Quantitative Life Sciences, The Abdus Salam International Center for Theoretical Physics, 34151 Trieste, Italy.
Department of Physics, Università Degli Studi di Trieste, 34127 Trieste, Italy.
Proc Natl Acad Sci U S A. 2023 Aug 22;120(34):e2304230120. doi: 10.1073/pnas.2304230120. Epub 2023 Aug 14.
Long-range olfactory search is an extremely difficult task in view of the sparsity of odor signals that are available to the searcher and the complex encoding of the information about the source location. Current algorithmic approaches typically require a continuous memory space, sometimes of large dimensionality, which may hamper their optimization and often obscure their interpretation. Here, we show how finite-state controllers with a small set of discrete memory states are expressive enough to display rich, time-extended behavioral modules that resemble the ones observed in living organisms. Finite-state controllers optimized for olfactory search have an immediate interpretation in terms of approximate clocks and coarse-grained spatial maps, suggesting connections with neural models of search behavior.
远程嗅觉搜索是一项极其困难的任务，因为搜索者可用的气味信号稀疏，而且关于源位置的信息编码复杂。目前的算法方法通常需要连续的内存空间，有时维度很大，这可能会阻碍它们的优化，并且常常使它们的解释变得模糊。在这里，我们展示了具有少量离散存储状态的有限状态控制器如何具有足够的表达能力，从而可以显示出类似于在活体生物中观察到的丰富的、时间扩展的行为模块。针对嗅觉搜索进行优化的有限状态控制器可以直接解释为近似时钟和粗粒度的空间图，这表明与搜索行为的神经模型存在联系。
