Integration of velocity-dependent spatio-temporal structure of place cell activation during navigation in a reservoir model of prefrontal cortex

Integration of velocity-dependent spatio-temporal structure of place cell activation during navigation in a reservoir model of prefrontal cortex
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前额皮质储层模型中导航过程中位置细胞激活的速度依赖性时空结构的整合

DOI:
10.1007/s00422-022-00945-6
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发表时间:
2022
影响因子:
1.9
通讯作者:
Dominey, Peter Ford
Dominey, Peter Ford
中科院分区:
工程技术3区
文献类型:
--
作者:
Scleidorovich, Pablo;Weitzenfeld, Alfredo;Fellous, Jean-Marc;Dominey, Peter Ford

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顺序行为在空间和时间上展开。通过改变瞬时速度,可以在相同的总时间内以不同的方式实现相同的空间轨迹。当前的研究调查了速度曲线如何被赋予行为意义以及皮层网络如何编码这些信息。我们首先证明老鼠可以将同一轨迹上的不同速度模式与不同的行为选择联系起来。在这个新颖的实验范例中,老鼠在一个巨大的空间环境中跟随一个小型诱饵机器人,其中老鼠的速度由机器人的速度精确控制。基于这一概念证明和研究表明,循环水库网络非常适合表示时空结构,然后我们在模拟导航环境中测试水库网络,并证明它们可以区分具有相同持续时间但不同速度剖面的同一路径的遍历。然后,我们在具体的机器人设置中测试网络,其中我们使用物理导航机器人的位置单元表示作为输入,并再次成功区分遍历。为了证明这种能力是循环网络所固有的,我们将该模型与简单的线性积分器进行了比较。有趣的是,虽然线性积分器也可以执行速度曲线辨别,但在检查两个模型中的信息编码时出现了明显的差异。储层神经元表现出一种统计混合选择性的形式,作为空间位置和速度之间复杂的相互作用,这在线性积分器中并不丰富。这种混合选择性是皮层和储存库的特征,使我们能够对未来实验中将在大鼠皮层中记录的神经活动进行具体预测。
Sequential behavior unfolds both in space and in time. The same spatial trajectory can be realized in different manners in the same overall time by changing instantaneous speeds. The current research investigates how speed profiles might be given behavioral significance and how cortical networks might encode this information. We first demonstrate that rats can associate different speed patterns on the same trajectory with distinct behavioral choices. In this novel experimental paradigm, rats follow a small baited robot in a large megaspace environment where the rat’s speed is precisely controlled by the robot’s speed. Based on this proof of concept and research showing that recurrent reservoir networks are ideal for representing spatio-temporal structures, we then test reservoir networks in simulated navigation contexts and demonstrate they can discriminate between traversals of the same path with identical durations but different speed profiles. We then test the networks in an embodied robotic setup, where we use place cell representations from physically navigating robots as input and again successfully discriminate between traversals. To demonstrate that this capability is inherent to recurrent networks, we compared the model against simple linear integrators. Interestingly, although the linear integrators could also perform the speed profile discrimination, a clear difference emerged when examining information coding in both models. Reservoir neurons displayed a form of statistical mixed selectivity as a complex interaction between spatial location and speed that was not as abundant in the linear integrators. This mixed selectivity is characteristic of cortex and reservoirs and allows us to generate specific predictions about the neural activity that will be recorded in rat cortex in future experiments.
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