@article {70118, title = {Composing trajectories for rapid inference of navigational goals}, journal = {bioRxiv}, year = {2025}, month = {2025 Sep 24}, abstract = {

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.

}, doi = {10.1101/2025.09.24.678123}, url = {https://www.biorxiv.org/content/early/2025/09/24/2025.09.24.678123}, author = {AbdelRahman, Nada Y and Jiang, Wanchen and Coddington, Luke T and Gong, Sheng and Dudman, Joshua T and Hermundstad, Ann M} } @article {70017, title = {Efficient planning and implementation of optimal foraging strategies under energetic constraints}, journal = {PNAS Nexus}, year = {2025}, month = {2026 Jan 17}, abstract = {

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 forager{\textquoteright}s 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.

}, issn = {2692-8205}, doi = {10.1093/pnasnexus/pgag009}, url = {https://doi.org/10.1093/pnasnexus/pgag009}, author = {Guo, Yipei and Hermundstad, Ann M} } @article {70223, title = {Environmental dynamics impact whether matching is optimal}, journal = {PNAS Nexus}, year = {2025}, month = {2025 Dec 17}, pages = {pgaf392}, abstract = {

Foraging animals often sample options that yield rewards with different probabilities. In such scenarios, many animals exhibit {\textquotedblleft}matching{\textquotedblright}, whereby they allocate their choices such that the fraction of rewarded samples is equal across options. While matching can be optimal in environments with diminishing returns, this condition alone is not sufficient to determine optimality. Moreover, diminishing returns arise when resources deplete and replenish over time, but their form depends on the temporal structure and statistics of replenishment. Here, we investigate how these environmental properties influence whether matching is optimal. We consider an agent that samples options at fixed rates and derive the resulting reward probabilities across different types of environments. This allows us to analytically determine conditions under which the optimal policy exhibits matching. When all options share the same replenishment dynamics, matching emerges as optimal across a wide range of environments. However, when dynamics differ across options, optimal policies can deviate from matching. In such cases, the rank-ordering of observed reward probabilities depends only on the qualitative nature of the replenishment process, and not on the specific replenishment rates. As a result, the optimal policy can exhibit under- or over-matching depending on which options are more rewarding. We use this result to identify environments where performance differs substantially between matching and optimality. Finally, we show that fluctuations in replenishment rates{\textemdash}representing environmental stochasticity or internal uncertainty{\textemdash}can amplify deviations from matching. These findings deepen our understanding of the relationship between environmental variability and behavioral optimality, and provide testable predictions across diverse settings.

}, issn = {2752-6542}, doi = {10.1093/pnasnexus/pgaf392}, url = {https://doi.org/10.1093/pnasnexus/pgaf392}, author = {Guo, Yipei and Hermundstad, Ann M} } @article {69515, title = {Exploration and exploitation are flexibly balanced during local search in flies}, journal = {bioRxiv}, year = {2024}, month = {2024 Jun 28}, abstract = {

After finding food, a foraging animal must decide whether to continue feeding, or to explore the environment for potentially better options. One strategy to negotiate this tradeoff is to perform local searches around the food but repeatedly return to feed. We studied this behavior in flies and used genetic tools to uncover the underlying mechanisms. Over time, flies gradually expand their search, shifting from primarily exploiting food sources to exploring the environment, a change that is likely driven by increases in satiety. We found that flies{\textquoteright} search patterns preserve these dynamics even as the overall scale of the search is modulated by starvation-induced changes in metabolic state. In contrast, search induced by optogenetic activation of sugar sensing neurons does not show these dynamics. We asked what navigational strategies underlie local search. Using a generative model, we found that a change in locomotor pattern after food consumption could account for repeated returns to the food, but failed to capture relatively direct, long return trajectories. Alternative strategies, such as path integration or sensory taxis could allow flies to return from larger distances. We tested this by individually silencing the fly{\textquoteright}s head direction system, olfaction and hygrosensation, and found that the only substantial effect was from perturbing hygrosensation, which reduced the number of long exploratory trips. Our study illustrates that local search is composed of multiple behavioral features that evolve over time based on both internal and external factors, providing a path towards uncovering the underlying neural mechanisms.

