Biology subjects

Angelaki, D.

Publications and source records attributed to Angelaki, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Intelligent systems distribute memory across internal and embodied resources

Intelligent systems do not operate in isolation from the body, yet how bodily actions support internal computation remains unclear. Here we show that primates and artificial agents recruit eye movements to maintain beliefs about hidden goals when internal computation is constrained. In a naturalistic navigation task, both systems spontaneously tracked goals with their eyes after they disappeared from view. This strategy emerged in artificial agents only when internal memory was constrained, identifying resource constraints as a driver of memory externalization. Restricting gaze impaired performance in both systems, while natural behavioral variability and causal perturbations in agents established a direct influence of oculomotor state on internal beliefs about hidden goals. Across agents, varying the reliability of internal memory and gaze redistributed goal information between internal and embodied states. The same resource-allocation principle predicted the distribution of goal information in macaque parietal cortex, where greater reliance on gaze was accompanied by weaker gaze-independent neural representations. Together, these findings identify eye movements as embodied memory and reveal a convergent principle across intelligent systems: memory is allocated across internal and embodied resources according to their reliability.

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bioRxiv2026

Rationalizing sensorimotor deficits in autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by socio-communicative, sensory, and motor anomalies. Recently, a host of probabilistic models have been proposed to account for the varied phenotypes observed in ASD. These computational psychiatry efforts, however, have (1) largely focused on simple tasks that artificially segregate action from perception, and (2) unequivocally assumed that both neurotypical and divergent populations attempt to optimize the same cost function. Here, we ask individuals with ASD and neurotypical (NT) controls to perform a task in virtual reality (VR): they should navigate to, and stop at, the position of briefly presented targets in a closed action-perception loop. First, we demonstrate that while summary task performance is insufficient to distinguish between ASD and NT individuals, data-rich virtual trajectories do differentiate these groups. This suggests that ASD and control participants use distinct navigational strategies and/or cost functions. Next, we leverage the theoretical framework of Inverse Rational Control (IRC) to interpret the sensorimotor anomalies present in ASD. IRC does not assume that agents are optimal, but rational: that is, agents attempt to maximize reward according to an internal model of the task, which may not match the true task and environment. Applying IRC to this VR navigation task revealed both sensory and motor differences between ASD and neurotypical participants. The sensory differences are consistent with Bayesian inference under altered weighting of sensory evidence. The motor differences manifest as more frequent and abrupt actions in ASD, which we explain by a reduced subjective action cost in ASD. Together, these results demonstrate the utility of model-based characterization of psychopathology and suggest that the computational study of ASD should consider not only perceptual anomalies, but also subjective costs.

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bioRxiv2026

Active locomotion predictively rescues head direction attractor dynamics in head-fixed mice

Head direction (HD) cells in the anterodorsal thalamic nuclei form the brains internal compass, and are often modeled as a ring attractor maintaining azimuth coding by leveraging continuous visual and inertial sensory input. Here, we test how the common experimental preparation of head-fixed animals alters this code. Complete head-fixation that creates vestibular conflict disrupts both unit and population encoding of head direction, while selectively constraining head-on-body movements either in real or virtual reality uniquely impairs HD population activity. More specifically, attractor dynamics is altered in head-restrained mice during periods of immobility, but remarkably recover several hundred milliseconds prior to locomotion onset. The rescue preceding movement onset suggests that an efference copy or prediction of a re-afferent signal is necessary to maintain HD network activity during head restraint. A computational model recapitulates these effects by perturbing lateral connectivity among HD neurons. More generally, the results indicate that the HD network is a context- and state-dependent predictive estimator, stabilized by forthcoming self-motion signals. The classic ring-attractor models should be revised to integrate context-dependent dynamics with prospective motor signals, offering a more complete account of how the brains compass remains stable across both naturalistic and constrained conditions.

