Graduate Program in Biophysical Sciences, University of Chicago, Chicago, Illinois, United States of America.
Laboratoire de physique de l'École normale supérieure, Centre National de la Recherche Scientifique, Paris, France.
PLoS Comput Biol. 2021 Mar 8;17(3):e1008743. doi: 10.1371/journal.pcbi.1008743. eCollection 2021 Mar.
Responding to stimuli requires that organisms encode information about the external world. Not all parts of the input are important for behavior, and resource limitations demand that signals be compressed. Prediction of the future input is widely beneficial in many biological systems. We compute the trade-offs between representing the past faithfully and predicting the future using the information bottleneck approach, for input dynamics with different levels of complexity. For motion prediction, we show that, depending on the parameters in the input dynamics, velocity or position information is more useful for accurate prediction. We show which motion representations are easiest to re-use for accurate prediction in other motion contexts, and identify and quantify those with the highest transferability. For non-Markovian dynamics, we explore the role of long-term memory in shaping the internal representation. Lastly, we show that prediction in evolutionary population dynamics is linked to clustering allele frequencies into non-overlapping memories.
响应刺激需要生物体对外部世界的信息进行编码。输入的并非所有部分对于行为都很重要，并且资源限制要求信号被压缩。在许多生物系统中，对未来输入的预测是广泛有益的。我们使用信息瓶颈方法来计算在真实表示过去和预测未来之间的权衡，针对具有不同复杂程度的输入动态。对于运动预测，我们表明，根据输入动态中的参数，速度或位置信息对于准确预测更为有用。我们展示了哪些运动表示对于在其他运动环境中进行准确预测最容易重复使用，并确定和量化那些具有最高可转移性的表示。对于非马尔可夫动力学，我们探索了长期记忆在塑造内部表示中的作用。最后，我们表明，进化群体动力学中的预测与将等位基因频率聚类到不重叠的记忆中有关。
