Infants' neural processing of facial attractiveness

textThe relationship between infants’ neural processing of and visual preferences for attractive and unattractive faces was investigated through the integration of event-related potential and preferential looking methods. Six-month-olds viewed color images of female faces previously rated by adults for attractiveness. The faces were presented in contrasting pairs of attractiveness (attractive/unattractive) for 1.5-second durations. The results showed that compared to attractive faces, unattractive faces elicited larger N290 amplitudes at left hemisphere electrode sites (PO9) and smaller P400 amplitudes at electrode sites across both hemispheres (PO9 and PO10). There were no significant differences between infants’ overall looking times based on attractiveness, however, a significant relationship was found between amplitude and trial looking time; larger N290 amplitudes were associated with longer trial looking times. The results suggest that compared to attractive faces, unattractive faces require greater cognitive resources and longer initial attention for visual processing.Psycholog

Neural processing of gravity information

The goal of this project was to use the linear acceleration capabilities of the NASA Vestibular Research Facility (VRF) at Ames Research Center to directly examine encoding of linear accelerations in the vestibular system of the cat. Most previous studies, including my own, have utilized tilt stimuli, which at very low frequencies (e.g., 'static tilt') can be considered a reasonably pure linear acceleration (e.g., 'down'); however, higher frequencies of tilt, necessary for understanding the dynamic processing of linear acceleration information, necessarily involves rotations which can stimulate the semicircular canals. The VRF, particularly the Long Linear Sled, has promise to provide controlled pure linear accelerations at a variety of stimulus frequencies, with no confounding angular motion

Microwave neural processing and broadcasting with spintronic nano-oscillators

Can we build small neuromorphic chips capable of training deep networks with billions of parameters? This challenge requires hardware neurons and synapses with nanometric dimensions, which can be individually tuned, and densely connected. While nanosynaptic devices have been pursued actively in recent years, much less has been done on nanoscale artificial neurons. In this paper, we show that spintronic nano-oscillators are promising to implement analog hardware neurons that can be densely interconnected through electromagnetic signals. We show how spintronic oscillators maps the requirements of artificial neurons. We then show experimentally how an ensemble of four coupled oscillators can learn to classify all twelve American vowels, realizing the most complicated tasks performed by nanoscale neurons

Modal Considerations on Information Processing and Computation in the Nervous System

We can characterize computationalism very generally as a complex thesis with two main parts: the thesis that the brain (or the nervous system) is a computational system and the thesis that neural computation explains cognition. As Piccinini and Bahar (2012) point out, over the last six decades, computationalism has been the mainstream theory of cognition. Nevertheless, there is still substantial debate about which type of computation explains cognition, and computationalism itself still remains controversial. My aim in this paper is to make two main contributions to the debate about the first subthesis of computationalism, i.e. that the brain is a computational system. First, I want to offer an accurate elucidation of the notion relevant for understanding computationalism (the notion of computation) and clarify the relation between computation and information as well as the relations between both computation and information processing and the nervous system. Second, I want to argue against a peculiar form of computationalism: the thesis that neural processes are constitutively computational in some sense; that neural processes cannot be realized by a system that is not in some sense computational. I will call this thesis "modal computationalism". In particular, I want to argue that neural processing can be realized by a system that is not a sui generis computer (i. e., a computing system that is neither digital nor analog) and by a system that is not a generic computer (a computer in the most general sense: one that includes digital, analog, and any other kind of computation). Actual neural processing is presumed to be computational in these two senses (Piccinini and Bahar 2012). I will argue that, even if this is true, neural processing can be realized by a computing system that is not of the same kind as those that perform actual neural processing and even by a system that is not computational at all.Fil: Wajnerman Paz, Abel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentin

Development during adolescence of the neural processing of social emotion

In this fMRI study, we investigated the development between adolescence and adulthood of the neural processing of social emotions. Unlike basic emotions (such as disgust and fear), social emotions (such as guilt and embarrassment) require the representation of another's mental states. Nineteen adolescents (10–18 years) and 10 adults (22–32 years) were scanned while thinking about scenarios featuring either social or basic emotions. In both age groups, the anterior rostral medial prefrontal cortex (MPFC) was activated during social versus basic emotion. However, adolescents activated a lateral part of the MPFC for social versus basic emotions, whereas adults did not. Relative to adolescents, adults showed higher activity in the left temporal pole for social versus basic emotions. These results show that, although the MPFC is activated during social emotion in both adults and adolescents, adolescents recruit anterior (MPFC) regions more than do adults, and adults recruit posterior (temporal) regions more than do adolescents

Neural processing of social rejection: the role of schizotypal personality traits

