predictions of a particular hydrodynamic model, we simulated the expected X-ray flux as a function of energy in the 0.1-2 keV band due to the Warm-Hot Intergalactic Medium (WHIM), and compared it with the flux from local and high red-shift diffuse components. Our results show that as much as 20% of the total diffuse X-ray background (DXB) in the energy range 0.37-0.925keV could be due to X-ray flux from the WHIM, 70% of which comes from filaments at redshift z between 0.1 and 0.6. Simulations done using a FOV of 3', comparable with that of Suzaku and Constellation-X, show that in more than 20% of the observations we expect the WHIM flux to contribute to more than 20% of the DXB. These simulations also show that in about 10% of all the observations a single bright filament in the FOV accounts, alone, for more than 20% of the DXB flux. Red-shifted oxygen lines should be clearly visible in these observations.Comment: 19 pages, 6 figure

Alpha and theta mechanisms operating in internal-external attention competition

Attention is the ability to prioritize a set of information at expense of others and can be internally- or externally-oriented. Alpha and theta oscillations have been extensively implicated in attention. However, it is unclear how these oscillations operate when sensory distractors are presented continuously during task-relevant internal processes, in close-to-real-life conditions. Here, EEG signals from healthy participants were obtained at rest and in three attentional conditions, characterized by the execution of a mental math task (internal attention), presentation of pictures on a monitor (external attention), and task execution under the distracting action of picture presentation (internal-external competition). Alpha and theta power were investigated at scalp level and at some cortical regions of interest (ROIs); moreover, functional directed connectivity was estimated via spectral Granger Causality. Results show that frontal midline theta was distinctive of mental task execution and was more prominent during competition compared to internal attention alone, possibly reflecting higher executive control; anterior cingulate cortex appeared as mainly involved and causally connected to distant (temporal/ occipital) regions. Alpha power in visual ROIs strongly decreased in external attention alone, while it assumed values close to rest during competition, reflecting reduced visual engagement against distractors; connectivity results suggested that bidirectional alpha influences between frontal and visual regions could contribute to reduce visual interference in internal attention. This study can help to understand how our brain copes with internal-external attention competition, a condition intrinsic in the human sensory-cognitive interplay, and to elucidate the relationships between brain oscillations and attentional functions/dysfunctions in daily tasks

A Semantic Model to Study Neural Organization of Language in Bilingualism

A neural network model of object semantic representation is used to simulate learning of new words from a foreign language. The network consists of feature areas, devoted to description of object properties, and a lexical area, devoted to words representation. Neurons in the feature areas are implemented as Wilson-Cowan oscillators, to allow segmentation of different simultaneous objects via gamma-band synchronization. Excitatory synapses among neurons in the feature and lexical areas are learned, during a training phase, via a Hebbian rule. In this work, we first assume that some words in the first language (L1) and the corresponding object representations are initially learned during a preliminary training phase. Subsequently, second-language (L2) words are learned by simultaneously presenting the new word together with the L1 one. A competitive mechanism between the two words is also implemented by the use of inhibitory interneurons. Simulations show that, after a weak training, the L2 word allows retrieval of the object properties but requires engagement of the first language. Conversely, after a prolonged training, the L2 word becomes able to retrieve object per se. In this case, a conflict between words can occur, requiring a higher-level decision mechanism

Reconstructing the shape of the correlation function

We develop an estimator for the correlation function which, in the ensemble average, returns the shape of the correlation function, even for signals that have significant correlations on the scale of the survey region. Our estimator is general and works in any number of dimensions. We develop versions of the estimator for both diffuse and discrete signals. As an application, we examine Monte Carlo simulations of X-ray background measurements. These include a realistic, spatially-inhomogeneous population of spurious detector events. We discuss applying the estimator to the averaging of correlation functions evaluated on several small fields, and to other cosmological applications.Comment: 10 pages, 5 figures, submitted to ApJS. Methods and results unchanged but text is expanded and significantly reordered in response to refere

