Behavioural evidence for the existence of a spatiotopic free-viewing saliency map

Recent evidence implicates both basal ganglia and ventrolateral prefrontal cortex (vlPFC) in encoding value memories. However, comparative roles of cortical and basal nodes in value memory are not well understood. Here, single-unit recordings in vlPFC and substantia nigra reticulata (SNr), within macaque monkeys, revealed a larger value signal in SNr that was nevertheless correlated with and had a comparable onset to the vlPFC value signal. The value signal was maintained for many objects (>90) many weeks after reward learning and was resistant to extinction in both regions and to repetition suppression in vlPFC. Both regions showed comparable granularity in encoding expected value and value uncertainty, which was paralleled by enhanced gaze bias during free viewing. The value signal dynamics in SNr could be predicted by combining responses of vlPFC neurons according to their value preferences consistent with a scheme in which cortical neurons reached SNr via direct and indirect pathways.

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On Combining DeepSnake and Global Saliency for Detection of Orchard Apples

Applied Sciences ◽

10.3390/app11146269 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6269

Author(s):

Wang Jing ◽

Wang Leqi ◽

Han Yanling ◽

Zhang Yun ◽

Zhou Ruyan

Keyword(s):

Saliency Detection ◽

Color Difference ◽

Saliency Map ◽

Apple Fruit ◽

Initial Contour ◽

Detection Accuracy ◽

Natural Environments ◽

Fast Detection ◽

Single Target ◽

Training Samples

For the fast detection and recognition of apple fruit targets, based on the real-time DeepSnake deep learning instance segmentation model, this paper provided an algorithm basis for the practical application and promotion of apple picking robots. Since the initial detection results have an important impact on the subsequent edge prediction, this paper proposed an automatic detection method for apple fruit targets in natural environments based on saliency detection and traditional color difference methods. Combined with the original image, the histogram backprojection algorithm was used to further optimize the salient image results. A dynamic adaptive overlapping target separation algorithm was proposed to locate the single target fruit and further to determine the initial contour for DeepSnake, in view of the possible overlapping fruit regions in the saliency map. Finally, the target fruit was labeled based on the segmentation results of the examples. In the experiment, 300 training datasets were used to train the DeepSnake model, and the self-built dataset containing 1036 pictures of apples in various situations under natural environment was tested. The detection accuracy of target fruits under non-overlapping shaded fruits, overlapping fruits, shaded branches and leaves, and poor illumination conditions were 99.12%, 94.78%, 90.71%, and 94.46% respectively. The comprehensive detection accuracy was 95.66%, and the average processing time was 0.42 s in 1036 test images, which showed that the proposed algorithm can effectively separate the overlapping fruits through a not-very-large training samples and realize the rapid and accurate detection of apple targets.

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Perceiving Music Through the Lens of Synaesthesia and Absolute Pitch

Perception ◽

10.1177/03010066211034439 ◽

2021 ◽

pp. 030100662110344

Author(s):

Solange Glasser

Keyword(s):

Music Perception ◽

Research Effort ◽

Absolute Pitch ◽

Potential Relationship ◽

Current Thinking ◽

Music Literature ◽

Behavioural Evidence

Synaesthesia and absolute pitch (AP) are two rare conditions that occur more frequently within populations of artistic professionals. Current thinking surrounding synaesthesia and AP and their relationship to music perception form the focus of this article. Given that synaesthesia has rarely been discussed in the music literature, the article surveys and consolidates general neurobiological, psychological, and behavioural evidence to summarise what is currently known on this topic, in order to link this back to the conditions that most relate to music. In contrast, research on AP is now well established in the music literature, but the important gap of linking AP to other conditions such as synaesthesia has yet to be fully explored. This article investigates the potential relationship between synaesthesia and AP for musicians who possess both conditions by systematically comparing the definitions, classifications, prevalence, diagnoses, and impacts on music perception of synaesthesia and AP and provides insights into the varying states of the literature and knowledge of both conditions. In so doing, this article aims to facilitate a greater understanding of music and auditory forms of synaesthesia and their interaction with AP and encourage increased research effort on this important topic.

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Explaining Neural Networks Using Attentive Knowledge Distillation

Sensors ◽

10.3390/s21041280 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1280

Author(s):

Hyeonseok Lee ◽

Sungchan Kim

Keyword(s):

Neural Networks ◽

Model Prediction ◽

Saliency Map ◽

Model Parameters ◽

Learning Capability ◽

Fine Grained ◽

Network Layers ◽

Saliency Maps ◽

Novel Approach ◽

Knowledge Distillation

Explaining the prediction of deep neural networks makes the networks more understandable and trusted, leading to their use in various mission critical tasks. Recent progress in the learning capability of networks has primarily been due to the enormous number of model parameters, so that it is usually hard to interpret their operations, as opposed to classical white-box models. For this purpose, generating saliency maps is a popular approach to identify the important input features used for the model prediction. Existing explanation methods typically only use the output of the last convolution layer of the model to generate a saliency map, lacking the information included in intermediate layers. Thus, the corresponding explanations are coarse and result in limited accuracy. Although the accuracy can be improved by iteratively developing a saliency map, this is too time-consuming and is thus impractical. To address these problems, we proposed a novel approach to explain the model prediction by developing an attentive surrogate network using the knowledge distillation. The surrogate network aims to generate a fine-grained saliency map corresponding to the model prediction using meaningful regional information presented over all network layers. Experiments demonstrated that the saliency maps are the result of spatially attentive features learned from the distillation. Thus, they are useful for fine-grained classification tasks. Moreover, the proposed method runs at the rate of 24.3 frames per second, which is much faster than the existing methods by orders of magnitude.

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