A neural circuit for flexible control of persistent behavioral states

Abstract

To adapt to their environments, animals must generate behaviors that are closely tuned to a dynamically changing sensory world. However, behavioral states such as foraging or mating typically persist over long time scales to ensure proper execution. It remains unclear how neural circuits generate stable activity patterns to drive behavioral states, while maintaining the flexibility to select among alternative states when the sensory context changes. Here, we elucidate the functional architecture of a neural circuit controlling the choice between exploration and exploitation states during foraging in C. elegans. We identify stable circuit-wide activity patterns underlying each behavioral state and show that feedback between a sensorimotor circuit and two antagonistic neuromodulatory inputs underlies the emergence of these network states. Sensory processing neurons that detect salient food cues can couple to either neuromodulatory system and bias the network towards different states in different sensory contexts. This allows animals to dynamically adjust the balance between exploration and exploitation during foraging. Our results demonstrate that bi-directional communication between sensorimotor and neuromodulatory circuits allows animals to flexibly select behavioral states appropriate for their sensory context. This neural circuit motif may be broadly used in a variety of cases where animals need to balance behavioral persistence with sensitivity to environmental change.3

Behavioral States

“…The abilities of these neuromodulators to drive long consolidated roaming or dwelling states suggests that a winner-takes-all architecture must be present in the neural circuitry that drives these states. Indeed, ensemble calcium imaging during roaming and dwelling states confirms the presence of a winnertakes-all mutual inhibitory loop between the serotonergic neuron NSM, which promotes dwelling, and the MOD-1and PDFR-1-expressing neurons that promote roaming (Ji et al 2020). The activities of these two opposing groups of neurons are mutually exclusive in wild-type animals, but mutants lacking PDF signaling display miscoordinated circuit activity where both cell populations can be simultaneously active.…”

“…When animals detect an increase in the concentration of food odors, they prolong their roaming state to navigate to the food source. But when the concentration of food odors decreases, they transition to dwelling states (Ji et al 2020). Detection of additional chemosensory cues also impacts these states: pheromones that signal a higher density of animals inhibit roaming (Greene et al 2016), and aversive stimuli that signal potential harm can drive a high-speed state reminiscent of roaming (Ardiel et al 2017;Chew et al 2018).…”

Diverse states and stimuli tune olfactory receptor expression levels to modulate food-seeking behavior

“…AgRP neurons in the hypothalamus drive behavioral changes typical of hunger (Betley et al, 2015; Chen et al, 2015; Mandelblat-Cerf et al, 2015), while neurons in the lamina terminalis drive those typical of thirst (Allen et al, 2019; Oka et al, 2015). Likewise, P1 interneurons in Drosophila can trigger a state of social arousal (Hindmarsh Sten et al, 2021; Hoopfer et al, 2015), and serotonergic NSM neurons in C. elegans can trigger dwelling states during foraging (Flavell et al, 2013; Ji et al, 2021; Rhoades et al, 2019; Sawin et al, 2000). These devoted cell populations appear to respond to state-relevant inputs and elicit a suite of behavioral changes that comprise the state.…”

Connecting the dots in ethology: applying network theory to understand neural and animal collectives

“…Although this approach is useful for making predictions about network activity, it is unlikely to provide generalizable insights because there is no one-to-one mapping between dynamics and parameters. For example, neural networks can exhibit the same dynamics despite morphological variations of neurons, heterogeneous circuit parameters, and neuromodulation [68,69,70], and, conversely, networks can support di erent dynamics despite very small variations in connectivity [67].…”