@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 {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} } @article {68276, title = {A vast space of compact strategies for highly efficient decisions}, journal = {Sci. Adv.}, volume = {10}, year = {2022}, month = {2024 Jun 21}, abstract = {

Inference-based decision-making, which underlies a broad range of behavioral tasks, is typically studied using a small number of handcrafted models. We instead enumerate a complete ensemble of strategies that could be used to effectively, but not necessarily optimally, solve a dynamic foraging task. Each strategy is expressed as a behavioral "program" that uses a limited number of internal states to specify actions conditioned on past observations. We show that the ensemble of strategies is enormous-comprising a quarter million programs with up to five internal states-but can nevertheless be understood in terms of algorithmic "mutations" that alter the structure of individual programs. We devise embedding algorithms that reveal how mutations away from a Bayesian-like strategy can diversify behavior while preserving performance, and we construct a compositional description to link low-dimensional changes in algorithmic structure with high-dimensional changes in behavior. Together, this work provides an alternative approach for understanding individual variability in behavior across animals and tasks.

}, doi = {10.1101/2022.08.10.503471}, url = {https://doi.org/10.1101/2022.08.10.503471}, author = {Tzuhsuan Ma and Ann M Hermundstad} }