Accurate path integration in continuous attractor network models of grid cells.
Accurate path integration in continuous attractor network models of grid cells.
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DOI：
10.1371/journal.pcbi.1000291
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发表时间：
2009-02
影响因子：
4.3
通讯作者：
Fiete, Ila R.
中科院分区：
文献类型：
--
作者：
Burak, Yoram;Fiete, Ila R.
大鼠内嗅皮层中的网格细胞对动物在2-D空间中的位置显示出惊人的规律性放电反应，并被假设为形成航位推算的神经基质。然而，当速度输入被集成在现有的网格细胞活动模型中时，误差迅速积累。为了产生类似网格细胞的反应，这些模型需要由外部感官线索触发的频繁重置。各种模型都存在这样的不足之处，这使人们对网格单元系统的航位推算潜力产生了怀疑。在这里，我们专注于准确的路径整合的问题，特别是在连续吸引模型的网格细胞活动。我们表明，与以前的模型相比，连续吸引子模型可以生成规则的三角网格响应，根据输入只编码大鼠的速度和前进方向。我们考虑的网络边界的网络的集成性能的作用，并表明，周期性和非周期性的网络能够准确的路径集成，尽管在其吸引子流形的重要差异。我们量化的速度积分中的错误积累的网络大小和内在的网络内的噪声的函数的速率。通过合理的参数范围和包含尖峰变异性，我们的模型网络可以准确地整合最大10-100米和10 -10分钟的速度输入。这些发现形成了一个概念证明，连续吸引动力学可能是背外侧内侧内嗅皮层速度整合的基础。模拟还产生相关的上界的精度集成，可以实现连续吸引子动态网格细胞网络。我们建议进行实验来测试连续吸引子模型，并将其与单个细胞相互独立地建立其响应的模型区分开来。即使在没有外部感官线索的情况下，觅食的啮齿动物也会对自己的位置进行估计，从而使它们能够以大致直线的方式回家。这种计算被称为航位推算或路径积分。三年前在老鼠身上的一项发现将注意力集中在背外侧内侧内嗅皮层（dMEC）上，因为这是老鼠大脑中可能进行这种计算的位置。在这个区域，所谓的网格细胞在老鼠位于一个三角形网格的任何顶点时都会被激活。在这里，我们提出了一个模型，可以在神经网络中产生类似网格的响应。模型网络的输入传达了关于大鼠速度和航向的信息，与投射到dMEC中的已知输入一致。该网络有效地整合了这些输入，以产生取决于大鼠绝对位置的响应。我们表明，这样的神经网络可以准确地整合位置，并可以重现类似于实验观察到的网格细胞的反应。然后，我们提出了一组实验，可以帮助确定我们提出的机制是否是负责网格细胞的出现和路径整合在大鼠的大脑。
Grid cells in the rat entorhinal cortex display strikingly regular firing responses to the animal's position in 2-D space and have been hypothesized to form the neural substrate for dead-reckoning. However, errors accumulate rapidly when velocity inputs are integrated in existing models of grid cell activity. To produce grid-cell-like responses, these models would require frequent resets triggered by external sensory cues. Such inadequacies, shared by various models, cast doubt on the dead-reckoning potential of the grid cell system. Here we focus on the question of accurate path integration, specifically in continuous attractor models of grid cell activity. We show, in contrast to previous models, that continuous attractor models can generate regular triangular grid responses, based on inputs that encode only the rat's velocity and heading direction. We consider the role of the network boundary in the integration performance of the network and show that both periodic and aperiodic networks are capable of accurate path integration, despite important differences in their attractor manifolds. We quantify the rate at which errors in the velocity integration accumulate as a function of network size and intrinsic noise within the network. With a plausible range of parameters and the inclusion of spike variability, our model networks can accurately integrate velocity inputs over a maximum of ∼10–100 meters and ∼1–10 minutes. These findings form a proof-of-concept that continuous attractor dynamics may underlie velocity integration in the dorsolateral medial entorhinal cortex. The simulations also generate pertinent upper bounds on the accuracy of integration that may be achieved by continuous attractor dynamics in the grid cell network. We suggest experiments to test the continuous attractor model and differentiate it from models in which single cells establish their responses independently of each other. Even in the absence of external sensory cues, foraging rodents maintain an estimate of their position, allowing them to return home in a roughly straight line. This computation is known as dead reckoning or path integration. A discovery made three years ago in rats focused attention on the dorsolateral medial entorhinal cortex (dMEC) as a location in the rat's brain where this computation might be performed. In this area, so-called grid cells fire whenever the rat is on any vertex of a triangular grid that tiles the plane. Here we propose a model that could generate grid-cell-like responses in a neural network. The inputs to the model network convey information about the rat's velocity and heading, consistent with known inputs projecting into the dMEC. The network effectively integrates these inputs to produce a response that depends on the rat's absolute position. We show that such a neural network can integrate position accurately and can reproduce grid-cell-like responses similar to those observed experimentally. We then suggest a set of experiments that could help identify whether our suggested mechanism is responsible for the emergence of grid cells and for path integration in the rat's brain.
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影响因子：
25
作者：
Ernst, UA;Pawelzik, KR;Tsodyks, MV
通讯作者：
Tsodyks, MV
DOI：
10.1109/tsmc.1983.6313075
发表时间：
1983-01-01
期刊：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS
影响因子：
--
作者：
COHEN, MA;GROSSBERG, S
通讯作者：
GROSSBERG, S
DOI：
10.1073/pnas.81.10.3088
发表时间：
1984-01-01
期刊：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES
影响因子：
--
作者：
HOPFIELD, JJ
通讯作者：
HOPFIELD, JJ
影响因子：
2.5
作者：
HEVNER, RF;WONGRILEY, MTT
通讯作者：
WONGRILEY, MTT
影响因子：
5.3
作者：
Fiete, Ila R.;Burak, Yoram;Brookings, Ted
通讯作者：
Brookings, Ted