Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown Lisbon, Portugal.
Front Neurorobot. 2014 Mar 11;8:10. doi: 10.3389/fnbot.2014.00010. eCollection 2014.
The ability to estimate the passage of time is essential for adaptive behavior in complex environments. Yet, it is not known how the brain encodes time over the durations necessary to explain animal behavior. Under temporally structured reinforcement schedules, animals tend to develop temporally structured behavior, and interval timing has been suggested to be accomplished by learning sequences of behavioral states. If this is true, trial to trial fluctuations in behavioral sequences should be predictive of fluctuations in time estimation. We trained rodents in an duration categorization task while continuously monitoring their behavior with a high speed camera. Animals developed highly reproducible behavioral sequences during the interval being timed. Moreover, those sequences were often predictive of perceptual report from early in the trial, providing support to the idea that animals may use learned behavioral patterns to estimate the duration of time intervals. To better resolve the issue, we propose that continuous and simultaneous behavioral and neural monitoring will enable identification of neural activity related to time perception that is not explained by ongoing behavior.
估计时间的能力对于在复杂环境中进行适应性行为至关重要。然而，目前尚不清楚大脑如何在解释动物行为所需的时间范围内编码时间。在时间结构强化时间表下，动物往往会发展出时间结构的行为，并且已经提出间隔计时是通过学习行为状态的序列来完成的。如果这是真的，那么行为序列在试验间的波动应该可以预测时间估计的波动。我们在一项时长分类任务中对啮齿动物进行训练，同时使用高速摄像机连续监测它们的行为。动物在被计时的时间间隔内表现出高度可重复的行为序列。此外，这些序列通常可以预测试验早期的感知报告，这为动物可能使用习得的行为模式来估计时间间隔的持续时间的观点提供了支持。为了更好地解决这个问题，我们提出，连续和同时的行为和神经监测将能够识别与时间感知相关的神经活动，而这些活动无法用正在进行的行为来解释。
