Neural networks and perceptual learning

Sensory perception is a learned trait. The brain strategies we use to perceive the world are constantly modified by experience. With practice, we subconsciously become better at identifying familiar objects or distinguishing fine details in our environment. Current theoretical models simulate some properties of perceptual learning, but neglect the underlying cortical circuits. Future neural network models must incorporate the top-down alteration of cortical function by expectation or perceptual tasks. These newly found dynamic processes are challenging earlier views of static and feedforward processing of sensory information.     

Neural mechanisms of perceptual learning

The traditional view of cortical visual processing is that primary visual cortex (V1) analyzes simple visual attributes, and that object recognition involves a progressive “complexification” of receptive field (RF) properties along the visual pathway extending into the temporal lobe. Based on our studies with a combination of electrophysiological, imaging, psychophysical and computational approaches, we find to the contrary that V1 is capable of encoding much more complex stimulus features than originally believed, and that it can integrate information over large parts of the visual field. Moreover, V1 is continually involved in encoding information about learned stimulus configurations under top-down influences specific to the trained perceptual task.     

Neural mechanisms of perceptual learning

The traditional view of cortical visual processing is that primary visual cortex (V1) analyzes simple visual attributes, and that object recognition involves a progressive “complexification” of receptive field (RF) properties along the visual pathway extending into the temporal lobe. Based on our studies with a combination of electrophysiological, imaging, psychophysical and computational approaches, we find to the contrary that V1 is capable of encoding much more complex stimulus features than originally believed, and that it can integrate information over large parts of the visual field. Moreover, V1 is continually involved in encoding information about learned stimulus configurations under top-down influences specific to the trained perceptual task.     

基于小波变换的信号去噪处理

小波变换是信号处理中一个十分有用的数学工具。基于小波分析理论,本文采用软阈值法滤除与有用信号频率相近的噪声信号,取得了很好的滤波效果。并通过实时数字仿真系统验证了这种方法的有效性。     

Consolidation during sleep of perceptual learning of spoken language

Memory consolidation resulting from sleep has been seen broadly: in verbal list learning, spatial learning, and skill acquisition in visual and motor tasks. These tasks do not generalize across spatial locations or motor sequences, or to different stimuli in the same location. Although episodic rote learning constitutes a large part of any organism's learning, generalization is a hallmark of adaptive behaviour. In speech, the same phoneme often has different acoustic patterns depending on context. Training on a small set of words improves performance on novel words using the same phonemes but with different acoustic patterns, demonstrating perceptual generalization. Here we show a role of sleep in the consolidation of a naturalistic spoken-language learning task that produces generalization of phonological categories across different acoustic patterns. Recognition performance immediately after training showed a significant improvement that subsequently degraded over the span of a day's retention interval, but completely recovered following sleep. Thus, sleep facilitates the recovery and subsequent retention of material learned opportunistically at any time throughout the day. Performance recovery indicates that representations and mappings associated with generalization are refined and stabilized during sleep.     

视知觉学习的自组织机制研究

在Hebb学习法和侧向相互作用的基础上,建立了具有知觉学习功能的神经网络模型,以汉字学习过程为例,模拟了视知觉模式从整体知觉到局部知觉的转变.知觉模式的转变源于网络权重的结构性变化,即由初始的随机分布变为块状分布,而局部的侧向相互作用是导致宏观有序结构产生的核心机制.     

感知学习风格及对大学英语分级教学的启示

通过对113名非英语专业学生的调查表明，大学英语分级教学后同一层次的学生，因性别、专业不同仍然存在感知学习风格的差异，而学习者的感知学习风格在一定程度上影响着学习效果。结合分级教学的理论原则，就如何把感知学习风格和提高分级教学效果结合起来提出了建议和设想。     

受噪声影响的复hybrid样本的学习理论关键定理

结合机会测度理论和统计学习理论,提出了机会空间上受噪声影响的复hybrid样本的复期望风险泛函、复经验风险泛函以及复经验风险最小化原则的定义,给出并证明了机会空间上受噪声影响的复hybrid样本的学习理论的关键定理.     

Non-identical residual learning for image enhancement via dynamic multi-level perceptual loss

Residual learning based deep generative networks have achieved promising performance in image enhancement.However,due to the large color gap between a low-quality image and its highquality version,the identical mapping in conventional residual learning cannot explore the elaborate detail differences,resulting in color deviations and texture losses in enhanced images.To address this issue,an innovative non-identical residual learning architecture is proposed,which views image enhancement as two c...     

受噪声影响的复拟随机样本的STL关键定理

引入了拟概率空间上复拟随机样本受噪声影响的复经验风险泛函、复期望风险泛函、复经验风险最小化原则以及严格一致性的定义,提出并证明了拟概率空间上复拟随机样本受噪声影响的学习理论关键定理,为系统建立拟概率空间上基于噪声影响的复拟随机样本的统计学习理论奠定了基础.     

Neural correlates of short-term perceptual learning in orientation discrimination indexed by event-related potentials

The current work investigated the neural correlates of visual perceptual learning in grating orientation discrimination by recording event-related potentials (ERPs) from human adults. Subjects were trained with a discrimination task of grating orientation in three consecutive training sessions within 2 h. While reaction times (RTs) were shortened gradually across training sessions, the N1 was decreased and the P2 was increased over the parietal and occipital areas. A broadly distributed P3 was increased along with more practices. In addition, the time course of learning reflected in the P2 and P3 amplitudes was in line with the changes of reaction times and exhibited a stable level during later training. The impli- cations of these results to the neural mechanisms subserving perceptual learning were discussed.