Biology subjects

Orsborn, A. L.

Publications and source records attributed to Orsborn, A. L..

11 recordsLinked to original sources

bioRxiv2026

Changes to task relevance in redundant motor environments distinguish structure learning from adaptation

Motor actions often exceed the dimensionality of task feedback, which creates control redundancy that requires learners to identify which movement dimensions influence task outcomes. The computations that shape learning in redundant tasks, especially when task-relevant spaces change, remain unclear. We studied this by combining established visuomotor perturbation approaches with a redundant motor interface reaching task. Two monkeys learned a 3D hand position to 2D cursor position mapping that allowed us to define and manipulate the task-relevant space. After animals learned an initial task-relevant space, we introduced two types of perturbations: Relevance Preserving (RP), which rotated hand-cursor relationships within the learned task relevant space, and Relevance Altering (RA), which altered the task relevant space. We hypothesized that RP perturbations would engage error-based adaptation (observed in non-redundant tasks), whereas RA perturbations would engage additional learning mechanisms associated with acquiring new skills. RP perturbations were learned rapidly during training (online) and generalized to the initial learned mapping, consistent with error-based adaptation. In contrast, adapting to the first RA perturbation took longer, engaged learning outside of training (offline), and did not generalize to the initial learned mapping. Animals also adapted faster across repeated exposures to new RA mappings and changed how they generated movements to learn each new mapping, consistent with computations associated with structure learning. These findings suggest that changing task relevance shifts motor learning from adaptation toward structure learning, which has implications for models of motor learning and sensorimotor therapies like brain-computer interfaces.

neuroscience↗

bioRxiv2026

Combining phase analysis and causal stimulation enables longitudinal mesoscale connectivity mapping

Uncovering the circuit mechanisms of flexible behavior requires characterizing how neural signals propagate across large-scale networks and tracking how those networks evolve over time. Signal propagation through networks can be characterized by injecting activity and analyzing signals propagation, thereby measuring "causal connectivity". However, current techniques cannot measure causal connectivity at centimeter scales over weeklong periods of time, especially in non-human primates. Here, we present a stimulation and recording method to longitudinally measure causal connectivity across centimeter-scale cortical networks in macaques. We combined multi-site optogenetic stimulation with micro-electrocorticography using a semi-chronic implant approach that enables stable daily measurements over weeks-long periods spanning years. Stimulation evoked activity reflected a mixture of local, stimulation-driven responses and secondary, delayed activity presumably driven through connections. We applied phase-based spectral analysis to disentangle stimulation-driven and secondary activity related to causal connections. We validated this approach by tracking connectivity across weeks. We demonstrated improved stability over long durations while preserving sensitivity to eyes-open vs. closed behavioral states. This platform provides a robust means to investigate the large-scale circuit dynamics underlying complex behavior in the primate brain.

neuroscience↗

bioRxiv2025

The spatiotemporal structure of neural activity in motor cortex during reaching

Intracortical brain-computer interfaces (BCI) leverage knowledge about neural representations to translate movement-related neural activity into actions. BCI implants have targeted broad cortical regions known to have relevant motor representations, but emerging technologies will allow flexible targeting to specific neural populations. The structure of motor representations in neural populations across frontal motor cortices, which span centimeters, has not been well characterized. Here, we investigate how motor representations and population dynamics (temporal coordination) vary across a large expanse of frontal motor cortices. We used high-density, laminar, microelectrode arrays to record many neurons and then sampled neural populations across frontal motor cortex in two monkeys while they performed a reaching task. Our experiments allowed us to map neuronal activity across three spatial dimensions and relate them to movement. Target decoding analysis revealed that target direction information (one key aspect of task information) was heterogeneously distributed across the cortical surface and in depth. Similarly, we found that the temporal dynamics of different neural populations was highly variable, but that the amount of task information predicted which neural populations had similar dynamics. The neural populations with the most similar dynamics were composed of neurons with high task information regardless of spatial location. Our results highlight the spatiotemporal complexity of motor representations across frontal motor cortex at the level of neurons and neural populations, where well-learned movements consistently recruit a spatially distributed subset of neurons. Further insights into the spatiotemporal structure of neural activity patterns across frontal motor cortex will be critical to guide future implants for improved BCI performance. Significance StatementMotor brain-computer interfaces (BCI) translate neural activity into movement, but how to target implants within motor cortices to maximize performance remains unclear. We used high-density recordings of neural activity spanning a large cortical area and related them to movement to map the spatial distribution of task information and the evolution of neural population activity over time. Our measurements revealed that neurons with the most task information were highly distributed across cortex yet also evolved coherently in time, suggesting that spatially distributed neurons coordinate to control movements. Our results provide new links between neuron- and population-level maps of motor representations, and highlight the complex spatiotemporal structure of activity that may need to be considered when designing next-generation BCIs.