}, doi = {10.1101/2024.06.26.600764}, url = {https://www.biorxiv.org/content/early/2024/06/28/2024.06.26.600764}, author = {Goldschmidt, Dennis and Guo, Yipei and Chitnis, Shivam S and Christoforou, Christina and Turner-Evans, Dan and Ribeiro, Carlos and Hermundstad, Ann M and Jayaraman, Vivek and Haberkern, Hannah} } @article {69659, title = {A global dopaminergic learning rate enables adaptive foraging across many options}, journal = {bioRxiv}, year = {2024}, month = {2024 Nov 05}, abstract = {

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.

}, doi = {10.1101/2024.11.04.621923}, url = {https://doi.org/10.1101/2024.11.04.621923}, author = {Grima, Laura L and Guo, Yipei and Narayan, Lakshmi and Hermundstad, Ann M and Dudman, Joshua T} } @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 {69589, title = {A theory of rapid behavioral inferences under the pressure of time}, journal = {PRX Life}, volume = {4}, year = {2024}, month = {2026 Mar 05}, abstract = {

To survive, animals must be able to 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 {\textquotedblleft}speed-accuracy tradeoff{\textquotedblright} (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.

Preprint:\ https://www.doi.org/10.1101/2024.08.26.609738

}, doi = {10.1103/8m59-vf2m}, url = { https://doi.org/10.1103/8m59-vf2m}, author = {Hermundstad, Ann M and M{\l}ynarski, Wiktor F} } @article {68932, title = {Dynamics of cortical contrast adaptation predict perception of signals in noise.}, journal = {Nature Communications}, volume = {14}, year = {2023}, month = {2023 Aug 09}, pages = {4817}, abstract = {

Neurons throughout the sensory pathway adapt their responses depending on the statistical structure of the sensory environment. Contrast gain control is a form of adaptation in the auditory cortex, but it is unclear whether the dynamics of gain control reflect efficient adaptation, and whether they shape behavioral perception. Here, we trained mice to detect a target presented in background noise shortly after a change in the contrast of the background. The observed changes in cortical gain and behavioral detection followed the dynamics of a normative model of efficient contrast gain control; specifically, target detection and sensitivity improved slowly in low contrast, but degraded rapidly in high contrast. Auditory cortex was required for this task, and cortical responses were not only similarly affected by contrast but predicted variability in behavioral performance. Combined, our results demonstrate that dynamic gain adaptation supports efficient coding in auditory cortex and predicts the perception of sounds in noise.

}, keywords = {Acoustic Stimulation, Adaptation, Physiological, Animals, Auditory Cortex, Auditory Perception, Mice, Noise, Sound}, issn = {2041-1723}, doi = {10.1038/s41467-023-40477-6}, author = {Angeloni, Christopher F and M{\l}ynarski, Wiktor and Piasini, Eugenio and Williams, Aaron M and Wood, Katherine C and Garami, Linda and Hermundstad, Ann M and Geffen, Maria N} } @article {68806, title = {Environmental dynamics shape perceptual decision bias.}, journal = {PLoS Computational Biology}, volume = {19}, year = {2023}, month = {2023 Jun 08}, pages = {e1011104}, abstract = {

To interpret the sensory environment, the brain combines ambiguous sensory measurements with knowledge that reflects 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 knowledge about the statistical structure of the task to maximize decision accuracy, including knowledge about the dynamics of the environment. We show that its decisions are biased by the dynamically changing task context. The magnitude of this decision bias depends on the observer{\textquoteright}s continually evolving belief about the current context. The model therefore not only predicts that decision bias will grow as the context is indicated more reliably, but also as the stability of the environment increases, and as the number of trials since the last context switch grows. Analysis of human choice data validates all three predictions, suggesting that the brain leverages knowledge of the statistical structure of environmental change when interpreting ambiguous sensory signals.

