An Artificial Optoelectronic Nociceptor Based on In2S3 Memristor

Nociceptors are an indispensable part of the human nervous system that can sense potential dangers from external environmental stimuli. The biomimetic studies of artificial nociceptors have inspired advanced technology in neuromorphic computing, humanoid robots and artificial visual sensors. In this work, we demonstrate an artificial optoelectronic nociceptor using the memristor of large-area In2S3 thin films. The nociceptor responses not only to electrical stimuli but also illumination of visual light, showing complete nociceptive behaviors of "threshold", "inadaptation", "flabby" and "sensitization". The features of the sensory signal such as responding threshold, relaxation time and sensitivity can be tuned in controllable manner, by the strength and frequency of the external stimuli as well as the biasing of electrostatic gate. Such realization of sensory response to multiple external stimuli in the artificial perceptron demonstrates the feasibility of constructing advanced electronic receptor and artificial human eye.

Interpreting sensory and cognitive signals in the cortical reading network

Receptive field properties measured in the reading portion of the ventral occipital-temporal (VOT) cortex are task- and stimulus-dependent. To understand these effects, we analyzed responses in visual field-maps (V1-3, hV4, VO1) whose signals are likely inputs to the VOT. Within these maps, each voxel contains neurons that are responsive to specific regions of the visual field; these regions can be quantified using the moving bar paradigm and population receptive field (pRF) analysis. We measured pRFs using several types of contrast patterns within the bar (English words, Hebrew words, checkers, and false fonts). Word and false-font stimuli produce estimates that are as much as 3-4 deg closer to the fovea than checker stimuli in all visual field maps, becoming very pronounced in V3, hV4 and VO-1. The responses in the visual field maps suggest that the pRF shifts depend mostly on the visual characteristics of the stimulus, and may be explained by sensory signal models and their known neural circuitry. Responses in the VOT reading regions do not follow the same pattern as the visual maps. The pRF centers are confined to the central five degrees, and the responses to false-fonts differ from the responses to words. To understand these VOT signals, we suggest it is necessary to extend the sensory pRF model to include an explicit cognitive signal that distinguishes words from false-fonts.

Oculomotor freezing tracks perception and is immune to decision bias

The appearance of a salient stimulus rapidly inhibits saccadic eye movements. Curiously, this "oculomotor freezing" reflex is triggered only by stimuli that the participant reports seeing (White & Rolfs, 2016). But is oculomotor freezing linked to the participant's sensory experience, or their decision that a stimulus was present? If it were decision-related, oculomotor freezing should become less prevalent when the participant is induced to have a conservative decision criterion and reports seeing a stimulus less often. Here we manipulated decision criterion in two ways: by adjusting monetary payoffs and stimulus probability in a detection task. These bias manipulations greatly affected participants' explicit reports but did not affect the degree to which microsaccades were inhibited by stimulus presence. In addition, the link between oculomotor freezing and explicit reports was stronger when the decision criterion was conservative rather than liberal. The simplest explanation is that conservative reports of stimulus presence are more often based on a strong sensory signal that also inhibits microsaccades. We conclude that the sensory threshold for oculomotor freezing is independent of decision bias. To the extent that conscious experience is also unaffected by such bias, oculomotor freezing provides an involuntary, implicit indication that a stimulus has entered awareness.

Imagery adds stimulus-specific sensory evidence to perceptual detection

Internally generated imagery and externally triggered perception rely on overlapping sensory processes. This overlap poses a challenge for perceptual reality monitoring: determining whether sensory signals reflect reality or imagination. In this study, we used psychophysics to investigate how imagery and perception interact to determine visual experience. Participants were instructed to detect oriented gratings that gradually appeared in noise while simultaneously either imagining the same grating, a grating perpendicular to the to-be-detected grating, or nothing. We found that, compared to both incongruent imagery and no imagery, congruent imagery caused a leftward shift of the psychometric function relating stimulus contrast to perceptual threshold. We discuss how this effect can best be explained by a model in which imagery adds sensory signal to the perceptual input, thereby increasing the visibility of perceived stimuli. These results suggest that, in contrast to changes in sensory signals caused by self-generated movement, the brain does not discount the influence of self-generated sensory signals on perception.

