Neural population clocks: Encoding time in dynamic patterns of neural activity.
Neural population clocks: Encoding time in dynamic patterns of neural activity.
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DOI：
10.1037/bne0000515
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发表时间：
2022-10
影响因子：
1.9
通讯作者：
Buonomano, Dean, V
中科院分区：
文献类型：
--
作者：
Zhou, Shanglin;Buonomano, Dean, V
大脑执行的最基本计算是预测和准备近距离事件的能力。由于时间对预测和感觉运动处理的重要性，大脑已经发展了多种机制，可以在微秒到几天到几天及以后的尺度上讲述和编码时间。融合的实验和计算数据表明，在秒数的规模上，定时依赖于分布在不同大脑区域的各种神经机制。在秒数的不同编码机制中，我们将神经群体时钟和渐强活动区分开为编码时间的不同策略。神经群体时钟的一个实例，神经序列在某些方面代表了时间的最佳和灵活的动态状态。具体而言，神经序列包括一个高维表示，下游区域可以使用该序列使用生物学上合理的学习规则来灵活地生成任意简单且复杂的输出模式。我们建议高级集成区域可能使用高维动力（例如神经序列）来编码时间，从而提供下游区域信息以构建低维坡道的活动，从而可以推动运动和时间期望。
The ability to predict and prepare for near and far future events is among the most fundamental computations the brain performs. Because of the importance of time for prediction and sensorimotor processing the brain has evolved multiple mechanisms to tell and encode time across scales ranging from microseconds to days and beyond. Converging experimental and computational data indicate that on the scale of seconds timing relies on diverse neural mechanisms distributed across different brain areas. Among the different encoding mechanisms on the scale of seconds, we distinguish between neural population clocks and ramping activity as distinct strategies to encode time. One instance of neural population clocks, neural sequences, represent in some ways an optimal and flexible dynamic regime for the encoding of time. Specifically, neural sequences comprise a high-dimensional representation that can be used by downstream areas to flexibly generate arbitrarily simple and complex output patterns using biologically plausible learning rules. We propose that high-level integration areas may use high-dimensional dynamics such as neural sequences to encode time, providing downstream areas information to build low-dimensional ramp-like activity that can drive movements and temporal expectation.
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