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bioRxiv2024

Dorsolateral prefrontal cortex drives strategic aborting by optimizing long-run policy extraction

Real world choices often involve balancing decisions that are optimized for the short-vs. long-term. Here, we reason that apparently sub-optimal single trial decisions in macaques may in fact reflect long-term, strategic planning. We demonstrate that macaques freely navigating in VR for sequentially presented targets will strategically abort offers, forgoing more immediate rewards on individual trials to maximize session-long returns. This behavior is highly specific to the individual, demonstrating that macaques reason about their own long-run performance. Reinforcement-learning (RL) models suggest this behavior is algorithmically supported by modular actor-critic networks with a policy module not only optimizing long-term value functions, but also informed of specific state-action values allowing for rapid policy optimization. The behavior of artificial networks suggests that changes in policy for a matched offer ought to be evident as soon as offers are made, even if the aborting behavior occurs much later. We confirm this prediction by demonstrating that single units and population dynamics in macaque dorsolateral prefrontal cortex (dlPFC), but not parietal area 7a or dorsomedial superior temporal area (MSTd), reflect the upcoming reward-maximizing aborting behavior upon offer presentation. These results cast dlPFC as a specialized policy module, and stand in contrast to recent work demonstrating the distributed and recurrent nature of belief-networks.

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bioRxiv2024

A common computational and neural anomaly across mouse models of autism

Computational psychiatry has suggested that humans within the autism spectrum disorder (ASD) inflexibly update their expectations (i.e., Bayesian priors). Here, we leveraged high-yield rodent psychophysics (n = 75 mice), extensive behavioral modeling (including principled and heuristics), and (near) brain-wide single cell extracellular recordings (over 53k units in 150 brain areas) to ask (1) whether mice with different genetic perturbations associated with ASD show this same computational anomaly, and if so, (2) what neurophysiological features are shared across genotypes in subserving this deficit. We demonstrate that mice harboring mutations in Fmr1, Cntnap2, and Shank3B show a blunted update of priors during decision-making. Neurally, the differentiating factor between animals flexibly and inflexibly updating their priors was a shift in the weighting of prior encoding from sensory to frontal cortices. Further, in mouse models of ASD frontal areas showed a preponderance of units coding for deviations from the animals long-run prior, and sensory responses did not differentiate between expected and unexpected observations. These findings demonstrate that distinct genetic instantiations of ASD may yield common neurophysiological and behavioral phenotypes.

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bioRxiv2023

Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex

Natural behaviors occur in closed action-perception loops and are supported by dynamic and flexible beliefs abstracted away from our immediate sensory milieu. How this real-world flexibility is instantiated in neural circuits remains unknown. Here we have macaques navigate in a virtual environment by primarily leveraging sensory (optic flow) signals, or by more heavily relying on acquired internal models. We record single-unit spiking activity simultaneously from the dorsomedial superior temporal area (MSTd), parietal area 7a, and the dorso-lateral prefrontal cortex (dlPFC). Results show that while animals were able to maintain adaptive task-relevant beliefs regardless of sensory context, the fine-grain statistical dependencies between neurons, particularly in 7a and dlPFC, dynamically remapped with the changing computational demands. In dlPFC, but not 7a, destroying these statistical dependencies abolished the areas ability for cross-context decoding. Lastly, correlation analyses suggested that the more unit-to-unit couplings remapped in dlPFC, and the less they did so in MSTd, the less were population codes and behavior impacted by the loss of sensory evidence. We conclude that dynamic functional connectivity between prefrontal cortex neurons maintains a stable population code and context-invariant beliefs during naturalistic behavior with closed action-perception loops.