A fear of being rejected can cause perceptions of more insecurity and stress in close relationships. Healthy individuals activate the dorsal anterior cingulate cortex (dACC) when experiencing social rejection, while those who are vulnerable to depression deactivate the dACC presumably to downregulate salience of rejection cues and minimize distress. Schizotypal individuals, characterized by unusual perceptual experiences and/or odd beliefs, are more rejection sensitive than normal. We tested the hypothesis, for the first time, that individuals with high schizotypy also have an altered dACC response to rejection stimuli. Twenty-six healthy individuals, 14 with low schizotypy (LS) and 12 with high schizotypy (HS), viewed depictions of rejection and acceptance and neutral scenes while undergoing functional MRI. Activation maps in LS and HS groups during each image type were compared using SPM5, and their relation to participant mood and subjective ratings of the images was examined. During rejection relative to neutral scenes, LS activated and HS deactivated the bilateral dACC, right superior frontal gyrus, and left ventral prefrontal cortex. Across both groups, a temporo-occipito-parieto-cerebellar network was active during rejection, and a left fronto-parietal network during acceptance, relative to neutral scenes, and the bilateral lingual gyrus during rejection relative to acceptance scenes. Our finding of dACC-dorso-ventral PFC activation in LS, but deactivation in HS individuals when perceiving social rejection scenes suggests that HS individuals attach less salience to and distance themselves from such stimuli. This may enable them to cope with their higher-than-normal sensitivity to rejection

Training of Working Memory Impacts Neural Processing of Vocal Pitch Regulation

Working memory training can improve the performance of tasks that were not trained. Whether auditory-motor integration for voice control can benefit from working memory training, however, remains unclear. The present event-related potential (ERP) study examined the impact of working memory training on the auditory-motor processing of vocal pitch. Trained participants underwent adaptive working memory training using a digit span backwards paradigm, while control participants did not receive any training. Before and after training, both trained and control participants were exposed to frequency-altered auditory feedback while producing vocalizations. After training, trained participants exhibited significantly decreased N1 amplitudes and increased P2 amplitudes in response to pitch errors in voice auditory feedback. In addition, there was a significant positive correlation between the degree of improvement in working memory capacity and the post-pre difference in P2 amplitudes. Training-related changes in the vocal compensation, however, were not observed. There was no systematic change in either vocal or cortical responses for control participants. These findings provide evidence that working memory training impacts the cortical processing of feedback errors in vocal pitch regulation. This enhanced cortical processing may be the result of increased neural efficiency in the detection of pitch errors between the intended and actual feedback

Predictability modulates the affective and sensory-discriminative neural processing of pain

Knowing what is going to happen next, that is, the capacity to predict upcoming events, modulates the extent to which aversive stimuli induce stress and anxiety. We explored this issue by manipulating the temporal predictability of aversive events by means of a visual cue, which was either correlated or uncorrelated with pain stimuli (electric shocks). Subjects reported lower levels of anxiety, negative valence and pain intensity when shocks were predictable. In addition to attenuate focus on danger, predictability allows for correct temporal estimation of, and selective attention to, the sensory input. With functional magnetic resonance imaging, we found that predictability was related to enhanced activity in relevant sensory-discriminative processing areas, such as the primary and secondary sensory cortex and posterior insula. In contrast, the unpredictable more aversive context was correlated to brain activity in the anterior insula and the orbitofrontal cortex, areas associated with affective pain processing. This context also prompted increased activity in the posterior parietal cortex and lateral prefrontal cortex that we attribute to enhanced alertness and sustained attention during unpredictability. (c) 2006 Elsevier Inc. All rights reserved.This study was supported by grants from The Swedish Research Council (2003-5810), The family Hedlund Foundation and Karolinska Institutet. The project was finished in the context of Stockholm Brain Institute.info:eu-repo/semantics/publishedVersio

Accurate and robust image superresolution by neural processing of local image representations

Image superresolution involves the processing of an image sequence to generate a still image with higher resolution. Classical approaches, such as bayesian MAP methods, require iterative minimization procedures, with high computational costs. Recently, the authors proposed a method to tackle this problem, based on the use of a hybrid MLP-PNN architecture. In this paper, we present a novel superresolution method, based on an evolution of this concept, to incorporate the use of local image models. A neural processing stage receives as input the value of model coefficients on local windows. The data dimension-ality is firstly reduced by application of PCA. An MLP, trained on synthetic se-quences with various amounts of noise, estimates the high-resolution image data. The effect of varying the dimension of the network input space is exam-ined, showing a complex, structured behavior. Quantitative results are presented showing the accuracy and robustness of the proposed method

The neural processing of taste

Although there have been many recent advances in the field of gustatory neurobiology, our knowledge of how the nervous system is organized to process information about taste is still far from complete. Many studies on this topic have focused on understanding how gustatory neural circuits are spatially organized to represent information about taste quality (e.g., "sweet", "salty", "bitter", etc.). Arguments pertaining to this issue have largely centered on whether taste is carried by dedicated neural channels or a pattern of activity across a neural population. But there is now mounting evidence that the timing of neural events may also importantly contribute to the representation of taste. In this review, we attempt to summarize recent findings in the field that pertain to these issues. Both space and time are variables likely related to the mechanism of the gustatory neural code: information about taste appears to reside in spatial and temporal patterns of activation in gustatory neurons. What is more, the organization of the taste network in the brain would suggest that the parameters of space and time extend to the neural processing of gustatory information on a much grander scale