neuroscience↗

bioRxiv2024

Mapping eye, arm, and reward information in frontal motor cortices using electrocorticography in non-human primates

Goal-directed reaches give rise to dynamic neural activity across the brain as we move our eyes and arms, and process outcomes. High spatiotemporal resolution mapping of multiple cortical areas will improve our understanding of how these neural computations are spatially and temporally distributed across the brain. In this study, we used micro-electrocorticography ({micro}ECoG) recordings in two male monkeys performing visually guided reaches to map information related to eye movements, arm movements, and receiving rewards over a 1.37 cm2 area of frontal motor cortices (primary motor cortex, premotor cortex, frontal eye field, and dorsolateral pre-frontal cortex). Time-frequency and decoding analyses revealed that eye and arm movement information shifts across brain regions during a reach, likely reflecting shifts from planning to execution. We then used phase-based analyses to reveal potential overlaps of eye and arm information. We found that arm movement decoding performance was impacted by task-irrelevant eye movements, consistent with the presence of intermixed eye and arm information across much of motor cortices. Phase-based analyses also identified reward-related activity primarily around the principal sulcus in the pre-frontal cortex as well as near the arcuate sulcus in the premotor cortex. Our results demonstrate {micro}ECoGs strengths for functional mapping and provide further detail on the spatial distribution of eye, arm, and reward information processing distributed across frontal cortices during reaching. These insights advance our understanding of the overlapping neural computations underlying coordinated movements and reveal opportunities to leverage these signals to enhance future brain-computer interfaces. Significance statement Picking up your coffee mug requires coordinating movements of your eyes and hand and processing the outcomes of those movements. Mapping how neural activity relates to different functions helps us understand how the brain performs these computations. Many mapping techniques have limited spatial or temporal resolution, restricting our ability to dissect computations that overlap closely in space and time. We used micro-electrocorticography recordings to map neural activity across multiple cortical areas while monkeys made goal-directed reaches. These measurements revealed high spatial and temporal resolution maps of neural activity related to eye, arm, and reward information processing. These maps reveal overlapping neural computations underlying movement and open opportunities to use eye and reward information to improve therapies to restore motor function.

neuroscience↗

bioRxiv2024

Predicting and Shaping Human-Machine Interactions in Closed-loop, Co-adaptive Neural Interfaces

Neural interfaces can restore or augment human sensorimotor capabilities by converting high-bandwidth biological signals into control signals for an external device via a decoder algorithm. Leveraging user and decoder adaptation to create co-adaptive interfaces presents opportunities to improve usability and personalize devices. However, we lack principled methods to model and optimize the complex two-learner dynamics that arise in co-adaptive interfaces. Here, we present computational methods based on control theory and game theory to analyze and generate predictions for user-decoder co-adaptive outcomes in continuous interactions. We tested these computational methods using an experimental platform where human participants (N=14) learn to control a cursor using an adaptive myoelectric interface to track a target on a computer display. Our framework allowed us to characterize user and decoder changes within co-adaptive myoelectric interfaces. Our framework further allowed us to predict how decoder algorithm changes impacted co-adaptive interface performance and revealed how interface properties can shape user behavior. Our findings demonstrate an experimentally-validated computational framework that can be used to design user-decoder interactions in closed-loop, co-adaptive neural interfaces. This framework opens future opportunities to optimize co-adaptive neural interfaces to expand the performance and application domains for neural interfaces.

neuroscience↗

bioRxiv2024

Using Eye Gaze to Train an Adaptive Myoelectric Interface

Myoelectric interfaces hold promise in consumer and health applications, but they are currently limited by variable performance across users and poor generalizability across tasks. To address these limitations, we consider interfaces that continually adapt during operation. Although current adaptive interfaces can reduce inter-subject variability, they still generalize poorly between tasks because they make use of task-specific data during training. To address this limitation, we propose a new paradigm to adapt myoelectric interfaces using natural eye gaze as training data. We recruited 11 subjects to test our proposed method on a 2D computer cursor control task using high-density surface EMG signals measured from forearm muscles. We find comparable task performance between our gaze-trained paradigm and the current task-dependent method. This result demonstrates the feasibility of using eye gaze to replace task-specific training data in adaptive myoelectric interfaces, holding promise for generalization across diverse computer tasks. CCS Concepts* Human-centered computing [->] Interaction devices; Empirical studies in HCI.

bioengineering↗

bioRxiv2024

Assistive sensory-motor perturbations influence learned neural representations

Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using Brain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days. Over time, task-relevant information became concentrated in fewer neurons, unlike with fixed decoders. At the population level, task information also became largely confined to a few neural modes that accounted for an unexpectedly small fraction of the population variance. A neural network model suggests the adaptive decoders directly contribute to forming these more compact neural representations. Our findings show that assistive decoders manipulate error information used for long-term learning computations like credit assignment, which informs our understanding of motor learning and has implications for designing real-world BCIs.

