Biology subjects

Hermundstad, A. M.

Publications and source records attributed to Hermundstad, A. M..

17 recordsLinked to original sources

bioRxiv2026

Mice navigate complex landscapes by learning a sparse vocabulary of paths

Effective navigation is critical for survival, but how animals contend with the scale, complexity, and self-similarity of natural landscapes is unknown. Here, we investigate spontaneous exploratory mouse behavior, both outdoors and in dark self-similar landscapes made from tens to hundreds of tiles of varying heights. Within 2 hours of exploration, mice detected small covert changes to the landscape and jumped across ledges to invisible landing spots, revealing detailed spatial knowledge. In this process, mice converged on a sparse set of memorized paths. These routes depended on prior experience and increased the compressibility of sensory information over time. Our results demonstrate that mice rapidly learn sparse internal representations of their surroundings and suggest that animals use structured behavioral sequences to reduce the complexity of natural navigation.

animal behavior and cognition↗

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↗

bioRxiv2025

Composing trajectories for rapid inference of navigational goals

Animals efficiently learn to navigate their environment. In the laboratory, naive mice explore their environment via highly structured trajectories and can learn to localize new spatial targets in as few as a handful of trials. It is unclear how such efficient learning is possible, since existing computational models of spatial navigation require far more experience to achieve comparable performance and do not attempt to explain the evolving structure of animal behavior during learning. To inform a new algorithm for rapid learning of navigational goals, we took inspiration from the reliable structure of behavior as mice learned to intercept hidden spatial targets. We designed agents that generate behavioral trajectories by controlling the speed and angular velocity of smooth path segments between anchor points. To rapidly learn good anchors, we use Bayesian inference on the history of rewarded and unrewarded trajectories to infer the probability that an anchor will be successful, and active sampling to trim hypothesized anchors. Agents learn within tens of trials to generate compact trajectories that intercept a target, capturing the evolution of behavioral structure and matching the upper limits of learning efficiency observed in mice. We further show that this algorithm can explain how mice avoid obstacles and rapidly adapt to target switches. Finally, we show that this framework naturally encompasses both egocentric and allocentric strategies for navigation.

animal behavior and cognition↗

bioRxiv2025

Environmental dynamics impact whether matching is optimal

When foraging for resources, animals must often sample many options that yield reward with different probabilities. In such scenarios, many animals have been shown to exhibit "matching", an empirical behavioral observation in which the fraction of rewarded samples is the same across all options. While previous work has shown that matching can be optimal in environments with diminishing returns, this condition is not sufficient to determine optimality. Furthermore, while diminishing returns naturally arise when resources in the environment deplete and take time to be replenished, the specific form of diminishing returns depends on the temporal structure and statistics of the replenishment process. Here, we explore how these environmental properties affect whether matching is optimal. By considering an agent that samples different options with fixed sampling rates, we derive the probability of collecting a reward as a function of these sampling rates for different types of environments, and we analytically determine the conditions under which the optimal sampling-rate policy exhibits matching. When all options are governed by the same replenishment dynamics, we find that optimality gives rise to matching across a wide range of environments. However, when these dynamics differ across options, the optimal policy can deviate from matching. In such cases, the rank-ordering of observed reward probabilities depends only on the qualitative nature of the replenishment process, but not on the specific replenishment rates. As a result, the optimal policy can exhibit underor over-matching depending on how rewarding the different options are. We use this result to identify environmental settings under which performance differs substantially between matching and optimality. Finally, we show how fluctuations in these replenishment rates--which can represent either environmental stochasticity or the agents internal uncertainty about the environment--can accentuate deviations between optimality and matching. Together, these findings deepen our understand of the relationship between environmental variability and behavioral optimality, and they provide testable experimental predictions across a wide range of environmental settings.

animal behavior and cognition↗

bioRxiv2025

Efficient planning and implementation of optimal foraging strategies under energetic constraints

To successfully forage for food, animals must balance the energetic cost of searching for food sources with the energetic benefit of exploiting those sources. While the Marginal Value Theorem provides one normative account of this balance by specifying that a forager should leave a food patch when its energetic yield falls below the average yield of other patches in the environment, it assumes the presence of other readily reachable patches. In natural settings, however, a forager does not know whether it will encounter additional food patches, and it must balance potential energetic costs and benefits accordingly. Upon first encountering a patch of food, it faces a decision of whether and when to leave the patch in search of better options, and when to return if no better options are found. Here, we explore how a forager should structure its search for new food patches when the existence of those patches is unknown, and when searching for those patches requires energy that can only be harvested from a single known food patch. We identify conditions under which it is more favorable to explore the environment in several successive trips rather than in a single long exploration, and we show how the optimal sequence of trips depends on the foragers beliefs about the distribution and nutritional content of food patches in the environment. This optimal strategy is well approximated by a local decision that can be implemented by a simple neural circuit architecture. Together, this work highlights how energetic constraints and prior beliefs shape optimal foraging strategies, and how such strategies can be approximated by simple neural networks that implement local decision rules.

