论文标题
探索强烈的非马克维亚动物行为
Exploring a strongly non-Markovian animal behavior
论文作者
论文摘要
自由步行的苍蝇在强烈的非马克维亚序列中访问了大约100个定型状态。为了探索这些动态，我们开发了信息瓶颈方法的概括，将大量的行为状态压缩为更紧凑的描述，从而最大程度地保留了连续状态之间的相关性。令人惊讶的是，将这些短时间相关性保留到只有两个状态的压缩中捕获了原始数据中长期范围的相关性。将行为降低到二进制序列后，我们通过具有成对相互作用的Ising模型来描述这些序列的分布，这是与两点相关性匹配的最大熵模型。匹配在更长和更长时间的相关函数将所得模型驱动到具有反平方相互作用和接近零磁场的Ising模型。从分析中，关于动物行为的实际数据出现了这种统计物理问题的出现是出乎意料的。
A freely walking fly visits roughly 100 stereotyped states in a strongly non-Markovian sequence. To explore these dynamics, we develop a generalization of the information bottleneck method, compressing the large number of behavioral states into a more compact description that maximally preserves the correlations between successive states. Surprisingly, preserving these short time correlations with a compression into just two states captures the long ranged correlations seen in the raw data. Having reduced the behavior to a binary sequence, we describe the distribution of these sequences by an Ising model with pairwise interactions, which is the maximum entropy model that matches the two-point correlations. Matching the correlation function at longer and longer times drives the resulting model toward the Ising model with inverse square interactions and near zero magnetic field. The emergence of this statistical physics problem from the analysis real data on animal behavior is unexpected.
