Applications of Information Theory to Analysis of Neural Data

Information theory is a practical and theoretical framework developed for the study of communication over noisy channels. Its probabilistic basis and capacity to relate statistical structure to function make it ideally suited for studying information flow in the nervous system. It has a number of useful properties: it is a general measure sensitive to any relationship, not only linear effects; it has meaningful units which in many cases allow direct comparison between different experiments; and it can be used to study how much information can be gained by observing neural responses in single trials, rather than in averages over multiple trials. A variety of information theoretic quantities are commonly used in neuroscience - (see entry "Definitions of Information-Theoretic Quantities"). In this entry we review some applications of information theory in neuroscience to study encoding of information in both single neurons and neuronal populations.Comment: 8 pages, 2 figure

Síndrome de dermatitis y nefropatía porcino: una revisión sobre su epidemiología, patología y etiología

Porcine dermatitis and nephropathy syndrome (PDNS) is a disease that affects growing and finishing pigs characterized by a sporadic presentation, prevalence lower than 1 % and variable lethality. PDNS affected pigs shows acute multifocal red-to-purple skin lesions and enlarged tan kidneys with petechial hemorrhages. The hallmark microscopic lesions of PDNS are a generalized vasculitis and glomerulonephritis that suggest a type III hypersensitivity reaction. Although the etiology remains unknown, different works have showed the association between porcine circovirus type 2 (PCV-2) and PDNS based on epidemiological evidences, microscopic lesions and, the inconstant detection of PCV-2 antigen and / or nucleic acid in affected tissues. In this article the main characteristics of the disease from an epidemiological, pathological and etiological standpoint are described. Information about national situation is also included.El síndrome de dermatitis y nefropatía porcino (SDNP) es una entidad exclusiva de los cerdos que afecta, en general, a animales de desarrollo y engorde. Su presentación suele ser esporádica con una prevalencia en las granjas afectadas menor al 1% y una letalidad entre el 50 y 100% que varia según la edad. Se caracteriza por la aparición súbita de lesiones multifocales rojo-violáceas en piel y riñones pálidos que se cubren de hemorragias petequiales. La lesión microscópica típica consiste en una vasculitis generalizada y glomérulonefritis sugestivas de una reacción de hipersensibilidad tipo III, mediada por inmunocomplejos. Si bien su etiología no es conocida, distintos trabajos asocian al SDNP con la infección por circovirus porcino tipo 2 (PCV-2) basados en ciertas evidencias epidemiológicas, las características de algunas de las lesiones microscópicas y, aún cuando inconstante, la detección de antígeno y/o ácido nucleico de PCV-2 en tejidos de animales enfermos. En el presente trabajo se describen las principales características de la enfermedad desde el punto de vista de su epidemiología, patología y etiología, incluyendo datos sobre la situación en la Argentina

Local field potential phase and spike timing convey information about different visual features in primary visual cortex

The natural visual environment is characterized by both “what/where” aspects (image features such as contrast or orientation which are defined by the relationship between visual signals simultaneously presented at different points in space) and “when” aspects, describing the temporal variations of the image features. Both “when” and “what/where” information is necessary to describe and understand the natural visual environment, and to take appropriate behavioral decisions. While “where” can be considered embedded as retinotopy, it is likely that localized neural populations in the visual cortex keep a simultaneous representation of both “what” and “when” aspects of the visual stimuli. However, little is yet known about how the spike trains of neurons in primary visual cortex encode both sources of information. The traditional hypothesis in systems neuroscience is that sensory variables are represented by a rate code, i.e. all sensory information is encoded by the number of spikes emitted over relatively long time windows. Although the relevance of rate in encoding static features is well established, this code can be inherently ambiguous in changing environments [1] and it is unlikely that this code is rich enough to represent simultaneously different types of information. Therefore here we explore the hypothesis that the timing of spikes is a crucial variable in representing both “what” and “when” aspects of the natural visual environment. To address these issues, we recorded single unit activity and LFPs in primary visual cortex of opiate anaesthetized macaques during the binocular presentation of naturalistic color movies. By means of computational analysis, we extracted several image features (color, orientation, luminance, space and time contrast, motion) from the receptive fields of each single neuron. We then considered two different spike timing codes previously studied in both the auditory [2] and the visual cortex [3]. In the first code, which we call spike patterns code, sequences of spike times from single neurons are measured (with a resolution of the order of 10 ms) with respect to the time course of the external stimulus. In the second code, which we call phase of firing code, spikes are measured with respect to the phase of the concurrent low frequency LFPs recorded from the same electrode as the spikes. We then used these data to investigate systematically which types of neural codes carry information about the static features of the image and which neural codes carry information about the time course of these features. We found that both “when” and “what” aspects are encoded simultaneously by spike times of visual cortical neurons. However, “what” and “when” are encoded by two different neural information streams; “what” aspects are encoded (on a fine scale of few ms) by spike patterns, and “when” stimulus aspects are encoded by the phase of firing (on a coarse scale of hundreds of ms)

