Jain Rishabh, Millin Rachel, Mel Bartlett W
J Vis. 2015;15(16):3. doi: 10.1167/15.16.3.
An extrastriate visual area such as V2 or V4 contains neurons selective for a multitude of complex shapes, all sharing a common topographic organization. Simultaneously developing multiple interdigitated maps--hereafter a "multimap"--is challenging in that neurons must compete to generate a diversity of response types locally, while cooperating with their dispersed same-type neighbors to achieve uniform visual field coverage for their response type at all orientations, scales, etc. Previously proposed map development schemes have relied on smooth spatial interaction functions to establish both topography and columnar organization, but by locally homogenizing cells' response properties, local smoothing mechanisms effectively rule out multimap formation. We found in computer simulations that the key requirements for multimap development are that neurons are enabled for plasticity only within highly active regions of cortex designated "learning eligibility regions" (LERs), but within an LER, each cell's learning rate is determined only by its activity level with no dependence on location. We show that a hybrid developmental rule that combines spatial and activity-dependent learning criteria in this way successfully produces multimaps when the input stream contains multiple distinct feature types, or in the degenerate case of a single feature type, produces a V1-like map with "salt-and-pepper" structure. Our results support the hypothesis that cortical maps containing a fine mixture of different response types, whether in monkey extrastriate cortex, mouse V1 or elsewhere in the cortex, rather than signaling a breakdown of map formation mechanisms at the fine scale, are a product of a generic cortical developmental scheme designed to map cells with a diversity of response properties across a shared topographic space.
诸如V2或V4这样的纹外视觉区域包含对多种复杂形状具有选择性的神经元,它们都共享一种共同的拓扑组织。同时发育多个相互交错的图谱——以下简称“多图谱”——具有挑战性,因为神经元必须在局部竞争以产生多种反应类型,同时与分散的同类型邻居协作,以在所有方向、尺度等上实现其反应类型的均匀视野覆盖。先前提出的图谱发育方案依赖于平滑的空间相互作用函数来建立拓扑结构和柱状组织,但通过局部均匀化细胞的反应特性,局部平滑机制有效地排除了多图谱的形成。我们在计算机模拟中发现,多图谱发育的关键要求是,仅在被指定为“学习合格区域”(LERs)的皮质高活性区域内,神经元才具有可塑性,但在一个LER内,每个细胞的学习率仅由其活动水平决定,而不依赖于位置。我们表明,当输入流包含多种不同特征类型时,以这种方式结合空间和活动依赖学习标准的混合发育规则成功地产生了多图谱,或者在单一特征类型的退化情况下,产生了具有“椒盐”结构的类似V1的图谱。我们的结果支持这样一种假设,即包含不同反应类型精细混合的皮质图谱,无论是在猴纹外皮质、小鼠V1还是皮质的其他地方,并非在精细尺度上标志着图谱形成机制的崩溃,而是一种通用皮质发育方案的产物,该方案旨在将具有多种反应特性的细胞映射到共享的拓扑空间中。