@article {70416, title = {Hidden symmetries in network connectivity support ring attractor dynamics in the fly{\textquoteright}s neural compass}, journal = {bioRxiv}, year = {2026}, month = {2026 May 20}, abstract = {

Theoretical models can explain how network structure shapes neural computation, but they typically assume idealized connectivity that is inconsistent with the heterogeneous wiring of biological circuits. We address this issue in the Drosophila head-direction system, a recurrent network with ring-attractor dynamics that enable angular velocity integration. The network{\textquoteright}s symmetric wiring motifs are reminiscent of classical models, but with additional heterogeneity that should, in principle, destabilize attractor dynamics. Inspired by novel architectures discovered through machine-learning-based optimization, we develop an algorithm that transforms attractor models with symmetric connectivity into functionally equivalent models with heterogeneous connectivity. By replacing each unit with multiple clones that preserve its output, the algorithm embeds hidden symmetries in heterogeneous connectivity, maintaining ring-attractor dynamics and accurate integration. Analysis of multiple fly connectomes provides evidence for duplicated units whose connectivity reflects hidden symmetries, consistent with our theory. Our framework helps reconcile idealized models of neural computation with heterogeneous biological circuits.

}, doi = {10.64898/2026.05.18.725766}, url = {https://www.biorxiv.org/content/early/2026/05/20/2026.05.18.725766}, author = {Hulse, Brad K and Aneesh, P.B. and Romani, Sandro and Jayaraman, Vivek and Hermundstad, Ann M} } @article {69632, title = {Maintaining and updating accurate internal representations of continuous variables with a handful of neurons.}, journal = {Nat Neurosci}, year = {2024}, month = {2024 Oct 03}, abstract = {

Many animals rely on persistent internal representations of continuous variables for working memory, navigation, and motor control. Existing theories typically assume that large networks of neurons are required to maintain such representations accurately; networks with few neurons are thought to generate discrete representations. However, analysis of two-photon calcium imaging data from tethered flies walking in darkness suggests that their small head-direction system can maintain a surprisingly continuous and accurate representation. We thus ask whether it is possible for a small network to generate a continuous, rather than discrete, representation of such a variable. We show analytically that even very small networks can be tuned to maintain continuous internal representations, but this comes at the cost of sensitivity to noise and variations in tuning. This work expands the computational repertoire of small networks, and raises the possibility that larger networks could represent more and higher-dimensional variables than previously thought.

}, issn = {1546-1726}, doi = {10.1038/s41593-024-01766-5}, url = {https://10.1038/s41593-024-01766-5}, author = {Noorman, Marcella and Hulse, Brad K and Jayaraman, Vivek and Romani, Sandro and Hermundstad, Ann M} } @article {67771, title = {A connectome of the central complex reveals network motifs suitable for flexible navigation and context-dependent action selection.}, journal = {eLife}, volume = {10}, year = {2021}, month = {2021 Oct 26}, abstract = {

Flexible behaviors over long timescales are thought to engage recurrent neural networks in deep brain regions, which are experimentally challenging to study. In insects, recurrent circuit dynamics in a brain region called the central complex (CX) enable directed locomotion, sleep, and context- and experience-dependent spatial navigation. We describe the first complete electron-microscopy-based connectome of the CX, including all its neurons and circuits at synaptic resolution. We identified new CX neuron types, novel sensory and motor pathways, and network motifs that likely enable the CX to extract the fly{\textquoteright}s head-direction, maintain it with attractor dynamics, and combine it with other sensorimotor information to perform vector-based navigational computations. We also identified numerous pathways that may facilitate the selection of CX-driven behavioral patterns by context and internal state. The CX connectome provides a comprehensive blueprint necessary for a detailed understanding of network dynamics underlying sleep, flexible navigation, and state-dependent action selection.

}, issn = {2050-084X}, doi = {10.7554/eLife.66039}, author = {Hulse, Brad K and Haberkern, Hannah and Franconville, Romain and Turner-Evans, Daniel B and Takemura, Shin-ya and Wolff, Tanya and Noorman, Marcella and Dreher, Marisa and Dan, Chuntao and Parekh, Ruchi and Hermundstad, Ann M and Rubin, Gerald M and Jayaraman, Vivek} } @article {64986, title = {High-performance calcium sensors for imaging activity in neuronal populations and microcompartments.}, journal = {Nature Methods}, volume = {16}, year = {2019}, month = {2019 Jun 17}, pages = {649-57}, abstract = {

Calcium imaging with genetically encoded calcium indicators (GECIs) is routinely used to measure neural activity in intact nervous systems. GECIs are frequently used in one of two different modes: to track activity in large populations of neuronal cell bodies, or to follow dynamics in subcellular compartments such as axons, dendrites and individual synaptic compartments. Despite major advances, calcium imaging is still limited by the biophysical properties of existing GECIs, including affinity, signal-to-noise ratio, rise and decay kinetics and dynamic range. Using structure-guided mutagenesis and neuron-based screening, we optimized the green fluorescent protein-based GECI GCaMP6 for different modes of in vivo imaging. The resulting jGCaMP7 sensors provide improved detection of individual spikes (jGCaMP7s,f), imaging in neurites and neuropil (jGCaMP7b), and may allow tracking larger populations of neurons using two-photon (jGCaMP7s,f) or wide-field (jGCaMP7c) imaging.

}, issn = {1548-7105}, doi = {10.1038/s41592-019-0435-6}, author = {Dana, Hod and Sun, Yi and Mohar, Boaz and Hulse, Brad K and Kerlin, Aaron M and Hasseman, Jeremy P and Tsegaye, Getahun and Tsang, Arthur and Wong, Allan and Patel, Ronak and Macklin, John J and Chen, Yang and Konnerth, Arthur and Jayaraman, Vivek and Looger, Loren L and Schreiter, Eric R and Svoboda, Karel and Kim, Douglas S} } @article {65255, title = {Mechanisms underlying the neural computation of head direction.}, journal = {Annual Review of Neuroscience}, volume = {43}, year = {2019}, month = {2020 Jul 8}, pages = {31-54}, abstract = {

Many animals use an internal sense of direction to guide their movements through the world. Neurons selective to head direction are thought to support this directional sense and have been found in a diverse range of species, from insects to primates, highlighting their evolutionary importance. Across species, most head-direction networks share four key properties: a unique representation of direction at all times, persistent activity in the absence of movement, integration of angular velocity to update the representation, and the use of directional cues to correct drift. The dynamics of theorized network structures called ring attractors elegantly account for these properties, but their relationship to brain circuits is unclear. Here, we review experiments in rodents and flies that offer insights into potential neural implementations of ring attractor networks. We suggest that a theory-guided search across model systems for biological mechanisms that enable such dynamics would uncover general principles underlying head-direction circuit function. Expected final online publication date for the , Volume 43 is July 8, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

}, issn = {1545-4126}, doi = {10.1146/annurev-neuro-072116-031516}, author = {Hulse, Brad K and Jayaraman, Vivek} }