Neural circuits that support robust and flexible navigation in dynamic naturalistic environments
Tracking heading within an environment is a fundamental requirement for flexible, goal-directed navigation. In insects, a head-direction representation that guides the animal’s movements is maintained in a conserved brain region called the central complex. Two-photon calcium imaging of genetically targeted neural populations in the central complex of tethered fruit flies behaving in virtual reality (VR) environments has shown that the head-direction representation is updated based on self-motion cues and external sensory information, such as visual features and wind direction. Thus far, the head direction representation has mainly been studied in VR settings that only give flies control of the angular rotation of simple sensory cues. How the fly’s head direction circuitry enables the animal to navigate in dynamic, immersive and naturalistic environments is largely unexplored. I have developed a novel setup that permits imaging in complex VR environments that also accommodate flies’ translational movements. I have previously demonstrated that flies perform visually-guided navigation in such an immersive VR setting, and also that they learn to associate aversive optogenetically-generated heat stimuli with specific visual landmarks. A stable head direction representation is likely necessary to support such behaviors, but the underlying neural mechanisms are unclear. Based on a connectomic analysis of the central complex, I identified likely circuit mechanisms for prioritizing and combining different sensory cues to generate a stable head direction representation in complex, multimodal environments. I am now testing these predictions using calcium imaging in genetically targeted cell types in flies performing 2D navigation in immersive VR.
Neural mechanisms of navigation behavior
The regions of the insect brain devoted to spatial navigation are beautifully orderly, with a remarkably precise pattern of synaptic connections. Thus, we can learn much about the neural mechanisms of spatial navigation by targeting identifiable neurons in these networks for in vivo patch clamp recording and calcium imaging. Our lab has recently discovered that the "compass system" in the Drosophila brain is anchored to not only visual landmarks, but also the prevailing wind direction. Moreover, we found that the compass system can re-learn the relationship between these external sensory cues and internal self-motion cues, via rapid associative synaptic plasticity. Postsynaptic to compass neurons, we found neurons that conjunctively encode heading direction and body-centric translational velocity. We then showed how this representation of travel velocity is transformed from body- to world-centric coordinates at the subsequent layer of the network, two synapses downstream from compass neurons. By integrating this world-centric vector-velocity representation over time, it should be possible for the brain to form a stored representation of the body's path through the environment.
How does the cortex integrate conflicting time-information? A model of temporal averaging
In daily life, we consistently make decisions in pursuit of some goal. Many decisions are informed by multiple sources of information. Unfortunately, these sources often provide ambiguous information about what course of action to take. Therefore, determining how the brain integrates information to resolve this ambiguity is key to understanding the neural mechanisms of decision-making. In the domain of time, this topic can be studied by training subjects to predict when a future event will occur based on distinct cues (e.g., tone, light, etc.). If multiple cues are presented simultaneously and their cue-to-event intervals differ (e.g., tone-10s + light-30s), subjects will often expect the event to occur at the average of their intervals. This ‘temporal averaging’ effect is presumably how the timing system resolves ambiguous time-information. The neural mechanisms of temporal averaging are currently unclear. Here, we will propose how temporal averaging could emerge in cortical circuits using a simple modification of a ‘drift-diffusion’ model of timing.
Neural Mechanisms of Coordination in Duetting Wrens
To communicate effectively, two individuals must take turns to prevent overlap in their signals. How does the nervous system coordinate vocalizations between two individuals? Female and male plain-tailed wrens sing a duet in which they alternate syllable production so rapidly and precisely it sounds as if a single bird is singing. I will talk about experiments that examine the interaction between sensory cues and motor activity, using behavioral manipulations and neurophysiological recordings from pairs of awake, duetting wrens. I will show evidence that auditory cues link the brains of the wrens by modulating motor circuits.
Linking neural representations of space by multiple attractor networks in the entorhinal cortex and the hippocampus
In the past decade evidence has accumulated in favor of the hypothesis that multiple sub-networks in the medial entorhinal cortex (MEC) are characterized by low-dimensional, continuous attractor dynamics. Much has been learned about the joint activity of grid cells within a module (a module consists of grid cells that share a common grid spacing), but little is known about the interactions between them. Under typical conditions of spatial exploration in which sensory cues are abundant, all grid-cells in the MEC represent the animal’s position in space and their joint activity lies on a two-dimensional manifold. However, if the grid cells in a single module mechanistically constitute independent attractor networks, then under conditions in which salient sensory cues are absent, errors could accumulate in the different modules in an uncoordinated manner. Such uncoordinated errors would give rise to catastrophic readout errors when attempting to decode position from the joint grid-cell activity. I will discuss recent theoretical works from our group, in which we explored different mechanisms that could impose coordination in the different modules. One of these mechanisms involves coordination with the hippocampus and must be set up such that it operates across multiple spatial maps that represent different environments. The other mechanism is internal to the entorhinal cortex and independent of the hippocampus.
The Desire to Know: Non-Instrumental Information Seeking in Mice
Animals are motivated to acquire knowledge. A particularly striking example is information seeking behavior: animals often seek out sensory cues that will inform them about the properties of uncertain future rewards, even when there is no way for them to use this information to influence the reward outcome, and even when this information comes at a considerable cost. Evidence from monkey electrophysiology and human fMRI studies suggests that orbitofrontal cortex and midbrain dopamine neurons represent the subjective value of knowledge during information seeking behavior. However, it remains unclear how the brain assigns value to information and how it integrates this with other incentives to drive behavior. We have therefore developed a task to test if information preferences are present in mice and study how informational value is imparted on stimuli. Mice are trained to enter a center port and receive an initial odor that instructs them to either go to an informative side port, go to an uninformative side port, or choose freely between them. The chosen side port then yields a second odor cue followed by a delayed probabilistic water reward. The informative port’s odor cue indicates whether the upcoming reward will be big or small. The uninformative port’s odor cue is uncorrelated with the trial outcome. Crucially, the two ports only differ in their odor cues, not in their water value since both offer identical probabilities of big and small rewards. We find that mice prefer the informative port. This preference is evident as a higher percentage choice of the informative port when given a free choice (67% +/- 1.7%, n = 14, p < 0.03), as well as by faster reaction times when instructed to go to the informative port (544ms +/- 21ms vs 795ms +/- 21ms, n = 14, p < 0.001). The preference for information is robust to within-animal reversals of informative and uninformative port locations, and, moreover, mice are willing to pay for information by choosing the informative port even if its reward amount is reduced to be substantially lower than the uninformative port. These behavioral observations suggest that odor stimuli are imparted with informational value as mice learn the information seeking task. We are currently imaging neural activity in orbitofrontal cortex with microendoscopes to identify changes in neural activity that may reflect value associated with the acquisition of knowledge.