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bioRxiv2023

Causal inference during closed-loop navigation: parsing of self- and object-motion

A key computation in building adaptive internal models of the external world is to ascribe sensory signals to their likely cause(s), a process of Bayesian Causal Inference (CI). CI is well studied within the framework of two-alternative forced-choice tasks, but less well understood within the cadre of naturalistic action-perception loops. Here, we examine the process of disambiguating retinal motion caused by self- and/or object-motion during closed-loop navigation. First, we derive a normative account specifying how observers ought to intercept hidden and moving targets given their belief over (i) whether retinal motion was caused by the target moving, and (ii) if so, with what velocity. Next, in line with the modeling results, we show that humans report targets as stationary and steer toward their initial rather than final position more often when they are themselves moving, suggesting a misattribution of object-motion to the self. Further, we predict that observers should misattribute retinal motion more often: (i) during passive rather than active self-motion (given the lack of an efference copy informing self-motion estimates in the former), and (ii) when targets are presented eccentrically rather than centrally (given that lateral self-motion flow vectors are larger at eccentric locations during forward self-motion). Results confirm both of these predictions. Lastly, analysis of eye-movements show that, while initial saccades toward targets are largely accurate regardless of the self-motion condition, subsequent gaze pursuit was modulated by target velocity during object-only motion, but not during concurrent object- and self-motion. These results demonstrate CI within action-perception loops, and suggest a protracted temporal unfolding of the computations characterizing CI.

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bioRxiv2021

A neural mechanism for detecting object motion during self-motion

Detecting objects that move in a scene is a fundamental computation performed by the visual system. This computation is greatly complicated by observer motion, which causes most objects to move across the retinal image. How the visual system detects scene-relative object motion during self-motion is poorly understood. Human behavioral studies suggest that the visual system may identify local conflicts between motion parallax and binocular disparity cues to depth, and may use these signals to detect moving objects. We describe a novel mechanism for performing this computation based on neurons in macaque area MT with incongruent depth tuning for binocular disparity and motion parallax cues. Neurons with incongruent tuning respond selectively to scene-relative object motion and their responses are predictive of perceptual decisions when animals are trained to detect a moving object during self-motion. This finding establishes a novel functional role for neurons with incongruent tuning for multiple depth cues.

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bioRxiv2021

Flexible neural coding in sensory, parietal, and frontal cortices during goal-directed virtual navigation

We do not understand how neural nodes operate and coordinate within the recurrent action-perception loops that characterize naturalistic self-environment interactions. Here, we record single-unit spiking activity and local field potentials (LFPs) simultaneously from the dorsomedial superior temporal area (MSTd), parietal area 7a, and dorsolateral prefrontal cortex (dlPFC) as monkeys navigate in virtual reality to "catch fireflies." This task requires animals to actively sample from a closed-loop virtual environment while concurrently computing continuous latent variables: (i) the distance and angle travelled (i.e., path integration) and (ii) the distance and angle to a memorized firefly location (i.e., a hidden spatial goal). We observed a patterned mixed selectivity, with the prefrontal cortex most prominently coding for latent variables, parietal cortex coding for sensorimotor variables, and MSTd most often coding for eye-movements. However, even the traditionally considered sensory area (i.e., MSTd) tracked latent variables, demonstrating path integration and vector-coding of hidden spatial goals. Further, global encoding profiles and unit-to-unit coupling (i.e., noise correlations) suggested a functional subnetwork composed by MSTd and dlPFC, and not between these and 7a, as anatomy would suggest. We show that the greater the unit-to-unit coupling between MSTd and dlPFC, the more the animals gaze position was indicative of the ongoing location of the hidden spatial goal. We suggest this MSTd-dlPFC subnetwork reflects the monkeys natural and adaptive task strategy wherein they continuously gaze toward the location of the (invisible) target. Together, these results highlight the distributed nature of neural coding during closed action-perception loops and suggest that fine-grain functional subnetworks may be dynamically established to subserve (embodied) task strategies.