neuroscience↗

bioRxiv2023

Neural manifolds and gradient-based adaptation in neural-interface tasks

During well-trained behaviors, neural population activity in motor cortex lies on a low-dimensional manifold. This raises the question of how such structure constrains subsequent learning. In brain-computer interface experiments in nonhuman primates, perturbations aligned with this subspace induced rapid adaptation, whereas misaligned perturbations induced slower adaptation. Several theoretical accounts have been proposed to explain this differential adaptation, differing in the locus of plasticity. We compare these hypotheses using a minimal linear recurrent network operating at its fixed point and trained by gradient descent. All candidate plasticity sites are able to produce some degree of differential adaptation, whose strength depends on the variance of recurrent weights, with different sensitivities across sites. Hessian analysis reveals how misaligned perturbations reshape the loss landscape by introducing directions of shallow curvature along which gradient descent proceeds slowly. We further propose an experimental test to help distinguish the contributions of different plasticity sites during adaptation. Overall, our results identify the variance of recurrent weights as a key control parameter governing differential adaptation, alongside the site of plasticity.

neuroscience↗

bioRxiv2022

Multi-block RNN Autoencoders Enable Broadband ECoG Signal Reconstruction

ObjectiveNeural dynamical models reconstruct neural data using dynamical systems. These models enable direct reconstruction and estimation of neural time-series data as well as estimation of neural latent states. Nonlinear neural dynamical models using recurrent neural networks in an encoder-decoder architecture have recently enabled accurate single-trial reconstructions of neural activity for neuronal spiking data. While these models have been applied to neural field potential data, they have only so far been applied to signal feature reconstruction (e.g. frequency band power), and have not yet produced direct reconstructions of broadband time-series data preserving signal phase and temporal resolution. ApproachHere we present two encoder-decoder model architectures - the RNN autoencoder (RAE) and multi-block RAE (MRAE) for direct time-series reconstruction of broadband neural data. We trained and tested models on multi-channel micro-Electricorticography (ECoG) recordings from non-human primate motor corticies during unconstrained behavior. Main ResultsWe show that RAE reconstructs micro-electrocorticography recordings, but has reconstruction accuracy that is band-limited to model scale. The MRAE architecture overcomes these time-bandwidth restrictions, yielding broadband (0-100 Hz), accurate reconstructions of ECoG data. SignificanceRAE and MRAE reconstruct broadband ECoG data through multiblock dynamical modeling. The MRAE overcomes time-bandwitdh restrictions to provide improved accuracy for long time duration signals. The reconstruction capabilities provided by these models for broadband neural signals like ECoG may enable the development of improved tools and analysis for basic scientific research and applications like brain-computer interfaces.

neuroscience↗

bioRxiv2021

The brain uses invariant dynamics to generalize outputs across movements

It has been proposed that the nervous system has the capacity to generate a wide variety of movements because it re-uses some invariant code. Previous work has identified that dynamics of neural population activity are similar during different movements, where dynamics refer to how the instantaneous spatial pattern of population activity changes in time. Here we test whether invariant dynamics of neural populations are actually used to issue the commands that direct movement. Using a brain-machine interface that transformed rhesus macaques motor cortex activity into commands for a neuroprosthetic cursor, we discovered that the same command is issued with different neural activity patterns in different movements. However, these different patterns were predictable, as we found that the transitions between activity patterns are governed by the same dynamics across movements. These invariant dynamics are low-dimensional, and critically, they align with the brain-machine interface, so that they predict the specific component of neural activity that actually issues the next command. We introduce a model of optimal feedback control that shows that invariant dynamics can help transform movement feedback into commands, reducing the input that the neural population needs to control movement. Altogether our results demonstrate that invariant dynamics drive commands to control a variety of movements, and show how feedback can be integrated with invariant dynamics to issue generalizable commands.

neuroscience↗

bioRxiv2020

A Game-Theoretic Model for Co-Adaptive Brain-Machine Interfaces

Co-adaptation in brain-machine interfaces (BMIs) can improve performance and facilitate user learning. We propose and analyze a mathematical model for co-adaptation in BMIs. We model the brain and the decoder as strategic agents who seek to minimize their individual cost functions, leading to a game-theoretic formulation of interaction. We frame our BMI model as a potential game to identify stationary points (Nash equilibria) of the brain-decoder interactions, which correspond to points at which both the brain and the decoder stop adapting. Assuming the brain and the decoder adapt using gradientbased schemes, we analytically show how convergence to these equilibria depends on agent learning rates. This theoretical framework presents a basis for simulating co-adaption using dynamic game theory and can be extended to tasks with multiple dimensions and to different decoder models. This framework can ultimately be used to inform adaptive decoder design to shape brain learning and optimize BMI performance.

bioengineering↗