Topic: artificial agents

Memories are thought to be stored in synapses between neurons, but these connections in the hippocampus show complete turnover within a fortnight[1]. Dynamics have also been seen at the representational level, as the cells responding to a particular position change across days even when the animal isn’t overtly learning or forgetting[2]. While some researchers consider representational dynamics to be an inevitable consequence of ongoing learning, others suggest that drift may be beneficial. For instance, theoretical work by Kappel et al.,[3] suggests that if initial learning leads the system t

Tracking a turbulent plume to locate its source is a complex control problem requiring robust multi-sensory integration in the face of intermittent odors, changing wind direction, and variable plume shape. 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 comput

Although reinforcement learning has been highly successful in training artificial agents to perform a wide variety of complex tasks, the principles governing how animals actually learn to perform new tasks remain poorly understood. While normative approaches have provided profound insights into the design of optimal agents, they often make assumptions that may not be aligned with the sub-optimal learning strategies employed by animals in experimental settings. Even in simplified two-alternate force choice task, mice have shown significant variability in learning dynamics despite highly standar