Suppr超能文献

MT 神经元的模式运动处理。

Pattern Motion Processing by MT Neurons.

机构信息

NeuroEngineering Laboratory, Department of Biomedical Engineering, The University of Melbourne, Parkville, VIC, Australia.

Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC, Australia.

出版信息

Front Neural Circuits. 2019 Jun 21;13:43. doi: 10.3389/fncir.2019.00043. eCollection 2019.

Abstract

Based on stimulation with plaid patterns, neurons in the Middle Temporal (MT) area of primate visual cortex are divided into two types: pattern and component cells. The prevailing theory suggests that pattern selectivity results from the summation of the outputs of component cells as part of a hierarchical visual pathway. We present a computational model of the visual pathway from primary visual cortex (V1) to MT that suggests an alternate model where the progression from component to pattern selectivity is not required. Using standard orientation-selective V1 cells, end-stopped V1 cells, and V1 cells with extra-classical receptive fields (RFs) as inputs to MT, the model shows that the degree of pattern or component selectivity in MT could arise from the relative strengths of the three V1 input types. Dominance of end-stopped V1 neurons in the model leads to pattern selectivity in MT, while dominance of V1 cells with extra-classical RFs result in component selectivity. This model may assist in designing experiments to further understand motion processing mechanisms in primate MT.

摘要

基于对格子图案的刺激,灵长类视觉皮层中的中颞(MT)区域的神经元分为两种类型:模式细胞和成分细胞。主流理论认为,模式选择性是由作为分层视觉通路一部分的成分细胞输出的总和产生的。我们提出了一个从初级视觉皮层(V1)到 MT 的视觉通路的计算模型,该模型提出了一种替代模型,其中不需要从成分到模式选择性的进展。使用标准的方位选择性 V1 细胞、末端停止 V1 细胞和具有超经典感受野(RF)的 V1 细胞作为 MT 的输入,该模型表明 MT 中的模式或成分选择性的程度可能源于三种 V1 输入类型的相对强度。模型中末端停止 V1 神经元的主导地位导致 MT 中的模式选择性,而具有超经典 RF 的 V1 细胞的主导地位导致成分选择性。该模型可能有助于设计实验,以进一步了解灵长类 MT 中的运动处理机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e1b/6598444/64a736882b95/fncir-13-00043-g0001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验