Temporal binding as multisensory integration: Manipulating perceptual certainty of actions and their effects

It has been proposed that statistical integration of multisensory cues may be a suitable framework to explain temporal binding, that is, the finding that causally related events such as an action and its effect are perceived to be shifted towards each other in time. A multisensory approach to temporal binding construes actions and effects as individual sensory signals, which are each perceived with a specific temporal precision. When they are integrated into one multimodal event, like an action-effect chain, the extent to which they affect this event’s perception depends on their relative reliability. We test whether this assumption holds true in a temporal binding task by manipulating certainty of actions and effects. Two experiments suggest that a relatively uncertain sensory signal in such action-effect sequences is shifted more towards its counterpart than a relatively certain one. This was especially pronounced for temporal binding of the action towards its effect but could also be shown for effect binding. Other conceptual approaches to temporal binding cannot easily explain these results, and the study therefore adds to the growing body of evidence endorsing a multisensory approach to temporal binding.

Specific features of the relationship between creativity and inhibitory control in young adolescents

Inhibitory control develops rather late in ontogenesis since it depends on the development of the prefrontal cortex. There are contradictory data on its relationship with creativity at different stages of ontogenesis. One of the most unstudied age periods is early adolescence. It is considered an age when a child has not yet mastered the subtleties of speech expression. The purpose of the study was to reveal the connections between creativity and inhibitory control in young adolescents. Creativity is assessed with two tests: J. Gilford’s test and E.P. Torrance’s test. The go/go and go/no-go paradigms are used to assess inhibitory control. In the first case, subjects are presented with stimuli with a fractally organized structure. A reaction was required for each stimulus. The second case requires not responding to one of the stimuli to which one had previously developed a response. Each series consists of two identical parts. Data processing is carried out using SPSS software. The study sample consists of 158 students in grades 6-7 of which 61 are boys and 97 are girls. The result of regression analysis shows that none of the parameters of J. Guilford’s test are related to the parameters of inhibitory control. We attribute this to the fact that the test is verbal and adolescents find it difficult to find original solutions in the lexical domain. Overall score and flexibility (according to E.P. Torrance’s test) are related to the efficiency of inhibitory processes in the second part of the go/no-go test and to the quality of grasping the fractal structure of the sensory signal flow.

Reward Enhances Pain Discrimination in Humans

The notion that reward inhibits pain is a well-supported observation in both humans and animals, allowing suppression of pain reflexes to acquired rewarding stimuli. However, a blanket inhibition of pain by reward would also impair pain discrimination. In contrast, early counterconditioning experiments implied that reward might actually spare pain discrimination. To test this hypothesis, we investigated whether discriminative performance was enhanced or inhibited by reward. We found in adult human volunteers ( N = 25) that pain-based discriminative ability is actually enhanced by reward, especially when reward is directly contingent on discriminative performance. Drift-diffusion modeling shows that this relates to an augmentation of the underlying sensory signal strength and is not merely an effect of decision bias. This enhancement of sensory-discriminative pain-information processing suggests that whereas reward can promote reward-acquiring behavior by inhibition of pain in some circumstances, it can also facilitate important discriminative information of the sensory input when necessary.

A Deep Learning Model for Fault Diagnosis with a Deep Neural Network and Feature Fusion on Multi-Channel Sensory Signals

Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic model based on multiple DNNs (MDNNs) and softmax is constructed with the input of fused deep features. The proposed method is verified in intelligent failure recognition for automobile final drive to evaluate its performance. A set of contrastive analyses of several intelligent models based on the Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM) and the proposed deep architecture with single sensory signal and multi-channel sensory signals is implemented. The proposed deep architecture of feature extraction and feature fusion on multi-channel sensory signals can effectively recognize the fault patterns of final drive with the best diagnostic accuracy of 95.84%. The results confirm that the proposed method is more robust and effective than other comparative methods in the contrastive experiments.