The Two Phases of Galaxy Formation

Cosmological simulations of galaxy formation appear to show a two-phase character with a rapid early phase at z>2 during which in-situ stars are formed within the galaxy from infalling cold gas followed by an extended phase since z<3 during which ex-situ stars are primarily accreted. In the latter phase massive systems grow considerably in mass and radius by accretion of smaller satellite stellar systems formed at quite early times (z>3) outside of the virial radius of the forming central galaxy. These tentative conclusions are obtained from high resolution re-simulations of 39 individual galaxies in a full cosmological context with present-day virial halo masses ranging from 7e11 M_sun h^-1 < M_vir < 2.7e13 M_sun h^-1 and central galaxy masses between 4.5e10 M_sun h^-1 < M_* < 3.6e11 M_sun h^-1. The simulations include the effects of a uniform UV background, radiative cooling, star formation and energetic feedback from SNII. The importance of stellar accretion increases with galaxy mass and towards lower redshift. In our simulations lower mass galaxies ($M_* < 9e10 M_sun h^-1) accrete about 60 per cent of their present-day stellar mass. High mass galaxy ($M_* > 1.7e11 M_sun h^-1) assembly is dominated by accretion and merging with about 80 per cent of the stars added by the present-day. In general the simulated galaxies approximately double their mass since z=1. For massive systems this mass growth is not accompanied by significant star formation. The majority of the in-situ created stars is formed at z>2, primarily out of cold gas flows. We recover the observational result of archaeological downsizing, where the most massive galaxies harbor the oldest stars. We find that this is not in contradiction with hierarchical structure formation. Most stars in the massive galaxies are formed early on in smaller structures, the galaxies themselves are assembled late.Comment: 13 pages, 13 figures, accepted for publication in Ap

A model of working memory for encoding multiple items and ordered sequences exploiting the theta-gamma code

Recent experimental evidence suggests that oscillatory activity plays a pivotal role in the maintenance of information in working memory, both in rodents and humans. In particular, cross-frequency coupling between theta and gamma oscillations has been suggested as a core mechanism for multi-item memory. The aim of this work is to present an original neural network model, based on oscillating neural masses, to investigate mechanisms at the basis of working memory in different conditions. We show that this model, with different synapse values, can be used to address different problems, such as the reconstruction of an item from partial information, the maintenance of multiple items simultaneously in memory, without any sequential order, and the reconstruction of an ordered sequence starting from an initial cue. The model consists of four interconnected layers; synapses are trained using Hebbian and anti-Hebbian mechanisms, in order to synchronize features in the same items, and desynchronize features in different items. Simulations show that the trained network is able to desynchronize up to nine items without a fixed order using the gamma rhythm. Moreover, the network can replicate a sequence of items using a gamma rhythm nested inside a theta rhythm. The reduction in some parameters, mainly concerning the strength of GABAergic synapses, induce memory alterations which mimic neurological deficits. Finally, the network, isolated from the external environment ("imagination phase") and stimulated with high uniform noise, can randomly recover sequences previously learned, and link them together by exploiting the similarity among items

Causality estimates among brain cortical areas by Partial Directed Coherence: simulations and application to real data

The problem of the definition and evaluation of brain connectivity has become a central one in neuroscience during the latest years, as a way to understand the organization and interaction of cortical areas during the execution of cognitive or motor tasks. Among various methods established during the years, the Partial Directed Coherence (PDC) is a frequency-domain approach to this problem, based on a multivariate autoregressive modeling of time series and on the concept of Granger causality. In this paper we propose the use of the PDC method on cortical signals estimated from high resolution EEG recordings, a non invasive method which exhibits a higher spatial resolution than conventional cerebral electromagnetic measures. The principle contributions of this work are the results of a simulation study, testing the performances of PDC, and a statistical analysis (via the ANOVA, analysis of variance) of the influence of different levels of Signal to Noise Ratio and temporal length, as they have been systematically imposed on simulated signals. An application to high resolution EEG recordings during a foot movement is also presented