图 书 馆
知觉双稳中基于采样的概率推理的神经元适应Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability |
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| 课程网址: | http://videolectures.net/nips2011_reichert_inference/ |
| 主讲教师: | David P Reichert |
| 开课单位: | 爱丁堡大学 |
| 开课时间: | 2012-09-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | It has been argued that perceptual multistability reflects probabilistic inference performed by the brain when sensory input is ambiguous. Alternatively, more traditional explanations of multistability refer to low-level mechanisms such as neuronal adaptation. We employ a Deep Boltzmann Machine (DBM) model of cortical processing to demonstrate that these two different approaches can be combined in the same framework. Based on recent developments in machine learning, we show how neuronal adaptation can be understood as a mechanism that improves probabilistic, sampling-based inference. Using the ambiguous Necker cube image, we analyze the perceptual switching exhibited by the model. We also examine the influence of spatial attention, and explore how binocular rivalry can be modeled with the same approach. Our work joins earlier studies in demonstrating how the principles underlying DBMs relate to cortical processing, and offers novel perspectives on the neural implementation of approximate probabilistic inference in the brain. |
| 关 键 词: | 概率推理; 神经元; 皮质处理 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-06：lxf |
| 阅读次数: | 38 |