neuroscience↗

bioRxiv2024

A global dopaminergic learning rate enables adaptive foraging across many options

In natural environments, animals must efficiently allocate their choices across multiple concurrently available resources when foraging, a complex decision-making process not fully captured by existing models. To understand how rodents learn to navigate this challenge we developed a novel paradigm in which untrained, water-restricted mice were free to sample from six options rewarded at a range of deterministic intervals and positioned around the walls of a large ([~]2m) arena. Mice exhibited rapid learning, matching their choices to integrated reward ratios across six options within the first session. A reinforcement learning model with separate states for staying or leaving an option and a dynamic, global learning rate was able to accurately reproduce mouse learning and decision-making. Fiber photometry recordings revealed that dopamine in the nucleus accumbens core (NAcC), but not dorsomedial striatum (DMS), more closely reflected the global learning rate than local error-based updating. Altogether, our results provide insight into the neural substrate of a learning algorithm that allows mice to rapidly exploit multiple options when foraging in large spatial environments.

neuroscience↗

bioRxiv2024

A theory of rapid behavioral inferences under the pressure of time

To survive, animals must be able quickly infer the state of their surroundings. For example, to successfully escape an approaching predator, prey must quickly estimate the direction of approach from incoming sensory stimuli and guide their behavior accordingly. Such rapid inferences are particularly challenging because the animal has only a brief window of time to gather sensory stimuli, and yet the accuracy of inference is critical for survival. Due to evolutionary pressures, nervous systems have likely evolved effective computational strategies that enable accurate inferences under strong time limitations. Traditionally, the relationship between the speed and accuracy of inference has been described by the "speed-accuracy tradeoff" (SAT), which quantifies how the average performance of an ideal observer improves as the observer has more time to collect incoming stimuli. While this trial-averaged description can reasonably account for individual inferences made over long timescales, it does not capture individual inferences on short timescales, when trial-to-trial variability gives rise to diverse patterns of error dynamics. We show that an ideal observer can exploit this single-trial structure by adaptively tracking the dynamics of its belief about the state of the environment, which enables it to speed its own inferences and more reliably track its own error, but also causes it to violate the SAT. We show that these features can be used to improve overall performance during rapid escape. The resulting behavior qualitatively reproduces features of escape behavior in the fruit fly Drosophila melanogaster, whose escapes have presumably been highly optimized by natural selection.

neuroscience↗

bioRxiv2022

A vast space of compact strategies for highly efficient decisions

When foraging in dynamic and uncertain environments, animals can benefit from basing their decisions on smart inferences about hidden properties of the world. Typical theoretical approaches to understand the strategies that animals use in such settings combine Bayesian inference and value iteration to derive optimal behavioral policies that maximize total reward given changing beliefs about the environment. However, specifying these beliefs requires infinite numerical precision; with limited resources, this problem can no longer be separated into optimizing inference and optimizing action selections. To understand the space of behavioral policies in this constrained setting, we enumerate and evaluate all possible behavioral programs that can be constructed from just a handful of states. We show that only a small fraction of the top-performing programs can be constructed by approximating Bayesian inference; the remaining programs are structurally or even functionally distinct from Bayesian. To assess structural and functional relationships among all programs, we developed novel tree embedding algorithms; these embeddings, which are capable of extracting different relational structures within the program space, reveal that nearly all good programs are closely connected through single algorithmic "mutations". We demonstrate how one can use such relational structures to efficiently search for good solutions via an evolutionary algorithm. Moreover, these embeddings reveal that the diversity of non-Bayesian behaviors originates from a handful of key mutations that broaden the functional repertoire within the space of good programs. The fact that this diversity of behaviors does not significantly compromise performance suggests a novel approach for studying how these strategies generalize across tasks.