}, keywords = {Bayes Theorem, Bias, Brain, Decision Making, Humans, Uncertainty}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1011104}, author = {Charlton, Julie A and M{\l}ynarski, Wiktor F and Bai, Yoon H and Hermundstad, Ann M and Goris, Robbe L T} } @article {68296, title = {Disorder and the Neural Representation of Complex Odors.}, journal = {Frontiers in Computational Neuroscience}, volume = {16}, year = {2022}, month = {2022 Aug 08}, pages = {917786}, abstract = {

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 how the architecture of olfactory circuits leverages disorder, diffuse sensing and redundancy in representation to meet these immense complementary challenges. First, the diffuse and disordered binding of receptors to many molecules compresses a vast but sparsely-structured odor space into a small receptor space, yielding an odor code that preserves similarity in a precise sense. Introducing any order/structure in the sensing degrades similarity preservation. Next, lateral interactions further reduce the correlation present in the low-dimensional receptor code. Finally, expansive disordered projections from the periphery to the central brain reconfigure the densely packed information into a high-dimensional representation, which contains multiple redundant subsets from which downstream neurons can learn flexible associations and valences. Moreover, introducing any order in the expansive projections degrades the ability to recall the learned associations in the presence of noise. We test our theory empirically using data from . Our theory suggests that the neural processing of sparse but high-dimensional olfactory information differs from the other senses in its fundamental use of disorder.

}, issn = {1662-5188}, doi = {10.3389/fncom.2022.917786}, author = {Krishnamurthy, Kamesh and Hermundstad, Ann M and Mora, Thierry and Walczak, Aleksandra M and Balasubramanian, Vijay} } @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 {67675, title = {Structured patterns of activity in pulse-coupled oscillator networks with varied connectivity.}, journal = {PLoS One}, volume = {16}, year = {2021}, month = {2021 Aug 11}, pages = {e0256034}, abstract = {

Identifying coordinated activity within complex systems is essential to linking their structure and function. We study collective activity in networks of pulse-coupled oscillators that have variable network connectivity and integrate-and-fire dynamics. Starting from random initial conditions, we see the emergence of three broad classes of behaviors that differ in their collective spiking statistics. In the first class ("temporally-irregular"), all nodes have variable inter-spike intervals, and the resulting firing patterns are irregular. In the second ("temporally-regular"), the network generates a coherent, repeating pattern of activity in which all nodes fire with the same constant inter-spike interval. In the third ("chimeric"), subgroups of coherently-firing nodes coexist with temporally-irregular nodes. Chimera states have previously been observed in networks of oscillators; here, we find that the notions of temporally-regular and chimeric states encompass a much richer set of dynamical patterns than has yet been described. We also find that degree heterogeneity and connection density have a strong effect on the resulting state: in binomial random networks, high degree variance and intermediate connection density tend to produce temporally-irregular dynamics, while low degree variance and high connection density tend to produce temporally-regular dynamics. Chimera states arise with more frequency in networks with intermediate degree variance and either high or low connection densities. Finally, we demonstrate that a normalized compression distance, computed via the Lempel-Ziv complexity of nodal spike trains, can be used to distinguish these three classes of behavior even when the phase relationship between nodes is arbitrary.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0256034}, author = {Kadhim, Kyra L and Hermundstad, Ann M and Brown, Kevin S} } @article {65675, title = {Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures.}, journal = {eLife}, volume = {9}, year = {2020}, month = {2020 Aug 3}, abstract = {

Previously, in (Hermundstad et al., 2014), we showed that when sampling is limiting, the efficient coding principle leads to a {\textquoteright}variance is salience{\textquoteright} 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 {\textquoteright}variance is salience{\textquoteright} 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).