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bioRxiv2021

Two functionally distinct Purkinje cell populations implement an internal model within a single olivo-cerebellar loop

Olivo-cerebellar loops, where anatomical patches of the cerebellar cortex and inferior olive project one onto the other, form an anatomical unit of cerebellar computation. Here, we investigated how successive computational steps map onto olivo-cerebellar loops. Lobules IX-X of the cerebellar vermis, i.e. the nodulus and uvula, implement an internal model of the inner ears graviceptor, the otolith organs. We have previously identified two populations of Purkinje cells that participate in this computation: Tilt-selective cells transform egocentric rotation signals into allocentric tilt velocity signals, to track head motion relative to gravity, and translation-selective cells encode otolith prediction error. Here we show that, despite very distinct simple spike response properties, both types of Purkinje cells emit complex spikes that are proportional to sensory prediction error. This indicates that both cell populations comprise a single olivo-cerebellar loop, in which only translation-selective cells project to the inferior olive. We propose a neural network model where sensory prediction errors computed by translation-selective cells are used as a teaching signal for both populations, and demonstrate that this network can learn to implement an internal model of the otoliths.

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bioRxiv2021

Sensory evidence accumulation using optic flow in a naturalistic navigation task

Sensory evidence accumulation is considered a hallmark of decision-making in noisy environments. Integration of sensory inputs has been traditionally studied using passive stimuli, segregating perception from action. Lessons learned from this approach, however, may not generalize to ethological behaviors like navigation, where there is an active interplay between perception and action. We designed a sensory-based sequential decision task in virtual reality in which humans and monkeys navigated to a memorized location by integrating optic flow generated by their own joystick movements. A major challenge in such closed-loop tasks is that subjects actions will determine future sensory input, causing ambiguity about whether they rely on sensory input rather than expectations based solely on a learned model of the dynamics. To test whether subjects performed sensory integration, we used three independent experimental manipulations: unpredictable optic flow perturbations, which pushed subjects off their trajectory; gain manipulation of the joystick controller, which changed the consequences of actions; and manipulation of the optic flow density, which changed the reliability of sensory evidence. Our results suggest that both macaques and humans relied heavily on optic flow, thereby demonstrating a critical role for sensory evidence accumulation during naturalistic action-perception closed-loop tasks.

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bioRxiv2020

Spatial representations in macaque hippocampal formation

The hippocampal formation is linked to spatial navigation, but there is little corroboration from freely-moving primates with concurrent monitoring of three-dimensional head and gaze stances. We recorded neurons and local field potentials across hippocampal regions in rhesus macaques during free foraging in an open environment while tracking their head and eye. Theta band activity was intermittently present at movement onset and modulated by saccades. Many cells were phase-locked to theta, with few showing theta phase precession. Most hippocampal neurons encoded a mixture of spatial variables beyond place fields and a negligible number showed prominent grid tuning. Spatial representations were dominated by facing location and allocentric direction, mostly in head, rather than gaze, coordinates. Importantly, eye movements strongly modulated neural activity in all regions. These findings reveal that the macaque hippocampal formation represents three-dimensional space using a multiplexed code, with head orientation and eye movement properties dominating over simple place and grid coding during free exploration.

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bioRxiv2020

Influence of sensory modality and control dynamics on human path integration

Path integration is a sensorimotor computation that can be used to infer latent dynamical states by integrating self-motion cues. We studied the influence of sensory observation (visual/vestibular) and latent control dynamics (velocity/acceleration) on human path integration using a novel motion-cueing algorithm. Sensory modality and control dynamics were both varied randomly across trials, as participants controlled a joystick to steer to a memorized target location in virtual reality. Visual and vestibular steering cues allowed comparable accuracies only when participants controlled their acceleration, suggesting that vestibular signals, on their own, fail to support accurate path integration in the absence of sustained acceleration. Nevertheless, performance in all conditions reflected a failure to fully adapt to changes in the underlying control dynamics, a result that was well explained by a bias in the dynamics estimation. This work demonstrates how an incorrect internal model of control dynamics affects navigation in volatile environments in spite of continuous sensory feedback.

