A topological paradigm for hippocampal spatial map formation using persistent homology.
A topological paradigm for hippocampal spatial map formation using persistent homology.
复制标题
DOI：
10.1371/journal.pcbi.1002581
复制
发表时间：
2012
影响因子：
4.3
通讯作者：
Carlsson G
中科院分区：
文献类型：
--
作者：
Dabaghian Y;Mémoli F;Frank L;Carlsson G
动物在太空中导航的能力取决于它创建环境心理地图的能力。海马体是负责这类地图的大脑区域，它被认为编码几何信息(距离、角度)。然而，考虑到海马区的输出由许多神经元的尖峰模式组成，而下游区域必须能够将这些模式转化为关于动物空间环境的准确信息，我们假设1)神经元放电的时间模式，特别是协同放电，是解码空间信息的关键，以及2)由于协同放电意味着位置场的空间重叠，由协同放电编码的地图将基于连通性和邻接，即它将是一个拓扑图。在这里，我们用一个简单的海马体活动模型来验证这一拓扑假设，在计算机模拟三个拓扑和几何上不同的测试环境中的大鼠轨迹时，改变三个参数(发射速率、位置野大小和神经元数量)。使用基于代数拓扑学领域中持久同调理论最新发展的工具的计算算法，我们发现神经元共同放电的模式实际上可以在生物现实的时间长度内传达关于环境的拓扑信息。此外，我们的模拟显示了一个“学习区域”，它突出了参数之间的相互作用，以组合产生或多或少擅长地图形成的海马体状态。例如，在学习区域内，较少数量的神经元放电可以通过调整放电速率或位置场大小来补偿，但在超过某个点后，地图的形成开始失败。我们认为，这个学习区域提供了一个连贯的理论镜头，通过它来观察通过改变位置、细胞激发率或空间专一性而损害空间学习的条件。我们在环境中导航的能力依赖于我们大脑形成我们所处空间的内部表示的能力。海马体在形成这张内部空间地图中起着核心作用，人们认为活跃的“位置细胞”(对位置敏感的神经元)以某种方式编码了关于环境的计量信息，类似于街道地图。然而，一些考虑向我们暗示，大脑可能对拓扑信息更感兴趣--即连通性、包容性和邻接性，更类似于地铁地图--因此我们使用计算拓扑学中的新方法来估计神经元放电的基本属性如何影响形成三个测试环境的海马体空间地图所需的时间。我们的分析表明，为了在生物学上合理的时间内正确地编码拓扑信息，海马区细胞必须在特定的神经元活动参数内工作，该参数随环境的几何和拓扑属性而变化。这些参数的相互作用形成了一个“学习区域”，在这个区域中，一个参数的变化可以成功地补偿其他参数的变化；然而，超过这个区域限制的值会损害地图的形成。
An animal's ability to navigate through space rests on its ability to create a mental map of its environment. The hippocampus is the brain region centrally responsible for such maps, and it has been assumed to encode geometric information (distances, angles). Given, however, that hippocampal output consists of patterns of spiking across many neurons, and downstream regions must be able to translate those patterns into accurate information about an animal's spatial environment, we hypothesized that 1) the temporal pattern of neuronal firing, particularly co-firing, is key to decoding spatial information, and 2) since co-firing implies spatial overlap of place fields, a map encoded by co-firing will be based on connectivity and adjacency, i.e., it will be a topological map. Here we test this topological hypothesis with a simple model of hippocampal activity, varying three parameters (firing rate, place field size, and number of neurons) in computer simulations of rat trajectories in three topologically and geometrically distinct test environments. Using a computational algorithm based on recently developed tools from Persistent Homology theory in the field of algebraic topology, we find that the patterns of neuronal co-firing can, in fact, convey topological information about the environment in a biologically realistic length of time. Furthermore, our simulations reveal a “learning region” that highlights the interplay between the parameters in combining to produce hippocampal states that are more or less adept at map formation. For example, within the learning region a lower number of neurons firing can be compensated by adjustments in firing rate or place field size, but beyond a certain point map formation begins to fail. We propose that this learning region provides a coherent theoretical lens through which to view conditions that impair spatial learning by altering place cell firing rates or spatial specificity. Our ability to navigate our environments relies on the ability of our brains to form an internal representation of the spaces we're in. The hippocampus plays a central role in forming this internal spatial map, and it is thought that the ensemble of active “place cells” (neurons that are sensitive to location) somehow encode metrical information about the environment, akin to a street map. Several considerations suggested to us, however, that the brain might be more interested in topological information—i.e., connectivity, containment, and adjacency, more akin to a subway map— so we employed new methods in computational topology to estimate how basic properties of neuronal firing affect the time required to form a hippocampal spatial map of three test environments. Our analysis suggests that, in order to encode topological information correctly and in a biologically reasonable amount of time, the hippocampal place cells must operate within certain parameters of neuronal activity that vary with both the geometric and topological properties of the environment. The interplay of these parameters forms a “learning region” in which changes in one parameter can successfully compensate for changes in the others; values beyond the limits of this region, however, impair map formation.
登录
查看更多内容
DOI：
10.1523/jneurosci.2862-08.2008
发表时间：
2008-10-29
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Fenton AA;Kao HY;Neymotin SA;Olypher A;Vayntrub Y;Lytton WW;Ludvig N
通讯作者：
Ludvig N
影响因子：
5.3
作者：
Diba, Kamran;Buzsaki, Gyoergy
通讯作者：
Buzsaki, Gyoergy
影响因子：
3.5
作者：
Buzsáki, G
通讯作者：
Buzsáki, G
影响因子：
2.9
作者：
Brown, EN;Barbieri, R;Frank, LM
通讯作者：
Frank, LM
细胞群揭示刺激空间的结构。
影响因子：
4.3
作者：
Curto C;Itskov V
通讯作者：
Itskov V