Bernstein Center for Computational Neuroscience Munich and Faculty of Biology, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Germany.
Bernstein Center for Computational Neuroscience Munich and Faculty of Biology, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Germany
J Neurosci. 2019 Apr 10;39(15):2847-2859. doi: 10.1523/JNEUROSCI.2605-18.2019. Epub 2019 Jan 28.
Insects and vertebrates harbor specific neurons that encode the animal's head direction (HD) and provide an internal compass for spatial navigation. Each HD cell fires most strongly in one preferred direction. As the animal turns its head, however, HD cells in rat anterodorsal thalamic nucleus (ADN) and other brain areas fire already before their preferred direction is reached, as if the neurons anticipated the future HD. This phenomenon has been explained at a mechanistic level, but a functional interpretation is still missing. To close this gap, we use a computational approach based on the movement statistics of male rats and a simple model for the neural responses within the ADN HD network. Network activity is read out using population vectors in a biologically plausible manner, so that only past spikes are taken into account. We find that anticipatory firing improves the representation of the present HD by reducing the motion-induced temporal bias inherent in causal decoding. The amount of anticipation observed in ADN enhances the precision of the HD compass read-out by up to 40%. More generally, our theoretical framework predicts that neural integration times not only reflect biophysical constraints, but also the statistics of behaviorally relevant stimuli; in particular, anticipatory tuning should be found wherever neurons encode sensory signals that change gradually in time. Across different brain regions, populations of noisy neurons encode dynamically changing stimuli. Decoding a time-varying stimulus from the population response involves a trade-off: For short read-out times, stimulus estimates are unreliable as the number of stochastic spikes is small; for long read-outs, estimates are biased because they lag behind the true stimulus. We show that optimal decoding of temporally correlated stimuli not only relies on finding the right read-out time window but requires neurons to anticipate future stimulus values. We apply this general framework to the rodent head-direction system and show that the experimentally observed anticipation of future head directions can be explained at a quantitative level from the neuronal tuning properties, network size, and the animal's head-movement statistics.
昆虫和脊椎动物都有特定的神经元，这些神经元编码动物的头部方向（HD），为空间导航提供内部罗盘。每个 HD 细胞在一个首选方向上最强劲地发射。然而，当动物转头时，大鼠前背侧丘脑核（ADN）和其他大脑区域中的 HD 细胞会在其首选方向到达之前就开始发射，就好像神经元预测了未来的 HD 方向一样。这种现象已经在机制层面上得到了解释，但仍缺少功能上的解释。为了弥补这一空白，我们使用了一种基于雄性大鼠运动统计数据和 ADN HD 网络内神经反应的简单模型的计算方法。网络活动以一种生物上合理的方式使用群体向量进行读取，因此只考虑过去的尖峰。我们发现，预测性发射通过减少因果解码中固有的运动诱导的时间偏差，改善了当前 HD 的表示。ADN 中观察到的预测发射量可以将 HD 罗盘读取的精度提高高达 40%。更一般地说，我们的理论框架预测，神经整合时间不仅反映了生物物理限制，还反映了行为相关刺激的统计信息；特别是，只要神经元编码随时间逐渐变化的感觉信号，就应该找到预测性调谐。在不同的大脑区域中，嘈杂神经元的群体编码动态变化的刺激。从群体反应中解码随时间变化的刺激涉及到一种权衡：对于短的读取时间，由于随机尖峰的数量较少，刺激估计不可靠；对于长的读取，估计会产生偏差，因为它们滞后于真实的刺激。我们表明，对时间相关刺激的最佳解码不仅依赖于找到正确的读取时间窗口，还需要神经元预测未来的刺激值。我们将这个一般框架应用于啮齿动物头部方向系统，并表明实验观察到的对未来头部方向的预测可以从神经元调谐特性、网络大小和动物头部运动统计数据的角度在定量水平上得到解释。
