@article {69172, title = {A neural circuit architecture for rapid learning in goal-directed navigation}, journal = {Neuron}, volume = {112}, year = {2024}, month = {2024 May 23}, pages = {2581-2599.e23}, abstract = {

Anchoring goals to spatial representations enables flexible navigation but is challenging in novel environments when both representations must be acquired simultaneously. We propose a framework for how Drosophila uses internal representations of head direction (HD) to build goal representations upon selective thermal reinforcement. We show that flies use stochastically generated fixations and directed saccades to express heading preferences in an operant visual learning paradigm and that HD neurons are required to modify these preferences based on reinforcement. We used a symmetric visual setting to expose how flies{\textquoteright} HD and goal representations co-evolve and how the reliability of these interacting representations impacts behavior. Finally, we describe how rapid learning of new goal headings may rest on a behavioral policy whose parameters are flexible but whose form is genetically encoded in circuit architecture. Such evolutionarily structured architectures, which enable rapidly adaptive behavior driven by internal representations, may be relevant across species.

}, doi = {https://doi.org/10.1016/j.neuron.2024.04.036}, url = {https://doi.org/10.1016/j.neuron.2024.04.036}, author = {Chuntao Dan and Brad K. Hulse and Ramya Kappagantula and Vivek Jayaraman and Ann M. Hermundstad} } @article {69012, title = {A rotational velocity estimate constructed through visuomotor competition updates the fly{\textquoteright}s neural compass}, journal = {bioRxiv}, year = {2023}, month = {2023 Sep 26}, abstract = {

Navigating animals continuously integrate velocity signals to update internal representations of their directional heading and spatial location in the environment. How neural circuits combine sensory and motor information to construct these velocity estimates and how these self-motion signals, in turn, update internal representations that support navigational computations are not well understood. Recent work in Drosophila has identified a neural circuit that performs angular path integration to compute the fly{\textquoteright}s head direction, but the nature of the velocity signal is unknown. Here we identify a pair of neurons necessary for angular path integration that encode the fly{\textquoteright}s rotational velocity with high accuracy using both visual optic flow and motor information. This estimate of rotational velocity does not rely on a moment-to-moment integration of sensory and motor information. Rather, when visual and motor signals are congruent, these neurons prioritize motor information over visual information, and when the two signals are in conflict, reciprocal inhibition selects either the motor or visual signal. Together, our results suggest that flies update their head direction representation by constructing an estimate of rotational velocity that relies primarily on motor information and only incorporates optic flow signals in specific sensorimotor contexts, such as when the motor signal is absent.

}, doi = {10.1101/2023.09.25.559373}, url = {https://doi.org/10.1101/2023.09.25.559373}, author = {Brad K Hulse and Angel Stanoev and Daniel B Turner-Evans and Johannes Seelig and Vivek Jayaraman} } @article {68115, title = {Accurate angular integration with only a handful of neurons.}, journal = {bioRxiv}, year = {2022}, month = {2022 May 25}, abstract = {

To flexibly navigate, many animals rely on internal spatial representations that persist when the animal is standing still in darkness, and update accurately by integrating the animal{\textquoteright}s movements in the absence of localizing sensory cues. Theories of mammalian head direction cells have proposed that these dynamics can be realized in a special class of networks that maintain a localized bump of activity via structured recurrent connectivity, and that shift this bump of activity via angular velocity input. Although there are many different variants of these so-called ring attractor networks, they all rely on large numbers of neurons to generate representations that persist in the absence of input and accurately integrate angular velocity input. Surprisingly, in the fly,\ Drosophila melanogaster, a head direction representation is maintained by a much smaller number of neurons whose dynamics and connectivity resemble those of a ring attractor network. These findings challenge our understanding of ring attractors and their putative implementation in neural circuits. Here, we analyzed failures of angular velocity integration that emerge in small attractor networks with only a few computational units. Motivated by the peak performance of the fly head direction system in darkness, we mathematically derived conditions under which small networks, even with as few as 4 neurons, achieve the performance of much larger networks. The resulting description reveals that by appropriately tuning the network connectivity, the network can maintain persistent representations over the continuum of head directions, and it can accurately integrate angular velocity inputs. We then analytically determined how performance degrades as the connectivity deviates from this optimally-tuned setting, and we find a trade-off between network size and the tuning precision needed to achieve persistence and accurate integration. This work shows how even small networks can accurately track an animal{\textquoteright}s movements to guide navigation, and it informs our understanding of the functional capabilities of discrete systems more broadly.

}, doi = {10.1101/2022.05.23.493052}, url = {https://doi.org/10.1101/2022.05.23.493052}, author = {Marcella Noorman and Brad K Hulse and Vivek Jayaraman and Sandro Romani and Ann M Hermundstad} } @article {68275, title = {Flexible control of behavioral variability mediated by an internal representation of head direction}, journal = {bioRxiv}, year = {2022}, month = {2022 Aug 19}, abstract = {

Internal representations are thought to support the generation of flexible, long-timescale behavioral patterns in both animals and artificial agents. Here, we present a novel conceptual framework for how\ Drosophila\ use their internal representation of head direction to maintain preferred headings in their surroundings, and how they learn to modify these preferences in the presence of selective thermal reinforcement. To develop the framework, we analyzed flies{\textquoteright} behavior in a classical operant visual learning paradigm and found that they use stochastically generated fixations and directed turns to express their heading preferences. Symmetries in the visual scene used in the paradigm allowed us to expose how flies{\textquoteright} probabilistic behavior in this setting is tethered to their head direction representation. We describe how flies{\textquoteright} ability to quickly adapt their behavior to the rules of their environment may rest on a behavioral policy whose parameters are flexible but whose form is genetically encoded in the structure of their circuits. Many of the mechanisms we outline may also be relevant for rapidly adaptive behavior driven by internal representations in other animals, including mammals.

}, doi = {10.1101/2021.08.18.456004}, url = {https://doi.org/10.1101/2021.08.18.456004}, author = {Chuntao Dan and Brad K. Hulse and Vivek Jayaraman and Ann M. Hermundstad} } @article {68103, title = {Maintaining a stable head direction representation in naturalistic visual environments}, journal = {bioRxiv}, year = {2022}, month = {2022 May 18}, abstract = {

Many animals rely on a representation of head direction for flexible, goal-directed navigation. In insects, a compass-like head direction representation is maintained in a conserved brain region called the central complex. This head direction representation is updated by self-motion information and by tethering to sensory cues in the surroundings through a plasticity mechanism. However, under natural settings, some of these sensory cues may temporarily disappear{\textemdash}for example, when clouds hide the sun{\textemdash}and prominent landmarks at different distances from the insect may move across the animal{\textquoteright}s field of view during translation, creating potential conflicts for a neural compass. We used two-photon calcium imaging in head-fixed Drosophila behaving in virtual reality to monitor the fly{\textquoteright}s compass during navigation in immersive naturalistic environments with approachable local landmarks. We found that the fly{\textquoteright}s compass remains stable even in these settings by tethering to available global cues, likely preserving the animal{\textquoteright}s ability to perform compass-driven behaviors such as maintaining a constant heading.

}, doi = {10.1101/2022.05.17.492284}, url = {https://doi.org/10.1101/2022.05.17.492284}, author = {Hannah Haberkern and Shivam S Chitnis and Philip M Hubbard and Tobias Goulet and Ann M Hermundstad and Vivek Jayaraman} }