animal behavior and cognition↗

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↗

bioRxiv2022

Perceptual decisions exhibit hallmarks of dynamic Bayesian inference

To interpret the sensory environment, the brain combines ambiguous sensory measurements with context-specific prior experience. But environmental contexts can change abruptly and unpredictably, resulting in uncertainty about the current context. Here we address two questions: how should context-specific prior knowledge optimally guide the interpretation of sensory stimuli in changing environments, and do human decision-making strategies resemble this optimum? We probe these questions with a task in which subjects report the orientation of ambiguous visual stimuli that were drawn from three dynamically switching distributions, representing different environmental contexts. We derive predictions for an ideal Bayesian observer that leverages the statistical structure of the task to maximize decision accuracy and show that its decisions are biased by task context. The magnitude of this decision bias is not a fixed property of the sensory measurement but depends on the observers belief about the current context. The model therefore predicts that decision bias will grow with the reliability of the context cue, the stability of the environment, and with the number of trials since the last context switch. Analysis of human choice data validates all three predictions, providing evidence that the brain continuously updates probabilistic representations of the environment to best interpret an uncertain, ever-changing world. SIGNIFICANCEThe brain relies on prior knowledge to make perceptual inferences when sensory information is ambiguous. However, when the environmental context changes, the appropriate prior knowledge often changes with it. Here, we develop a Bayesian observer model to investigate how to make optimal perceptual inferences when sensory information and environmental context are both uncertain. The behavioral signature of this strategy is a context-appropriate decision bias whose strength grows with the reliability of the context cue, the stability of the environment, and with the number of decisions since the most recent change in context. We identified exactly this pattern in the behavior of human subjects performing a dynamic orientation discrimination task. Together, our results suggest that the brain continuously updates probabilistic representations of the environment to make perceptual decisions in the face of uncertainty over both sensory and contextual information.

neuroscience↗

bioRxiv2022

Maintaining a stable head direction representation in naturalistic visual environments

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--for example, when clouds hide the sun--and prominent landmarks at different distances from the insect may move across the animals 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 flys compass during navigation in immersive naturalistic environments with approachable local landmarks. We found that the flys compass remains stable even in these settings by tethering to available global cues, likely preserving the animals ability to perform compass-driven behaviors such as maintaining a constant heading.

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↗

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↗

bioRxiv2019

Sensitivity to grayscale textures is adapted to natural scene statistics

Previously, in [1], we showed that when sampling is limiting, the efficient coding principle leads to a "variance is salience" hypothesis, and that this hypothesis accounts for visual sensitivity to binary image statistics. Here, using extensive new psychophysical data and image analysis, we show that this hypothesis accounts for visual sensitivity to a large set of grayscale image statistics at a striking level of detail, and also identify the limits of the prediction. We define a 66-dimensional space of local grayscale light-intensity correlations, and measure the relevance of each direction to natural scenes. The "variance is salience" hypothesis predicts that two-point correlations are most salient, and predicts their relative salience. We tested these predictions in a texture-segregation task using un-natural, synthetic textures. As predicted, correlations beyond second order are not salient, and predicted thresholds for over 300 second-order correlations match psychophysical thresholds closely (median fractional error < 0.13).

neuroscience↗

bioRxiv2019

Adaptability and efficiency in neural coding

The ability to adapt to changes in stimulus statistics is a hallmark of sensory systems. Here, we develop a theoretical framework that can account for the dynamics of adaptation from an information-processing perspective. We use this framework to optimize and analyze adaptive sensory codes, and we show that codes optimized for stationary environments can suffer from prolonged periods of poor performance when the environment changes. To mitigate the adversarial effects of these environmental changes, sensory systems must navigate tradeoffs between the ability to accurately encode incoming stimuli, and the ability to rapidly detect and adapt to changes in the distribution of these stimuli. We derive families of codes that balance these objectives, and we demonstrate their close match to experimentally-observed neural dynamics during mean and variance adaptation. Our results provide a unifying perspective on adaptation across a range of sensory systems, environments, and sensory tasks.

neuroscience↗

bioRxiv2017

Adaptive coding for dynamic sensory inference

Behavior relies on the ability of sensory systems to infer properties of the environment from incoming stimuli. The accuracy of inference depends on the fidelity with which behaviorally-relevant properties of stimuli are encoded in neural responses. High-fidelity encodings can be metabolically costly, but low-fidelity encodings can cause errors in inference. Here, we discuss general principles that underlie the tradeoff between encoding cost and inference error. We then derive adaptive encoding schemes that dynamically navigate this tradeoff. These optimal encodings tend to increase the fidelity of the neural representation following a change in the stimulus distribution, and reduce fidelity for stimuli that originate from a known distribution. We predict dynamical signatures of such encoding schemes and demonstrate how known phenomena, such as burst coding and firing rate adaptation, can be understood as hallmarks of optimal coding for accurate inference.

neuroscience↗

bioRxiv2017

Disorder and the neural representation of complex odors: smelling in the real world

Animals smelling in the real world use a small number of receptors to sense a vast number of natural molecular mixtures, and proceed to learn arbitrary associations between odors and valences. Here, we propose a new interpretation of how the architecture of olfactory circuits is adapted to meet these immense complementary challenges. First, the diffuse binding of receptors to many molecules compresses a vast odor space into a tiny receptor space, while preserving similarity. Next, lateral interactions \"densify\" and decorrelate the response, enhancing robustness to noise. Finally, disordered projections from the periphery to the central brain reconfigure the densely packed information into a format suitable for flexible learning of associations and valences. We test our theory empirically using data from Drosophila. Our theory suggests that the neural processing of olfactory information differs from the other senses in its fundamental use of disorder.

neuroscience↗