Center for Systems Neuroscience, Boston University, Boston, Massachusetts 02215, and
Center for Systems Neuroscience, Boston University, Boston, Massachusetts 02215, and.
J Neurosci. 2019 May 1;39(18):3434-3453. doi: 10.1523/JNEUROSCI.1450-18.2019. Epub 2019 Feb 25.
The firing rate of speed cells, a dedicated subpopulation of neurons in the medial entorhinal cortex (MEC), is correlated with running speed. This correlation has been interpreted as a speed code used in various computational models for path integration. These models consider firing rate to be linearly tuned by running speed in real-time. However, estimation of firing rates requires integration of spiking events over time, setting constraints on the temporal accuracy of the proposed speed code. We therefore tested whether the proposed speed code by firing rate is accurate at short time scales using data obtained from open-field recordings in male rats and mice. We applied a novel filtering approach differentiating between speed codes at multiple time scales ranging from deciseconds to minutes. In addition, we determined the optimal integration time window for firing-rate estimation using a general likelihood framework and calculated the integration time window that maximizes the correlation between firing rate and running speed. Data show that these time windows are on the order of seconds, setting constraints on real-time speed coding by firing rate. We further show that optogenetic inhibition of either cholinergic, GABAergic, or glutamatergic neurons in the medial septum/diagonal band of Broca does not affect modulation of firing rates by running speed at each time scale tested. These results are relevant for models of path integration and for our understanding of how behavioral activity states may modulate firing rates and likely information processing in the MEC. Path integration is the most basic form of navigation relying on self-motion cues. Models of path integration use medial septum/diagonal band of Broca (MSDB)-dependent MEC grid-cell firing patterns as the neurophysiological substrate of path integration. These models use a linear speed code by firing rate, but do not consider temporal constraints of integration over time for firing-rate estimation. We show that firing-rate estimation for speed cells requires integration over seconds. Using optogenetics, we show that modulation of firing rates by running speed is independent of MSDB inputs. These results enhance our understanding of path integration mechanisms and the role of the MSDB for information processing in the MEC.
速度细胞的放电率与跑动速度相关,速度细胞是内嗅皮层(MEC)中一个专门的神经元亚群。这种相关性被解释为各种用于路径整合的计算模型中使用的速度码。这些模型认为,放电率实时地由跑动速度线性调节。然而,放电率的估计需要随着时间的推移对尖峰事件进行积分,这对所提出的速度码的时间精度提出了限制。因此,我们使用雄性大鼠和小鼠在开放场记录中获得的数据,在短时间尺度上测试了基于放电率的拟议速度码的准确性。我们应用了一种新颖的滤波方法,在从十分之一秒到几分钟的多个时间尺度上区分速度码。此外,我们使用广义似然框架确定了用于放电率估计的最佳积分时间窗口,并计算了最大化放电率与跑动速度之间相关性的积分时间窗口。数据表明,这些时间窗口的量级为秒,对基于放电率的实时速度编码提出了限制。我们进一步表明,内侧隔核/Broca 斜角带中的胆碱能、GABA 能或谷氨酸能神经元的光遗传学抑制在每个测试的时间尺度上都不会影响跑动速度对放电率的调制。这些结果与路径整合模型相关,并且有助于我们理解行为活动状态如何调节 MEC 中的放电率和可能的信息处理。路径整合是依赖于自身运动线索的最基本的导航形式。路径整合模型使用内侧隔核/Broca 斜角带(MSDB)依赖性 MEC 网格细胞放电模式作为路径整合的神经生理基础。这些模型使用基于放电率的线性速度码,但没有考虑到时间上的积分限制,以便进行放电率估计。我们表明,速度细胞的放电率估计需要进行秒级的积分。使用光遗传学,我们表明跑动速度对放电率的调制独立于 MSDB 输入。这些结果增强了我们对路径整合机制的理解,以及 MSDB 在 MEC 中信息处理中的作用。