}, doi = {10.7554/eLife.54347}, url = {https://elifesciences.org/articles/54347}, author = {Tesileanu, Tiberiu and Conte, Mary M and Briguglio, John J and Hermundstad, Ann M and Victor, Jonathan D and Balasubramanian, Vijay} } @article {65234, title = {Generation of stable heading representations in diverse visual scenes.}, journal = {Nature}, volume = {576}, year = {2019}, month = {2019 Nov 20}, pages = {126-31}, abstract = {

Many animals rely on an internal heading representation when navigating in varied environments. How this representation is linked to the sensory cues that define different surroundings is unclear. In the fly brain, heading is represented by {\textquoteright}compass{\textquoteright} neurons that innervate a ring-shaped structure known as the ellipsoid body. Each compass neuron receives inputs from {\textquoteright}ring{\textquoteright} neurons that are selective for particular visual features; this combination provides an ideal substrate for the extraction of directional information from a visual scene. Here we combine two-photon calcium imaging and optogenetics in tethered flying flies with circuit modelling, and show how the correlated activity of compass and visual neurons drives plasticity, which flexibly transforms two-dimensional visual cues into a stable heading representation. We also describe how this plasticity enables the fly to convert a partial heading representation, established from orienting within part of a novel setting, into a complete heading representation. Our results provide mechanistic insight into the memory-related computations that are essential for flexible navigation in varied surroundings.

}, issn = {1476-4687}, doi = {10.1038/s41586-019-1767-1}, author = {Kim, Sung Soo and Hermundstad, Ann M and Romani, Sandro and Abbott, L F and Jayaraman, Vivek} } @article {49311, title = {Adaptive coding for dynamic sensory inference.}, journal = {eLife}, volume = {7}, year = {2018}, month = {2018 Jul 10}, abstract = {

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.

}, issn = {2050-084X}, doi = {10.7554/eLife.32055}, author = {M{\l}ynarski, Wiktor F and Hermundstad, Ann M} } @article {48549, title = {Neural signatures of dynamic stimulus selection in Drosophila.}, journal = {Nature Neuroscience}, volume = {20}, year = {2017}, month = {2017 Jun 12}, pages = {1104-13}, abstract = {

Many animals orient using visual cues, but how a single cue is selected from among many is poorly understood. Here we show that Drosophila ring neurons{\textemdash}central brain neurons implicated in navigation{\textemdash}display visual stimulus selection. Using\ in vivo\ two-color two-photon imaging with genetically encoded calcium indicators, we demonstrate that individual ring neurons inherit simple-cell-like receptive fields from their upstream partners. Stimuli in the contralateral visual field suppressed responses to ipsilateral stimuli in both populations. Suppression strength depended on when and where the contralateral stimulus was presented, an effect stronger in ring neurons than in their upstream inputs. This history-dependent effect on the temporal structure of visual responses, which was well modeled by a simple biphasic filter, may determine how visual references are selected for the fly{\textquoteright}s internal compass. Our approach highlights how two-color calcium imaging can help identify and localize the origins of sensory transformations across synaptically connected neural populations.

}, doi = {10.1038/nn.4581}, author = {Sun, Yi and Nern, Aljoscha and Franconville, Romain and Dana, Hod and Schreiter, Eric R and Looger, Loren L and Svoboda, Karel and Kim, Douglas S and Hermundstad, Ann M and Jayaraman, Vivek} } @article {47704, title = {Structurally-constrained relationships between cognitive states in the human brain.}, journal = {PLoS computational biology}, volume = {10}, year = {2014}, month = {2014 May}, pages = {e1003591}, abstract = {

The anatomical connectivity of the human brain supports diverse patterns of correlated neural activity that are thought to underlie cognitive function. In a manner sensitive to underlying structural brain architecture, we examine the extent to which such patterns of correlated activity systematically vary across cognitive states. Anatomical white matter connectivity is compared with functional correlations in neural activity measured via blood oxygen level dependent (BOLD) signals. Functional connectivity is separately measured at rest, during an attention task, and during a memory task. We assess these structural and functional measures within previously-identified resting-state functional networks, denoted task-positive and task-negative networks, that have been independently shown to be strongly anticorrelated at rest but also involve regions of the brain that routinely increase and decrease in activity during task-driven processes. We find that the density of anatomical connections within and between task-positive and task-negative networks is differentially related to strong, task-dependent correlations in neural activity. The space mapped out by the observed structure-function relationships is used to define a quantitative measure of separation between resting, attention, and memory states. We find that the degree of separation between states is related to both general measures of behavioral performance and relative differences in task-specific measures of attention versus memory performance. These findings suggest that the observed separation between cognitive states reflects underlying organizational principles of human brain structure and function.