Nonlinear vortex light beams supported and stabilized by dissipation

We describe nonlinear Bessel vortex beams as localized and stationary solutions with embedded vorticity to the nonlinear Schr\"odinger equation with a dissipative term that accounts for the multi-photon absorption processes taking place at high enough powers in common optical media. In these beams, power and orbital angular momentum are permanently transferred to matter in the inner, nonlinear rings, at the same time that they are refueled by spiral inward currents of energy and angular momentum coming from the outer linear rings, acting as an intrinsic reservoir. Unlike vortex solitons and dissipative vortex solitons, the existence of these vortex beams does not critically depend on the precise form of the dispersive nonlinearities, as Kerr self-focusing or self-defocusing, and do not require a balancing gain. They have been shown to play a prominent role in "tubular" filamentation experiments with powerful, vortex-carrying Bessel beams, where they act as attractors in the beam propagation dynamics. Nonlinear Bessel vortex beams provide indeed a new solution to the problem of the stable propagation of ring-shaped vortex light beams in homogeneous self-focusing Kerr media. A stability analysis demonstrates that there exist nonlinear Bessel vortex beams with single or multiple vorticity that are stable against azimuthal breakup and collapse, and that the mechanism that renders these vortexes stable is dissipation. The stability properties of nonlinear Bessel vortex beams explain the experimental observations in the tubular filamentation experiments.Comment: Chapter of boo

Effects of coarse-graining on the scaling behavior of long-range correlated and anti-correlated signals

We investigate how various coarse-graining methods affect the scaling properties of long-range power-law correlated and anti-correlated signals, quantified by the detrended fluctuation analysis. Specifically, for coarse-graining in the magnitude of a signal, we consider (i) the Floor, (ii) the Symmetry and (iii) the Centro-Symmetry coarse-graining methods. We find, that for anti-correlated signals coarse-graining in the magnitude leads to a crossover to random behavior at large scales, and that with increasing the width of the coarse-graining partition interval $\Delta$ this crossover moves to intermediate and small scales. In contrast, the scaling of positively correlated signals is less affected by the coarse-graining, with no observable changes when $\Delta1$ a crossover appears at small scales and moves to intermediate and large scales with increasing $\Delta$. For very rough coarse-graining ($\Delta>3$) based on the Floor and Symmetry methods, the position of the crossover stabilizes, in contrast to the Centro-Symmetry method where the crossover continuously moves across scales and leads to a random behavior at all scales, thus indicating a much stronger effect of the Centro-Symmetry compared to the Floor and the Symmetry methods. For coarse-graining in time, where data points are averaged in non-overlapping time windows, we find that the scaling for both anti-correlated and positively correlated signals is practically preserved. The results of our simulations are useful for the correct interpretation of the correlation and scaling properties of symbolic sequences.Comment: 19 pages, 13 figure

Infección experimental por herpesvirus bovino 1 en conejas gestantes

Natural infection with Bovine herpesvirus 1 (BoHV-1) produces several clinical manifestations including conjunctivitis, respiratory signs, genital diseases and abortion. The rabbit is a good model for studying of latency, pathogenicity of BoHV-5 and other bovine herpesviruses. This study was conducted in order to analyze the response to experimental infection with BoHV-1 in rabbits during different periods of pregnancy. The results obtained could be useful for better understanding of the infection in cattle. A new method of infection was used. The rabbits developed clinical signs. The virus was recovered from nasal swabs and histological lesions were found in the analyzed samples. The humoral response was demonstrated and viral DNA was detected from the placentas. This work showed for the first time the arrival of BoHV-1 to blood after intranasal infection with the virus. It also provides a useful tool that can be used to evaluate the pathogenicity of by BoHV-1 strains, the immune response and to study viral latency and reactivation.La infección natural por Herpesvirus bovino 1 (BoHV-1) se manifiesta por conjuntivitis, signos respiratorios (rinotraqueitis), lesiones genitales (vulvovaginitis pustular infecciosa o balanopostitis) y abortos. El conejo, hasta el momento, ha resultado ser el mejor modelo experimental para estudiar los diferentes aspectos de la infección por BoHV-1, el fenómeno de latencia, la neuropatogenicidad de BoHV-5 y el comportamiento de diferentes cepas virales. Este trabajo se desarrolló con el objetivo de estudiar la respuesta de conejas infectadas con BoHV-1 en diferentes períodos de la gestación para que los datos resultantes puedan ser utilizados para la mejor comprensión de la infección en el bovino. Se utilizó un nuevo método de infección intranasal. Los animales desarrollaron signos clínicos. Se recuperó virus a partir de hisopados nasales, se observaron lesiones histopatológicas en las muestras analizadas, se demostró la respuesta inmune humoral y se detectó ADN viral a partir de placentas de los animales gestantes infectados. En este trabajo se evidenció por primera vez en el modelo conejo la llegada del virus al torrente sanguíneo luego de la infección intranasal. Además se aporta una herramienta de utilidad que puede ser utilizada para evaluar la virulencia de diferentes cepas de BoHV-1 y la respuesta inmune a distintos inmunógenos como también para realizar estudios de latencia y reactivación

