@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} }