}, keywords = {Attention, Brain, Cognition, Computer Simulation, Connectome, Humans, Memory, Models, Anatomic, Models, Neurological, Nerve Net, White Matter}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1003591}, author = {Hermundstad, Ann M and Brown, Kevin S and Bassett, Danielle S and Aminoff, Elissa M and Frithsen, Amy and Johnson, Arianne and Tipper, Christine M and Miller, Michael B and Grafton, Scott T and Carlson, Jean M} } @article {47703, title = {Variance predicts salience in central sensory processing.}, journal = {eLife}, volume = {3}, year = {2014}, month = {2014}, abstract = {

Information processing in the sensory periphery is shaped by natural stimulus statistics. In the periphery, a transmission bottleneck constrains performance; thus efficient coding implies that natural signal components with a predictably wider range should be compressed. In a different regime--when sampling limitations constrain performance--efficient coding implies that more resources should be allocated to informative features that are more variable. We propose that this regime is relevant for sensory cortex when it extracts complex features from limited numbers of sensory samples. To test this prediction, we use central visual processing as a model: we show that visual sensitivity for local multi-point spatial correlations, described by dozens of independently-measured parameters, can be quantitatively predicted from the structure of natural images. This suggests that efficient coding applies centrally, where it extends to higher-order sensory features and operates in a regime in which sensitivity increases with feature variability.

}, keywords = {Humans, Sensory Receptor Cells, Visual Cortex, Visual Perception}, issn = {2050-084X}, doi = {10.7554/eLife.03722}, author = {Hermundstad, Ann M and Briguglio, John J and Conte, Mary M and Victor, Jonathan D and Balasubramanian, Vijay and Tka{\v c}ik, Ga{\v s}per} } @article {47705, title = {Structural foundations of resting-state and task-based functional connectivity in the human brain.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {110}, year = {2013}, month = {2013 Apr 9}, pages = {6169-74}, abstract = {

Magnetic resonance imaging enables the noninvasive mapping of both anatomical white matter connectivity and dynamic patterns of neural activity in the human brain. We examine the relationship between the structural properties of white matter streamlines (structural connectivity) and the functional properties of correlations in neural activity (functional connectivity) within 84 healthy human subjects both at rest and during the performance of attention- and memory-demanding tasks. We show that structural properties, including the length, number, and spatial location of white matter streamlines, are indicative of and can be inferred from the strength of resting-state and task-based functional correlations between brain regions. These results, which are both representative of the entire set of subjects and consistently observed within individual subjects, uncover robust links between structural and functional connectivity in the human brain.

}, keywords = {Aging, Attention, Brain, Brain Mapping, Cognition, Computational Biology, Diffusion Magnetic Resonance Imaging, Humans, Magnetic Resonance Imaging, Memory, Models, Statistical, Neural Pathways, Software}, issn = {1091-6490}, doi = {10.1073/pnas.1219562110}, author = {Hermundstad, Ann M and Bassett, Danielle S and Brown, Kevin S and Aminoff, Elissa M and Clewett, David and Freeman, Scott and Frithsen, Amy and Johnson, Arianne and Tipper, Christine M and Miller, Michael B and Grafton, Scott T and Carlson, Jean M} } @article {47706, title = {Learning, memory, and the role of neural network architecture.}, journal = {PLoS computational biology}, volume = {7}, year = {2011}, month = {2011 Jun}, pages = {e1002063}, abstract = {

The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.

}, keywords = {Computational Biology, Humans, Learning, Memory, Models, Neurological, Nerve Net, Neural Networks (Computer)}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1002063}, author = {Hermundstad, Ann M and Brown, Kevin S and Bassett, Danielle S and Carlson, Jean M} }