Empathy, engagement, entrainment: the interaction dynamics of aesthetic experience

A recent version of the view that aesthetic experience is based in empathy as inner imitation explains aesthetic experience as the automatic simulation of actions, emotions, and bodily sensations depicted in an artwork by motor neurons in the brain. Criticizing the simulation theory for committing to an erroneous concept of empathy and failing to distinguish regular from aesthetic experiences of art, I advance an alternative, dynamic approach and claim that aesthetic experience is enacted and skillful, based in the recognition of others’ experiences as distinct from one’s own. In combining insights from mainly psychology, phenomenology, and cognitive science, the dynamic approach aims to explain the emergence of aesthetic experience in terms of the reciprocal interaction between viewer and artwork. I argue that aesthetic experience emerges by participatory sense-making and revolves around movement as a means for creating meaning. While entrainment merely plays a preparatory part in this, aesthetic engagement constitutes the phenomenological side of coupling to an artwork and provides the context for exploration, and eventually for moving, seeing, and feeling with art. I submit that aesthetic experience emerges from bodily and emotional engagement with works of art via the complementary processes of the perception–action and motion–emotion loops. The former involves the embodied visual exploration of an artwork in physical space, and progressively structures and organizes visual experience by way of perceptual feedback from body movements made in response to the artwork. The latter concerns the movement qualities and shapes of implicit and explicit bodily responses to an artwork that cue emotion and thereby modulate over-all affect and attitude. The two processes cause the viewer to bodily and emotionally move with and be moved by individual works of art, and consequently to recognize another psychological orientation than her own, which explains how art can cause feelings of insight or awe and disclose aspects of life that are unfamiliar or novel to the viewer

Exploring the relationship between video game expertise and fluid intelligence

Hundreds of millions of people play intellectually-demanding video games every day. What does individual performance on these games tell us about cognition? Here, we describe two studies that examine the potential link between intelligence and performance in one of the most popular video games genres in the world (Multiplayer Online Battle Arenas: MOBAs). In the first study, we show that performance in the popular MOBA League of Legends' correlates with fluid intelligence as measured under controlled laboratory conditions. In the second study, we also show that the age profile of performance in the two most widely-played MOBAs (League of Legends and DOTA II) matches that of raw fluid intelligence. We discuss and extend previous videogame literature on intelligence and videogames and suggest that commercial video games can be useful as 'proxy' tests of cognitive performance at a global population level

Multi-task learning for subthalamic nucleus identification in deep brain stimulation

Deep brain stimulation (DBS) of Subthalamic nucleus (STN) is the most successful treatment for advanced Parkinson’s disease. Localization of the STN through Microelectrode recordings (MER) is a key step during the surgery. However, it is a complex task even for a skilled neurosurgeon. Different researchers have developed methodologies for processing and classification of MER signals to locate the STN. Previous works employ the classical paradigm of supervised classification, assuming independence between patients. The aim of this paper is to introduce a patient-dependent learning scenario, where the predictive ability for STN identification at the level of a particular patient, can be used to improve the accuracy for STN identification in other patients. Our inspiration is the multi-task learning framework, that has been receiving increasing interest within the machine learning community in the last few years. To this end, we employ the multi-task Gaussian processes framework that exhibits state of the art performance in multi-task learning problems. In our context, we assume that each patient undergoing DBS is a different task, and we refer to the method as multi-patient learning. We show that the multi-patient learning framework improves the accuracy in the identification of STN in a range from 4.1 to 7.7%, compared to the usual patient-independent setup, for two different datasets. Given that MER are non stationary and noisy signals. Traditional approaches in machine learning fail to recognize accurately the STN during DBS. By contrast in our proposed method, we properly exploit correlations between patients with similar diseases, obtaining an additional information. This information allows to improve the accuracy not only for locating STN for DBS but also for other biomedical signal classification problems