Abstract
Decisions as to whether to continue with an ongoing activity or to switch to an alternative are a constant in an animal's natural world, and in particular underlie foraging behavior and performance in food preference tests. Stimuli experienced by the animal both impact the choice and are themselves impacted by the choice, in a dynamic back and forth. Here, we present model neural circuits, based on spiking neurons, in which the choice to switch away from ongoing behavior instantiates this back and forth, arising as a state transition in neural activity. We analyze two classes of circuit, which differ in whether state transitions result from a loss of hedonic input from the stimulus (an "entice to stay" model) or from aversive stimulus-input (a "repel to leave" model). In both classes of model, we find that the mean time spent sampling a stimulus decreases with increasing value of the alternative stimulus, a fact that we linked to the inclusion of depressing synapses in our model. The competitive interaction is much greater in "entice to stay" model networks, which has qualitative features of the marginal value theorem, and thereby provides a framework for optimal foraging behavior. We offer suggestions as to how our models could be discriminatively tested through the analysis of electrophysiological and behavioral data.
Highlights
Decisions as to whether to stay in a current situation or switch to a different one face all animals continuously
We produce two classes of model for such stay-switch decisions, which differ in how decisions to switch stimuli can arise
Along with potentially observable behavioral differences that could distinguish the classes of networks, we found signatures in neural activity, such as oscillation of neural firing rates and a rapid change in rates preceding the time of choice to leave a stimulus
Summary
Decisions as to whether to stay in a current situation or switch to a different one face all animals continuously. In foraging studies, the current source of food necessarily yields diminishing returns, such that at some point in time it is optimal for the animal to seek a higher-quality option. This tendency appears “baked in” to animal behavior: while food sources in laboratory food preference tests remain in constant supply, animals typically switch back and forth between two or more alternatives. Quasi-stable attractor states [5] are patterns of neural activity that are essentially self-sustaining but limited by adaptation processes or fluctuations that eventually lead to a loss of stability and a transition to a new pattern of activity [6]. It is revealing that, according to behavioral data [15], the distribution of bout durations, i. e., when an animal stays at a stimulus (corresponding to the durations of the “stay” state in our model) is approximately exponential, which is a hallmark of noise-induced transitions between discrete states [16]
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