Biology subjects

Hulse, B. K.

Publications and source records attributed to Hulse, B. K..

6 recordsLinked to original sources

bioRxiv2026

Hidden symmetries in network connectivity support ring attractor dynamics in the fly's neural compass

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 networks 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.

neuroscience↗

bioRxiv2023

A rotational velocity estimate constructed through visuomotor competition updates the fly's neural compass

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 flys 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 flys 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.

neuroscience↗

bioRxiv2022

Accurate angular integration with only a handful of neurons

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 animals 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 animals movements to guide navigation, and it informs our understanding of the functional capabilities of discrete systems more broadly.

neuroscience↗

bioRxiv2021

Flexible control of behavioral variability mediated by an internal representation of head direction

Anchoring goals to spatial representations enables flexible navigation in both animals and artificial agents. However, using this strategy can be challenging in novel environments, when both spatial and goal representations must be acquired quickly and simultaneously. Here, we propose a framework for how Drosophila use their internal representation of head direction to build a goal heading representation upon selective thermal reinforcement. We show that flies in a well-established operant visual learning paradigm use stochastically generated fixations and directed saccades to express heading preferences, and that compass neurons, which represent flies head direction, are required to modify these preferences based on reinforcement. We describe how flies ability to quickly map their surroundings and adapt their behavior to the rules of their environment may rest on a behavioral policy whose parameters are flexible but whose form and dependence on head direction and goal representations are genetically encoded in the modular structure of their circuits. Using a symmetric visual setting, which predictably alters the dynamics of the head direction system, enabled us to describe how interactions between the evolving representations of head direction and goal impact behavior. We show how a policy tethered to these two internal representations can facilitate rapid learning of new goal headings, drive more exploitative behavior about stronger goal headings, and ensure that separate learning processes involved in mapping the environment and forming goals within that environment remain consistent with one another. Many of the mechanisms we outline may be broadly relevant for rapidly adaptive behavior driven by internal representations.

neuroscience↗

bioRxiv2021

Inhibition is the hallmark of CA3 intracellular dynamics around awake ripples

Hippocampal ripples are transient population bursts that structure cortico-hippocampal communication and play a central role in memory processing. However, the mechanisms controlling ripple initiation in behaving animals remain poorly understood. Here we combine multisite extracellular and whole cell recordings in awake mice to contrast the brain state and ripple modulation of subthreshold dynamics across hippocampal subfields. We find that entorhinal input to DG exhibits UP and DOWN dynamics with ripples occurring exclusively in UP states. While elevated cortical input in UP states generates depolarization in DG and CA1, it produces persistent hyperpolarization in CA3 neurons. Furthermore, growing inhibition is evident in CA3 throughout the course of the ripple buildup, while DG and CA1 neurons exhibit depolarization transients 100 ms before and during ripples. These observations highlight the importance of CA3 inhibition for ripple generation, while pre-ripple responses indicate a long and orchestrated ripple initiation process in the awake state.

neuroscience↗

bioRxiv2020

A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection

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 Drosophila 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 flys 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.

neuroscience↗