2021/09/25 by Satpreet H. Singh, Floris van Breugel, Singh, Satpreet Harcharan +5
Agricultural and Biological Sciences · Environmental Science · #Artificial Intelligence (cs.AI) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #I.2.0 #I.2.6 #I.5.1 #Insect Pheromone Research and Control #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Neurons and Cognition (q-bio.NC) #Plant Surface Properties and Treatments #Systems and Control (eess.SY) #Wind and Air Flow Studies #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2109.12434
openalex publication_date 2021/09/25 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Tracking a turbulent plume to locate its source is a complex control problem because it requires multi-sensory integration and must be robust to intermittent odors, changing wind direction, and variable plume statistics. This task is routinely performed by flying insects, often over long distances, in pursuit of food or mates. Several aspects of this remarkable behavior have been studied in detail in many experimental studies. Here, we take a complementary in silico approach, using artificial agents trained with reinforcement learning to develop an integrated understanding of the behaviors and neural computations that support plume tracking. Specifically, we use deep reinforcement learning (DRL) to train recurrent neural network (RNN) agents to locate the source of simulated turbulent plumes. Interestingly, the agents' emergent behaviors resemble those of flying insects, and the RNNs learn to represent task-relevant variables, such as head direction and time since last odor encounter. Our analyses suggest an intriguing experimentally testable hypothesis for tracking plumes in changing wind direction -- that agents follow local plume shape rather than the current wind direction. While reflexive short-memory behaviors are sufficient for tracking plumes in constant wind, longer timescales of memory are essential for tracking plumes that switch direction. At the level of neural dynamics, the RNNs' population activity is low-dimensional and organized into distinct dynamical structures, with some correspondence to behavioral modules. Our in silico approach provides key intuitions for turbulent plume tracking strategies and motivates future targeted experimental and theoretical developments.