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bioRxiv2020

Aberrant Sensory Encoding in Patients with Autism

Perceptual anomalies in patients with Autism Spectrum Disorder (ASD) have been attributed to irregularities in the Bayesian interpretation (i.e., decoding) of sensory information. Here we show that how sensory information is encoded and adapts to changing stimulus statistics also characteristically differs between healthy and ASD groups. In a visual estimation task, we extracted the accuracy of sensory encoding directly from psychophysical data, bypassing the decoding stage by using information theoretic measures. Initially, sensory representations in both groups reflected the statistics of visual orientations in natural scenes, but encoding capacity was overall lower in the ASD group. Exposure to an artificial statistical distribution of visual orientations altered the sensory representations of the control group toward the novel experimental statistics, while also increasing their total encoding resources. Neither total encoding resources nor their allocation changed significantly in the ASD group. Most interestingly, across both groups the adaptive re-allocation of encoding resources was correlated with subjects initial encoding capacity. These findings suggest that neural encoding resources are limited in ASD, and this limitation may explain their reduced perceptual flexibility.

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bioRxiv2020

A standardized and reproducible method to measure decision-making in mice

Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We designed a task for head-fixed mice that combines established assays of perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be successfully reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path towards achieving reproducibility in neuroscience through collaborative open-science approaches.

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bioRxiv2019

Increased variability but intact integration during visual navigation in Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a common neurodevelopmental disturbance afflicting a variety of functions from perception to cognition. The recent computational focus suggesting aberrant Bayesian inference in ASD has yielded promising but conflicting results in attempting to explain a wide variety of phenotypes by canonical computations. Here we used a naturalistic visual path integration task that combines continuous action with active sensing and allows tracking of subjects dynamic belief states. Both groups showed a previously documented bias pattern, by overshooting the radial distance and angular eccentricity of targets. For both control and ASD groups, these errors were driven by misestimated velocity signals due to a non-uniform speed prior, rather than imperfect integration. We tracked participants beliefs and found no difference in the speed prior, but heightened variability in the ASD group. Both end-point variance and trajectory irregularities correlated with ASD symptom severity. With feedback, variance was reduced and ASD performance approached that of controls. These findings highlight the need for both more naturalistic tasks and a broader computational perspective to understand the ASD phenotype and pathology.

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bioRxiv2019

A cross-species neural integration of gravity for motor optimisation

Recent kinematic results, combined with model simulations, have provided support for the hypothesis that the human brain uses an internal model of gravity to shape motor patterns that minimise muscle effort. Because many different muscular activation patterns can give rise to the same trajectory, here we analyse muscular activation patterns during single-degree-of-freedom arm movements in various directions, which allow to specifically investigating gravity-related movement properties. Using a well-known decomposition method of tonic and phasic electromyographic activities, we demonstrate that phasic EMGs present systematic negative phases. This negativity demonstrates that gravity effects are harvested to save muscle effort and reveals that the brain implements an optimal motor plan using gravity to accelerate downward and decelerate upward movements. Furthermore, for the first time, we compare experimental findings in humans to monkeys, thereby generalising the Effort-optimization strategy across species.

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bioRxiv2019

Simple spike dynamics of Purkinje cells in the macaque vestibulo-cerebellum reflect sensory prediction error

Theories of cerebellar functions posit that the cerebellum implements forward models for online correction of motor actions and sensory estimation. As an example of such computations, a forward model compensates for a sensory ambiguity where the peripheral otolith organs in the inner ear sense both head tilts and translations. Here we exploit the response dynamics of two functionally-coupled Purkinje cell types in the caudal vermis to understand their role in this computation. We find that one population encodes tilt velocity, whereas the other, translation-selective, population encodes linear acceleration. Using a dynamical model, we further show that these signals likely represent sensory prediction error for the on-line updating of tilt and translation estimates. These properties also reveal the need for temporal integration between the tilt-selective velocity and translation-selective acceleration population signals. We show that a simple model incorporating a biologically plausible short time constant can mediate the required temporal integration.

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