Biology subjects

Codol, O.

Publications and source records attributed to Codol, O..

14 recordsLinked to original sources

bioRxiv2026

Evolutionarily conserved neural dynamics across mice, monkeys, and humans

On evolutionary timescales, brain circuits adapt to support survival in each species ecological niche. While some anatomical aspects of neural circuitry are conserved across species with distant evolutionary origins, each species also exhibits specific circuit adaptations that enable its behavioral repertoire. It remains unclear whether homologous brain regions leverage analogous neural computations as different species perform common behaviors such as reaching and manipulating objects. Here, we directly assessed conservation of neural computations using intracortical recordings from mouse, monkey, and human motor cortex--a homologous region across many mammals--during motor behaviors crucial for survival. We hypothesized that, despite their phylogenetic distance, rodents and primates produce movements through conserved neural computations implemented by motor cortical population dynamics. Remarkably, we found that movement-related neural dynamics were highly conserved across species, while variations in behavioral output were uniquely captured in neural trajectory geometries. Strikingly, neural dynamics during movement across species were more conserved than those across brain regions in the same human and between motor preparation and execution in the same monkeys. Lastly, through manipulation of neural network models trained to perform reaching movements, we reinforce that conservation of neural dynamics across species likely stems from shared circuit constraints. We thus assert that evolution maintains neural computations across phylogeny even as behavioral repertoires expand.

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bioRxiv2025

A Context-Free Model of Savings in Motor Learning

Learning to adapt voluntary movements to an external perturbation, whether mechanical or visual, is faster during a second encounter than during the first. The mechanisms underlying this phenomenon, known as savings, remain unclear. Recent studies propose that the high dimensionality of neural control enables the retention of learning traces that may facilitate savings. To test this idea we used MotorNet, a framework for training recurrent neural networks (RNNs) to control biomechanical models of the human upper limb. RNNs were trained to perform reaching movements with a velocity-dependent force field (FF) and without (NF) in the sequence NF1 (baseline), FF1 (adaptation), NF2 (washout), and FF2 (re-adaptation). RNNs showed behavioural signatures of savings in the absence of any explicit contextual input signalling the presence or absence of the FF. Savings was more robust in RNNs with larger numbers of units. We identified a component of RNN activity associated with savings--a shift in preparatory activity that persisted even after washout. Displacing this preparatory activity in the direction of the shift enhanced savings, whereas perturbations in the opposite direction reduced or eliminated savings. These findings suggest a potential neural basis for motor memory retention underlying savings that is reliant on the high dimensionality of neural circuits for control, and is independent of cognitive or strategic learning.

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bioRxiv2025

Computation-through-Dynamics Benchmark: Simulated datasets and quality metrics for dynamical models of neural activity

A primary goal of systems neuroscience is to discover how ensembles of neurons transform inputs into goal-directed behavior, a process known as neural computation. A powerful framework for understanding neural computation uses neural dynamics - the rules that govern how neural activity evolves over time - to explain how goal-directed input-output transformations occur. As dynamical rules are not directly observable, we need computational models that can infer neural dynamics from recorded neural activity. We typically validate such models using synthetic datasets with known ground-truth dynamics, but unfortunately existing synthetic datasets dont reflect fundamental features of neural computation and may therefore be poor proxies for neural systems. Further, the field lacks validated metrics for quantifying the accuracy of the dynamics inferred by models. The Computation-through-Dynamics Toolkit (CtDToolkit) addresses these critical gaps by providing: 1) synthetic datasets that reflect computational properties of biological neural circuits, 2) interpretable metrics for quantifying model performance, and 3) a standardized pipeline for training and evaluating models with or without known external inputs. In this manuscript, we demonstrate how CtDToolkit can help guide the development, tuning, and troubleshooting of neural dynamics models. In summary, CtDToolkit provides a necessary framework for model developers to better understand and characterize neural computation through the lens of dynamics. Author SummaryUnderstanding how the brain works requires interpretable accounts of how populations of neurons process information to produce behavior. One powerful approach is to study "neural dynamics", the patterns of how neural activity evolves over time. Scientists develop computational models to infer these dynamics from neural recordings, but it has been challenging to know when the inferred dynamics are trustworthy. Existing datasets often lack key features of biological neural circuits, and current performance metrics can provide an incomplete picture of model quality. We developed the Computation-through-Dynamics Toolkit (CtDToolkit) to solve these problems. Our toolkit provides three key resources: biologically motivated synthetic datasets, improved metrics that provide more holistic accounts of model performance, and a standardized workflow for training and evaluating models. We hope that CtDToolkit enables researchers to rigorously test, improve, and troubleshoot their models before applying them to real brain data. This work establishes a crucial foundation for developing better methods to understand neural computation, ultimately advancing our ability to decode how the brain transforms sensory information into thought and action.

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bioRxiv2024

Sensory expectations shape neural population dynamics in motor circuits

The neural basis of movement preparation has been extensively studied during self-initiated actions where motor cortical activity during preparation shows a lawful relationship to the parameters of the subsequent action1,2. However, movements are regularly triggered and constantly corrected based on sensory inputs caused by disturbances to the body or environment. Since such disturbances are often predictable and since preparing for disturbances would make movements better, we hypothesized that expectations about sensory inputs also influence preparatory activity in motor circuits. Here we show that when humans and monkeys are probabilistically cued about the direction of a future mechanical perturbation, they incorporate sensory expectations into their movement preparation and improve their corrective responses. Using high-density neural recordings, we establish that sensory expectations are widespread across the brain, including the motor cortical areas involved in preparing self-initiated actions. The geometry of these preparatory signals in the neural population state is simple, scaling directly with the probability of each perturbation direction. After perturbation onset, a condition-independent perturbation signal shifts the neural state leading to rapid responses that initially reflect sensory expectations. Based on neural networks coupled to a biomechanical model of the arm3, we show that this neural geometry emerges through training, but only when the incoming sensory information indicating perturbation direction coincides with - or is preceded by - a condition-independent signal indicating that a perturbation has occurred. Thus, motor circuit dynamics are shaped by future sensory inputs, providing clear empirical support for the idea that movement is governed by the sophisticated manipulation of sensory feedback4.

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bioRxiv2024

Brain-like learning with exponentiated gradients

Computational neuroscience relies on gradient descent (GD) for training artificial neural network (ANN) models of the brain. The advantage of GD is that it is effective at learning difficult tasks. However, it produces ANNs that are a poor phenomenological fit to biology, making them less relevant as models of the brain. Specifically, it violates Dales law, by allowing synapses to change from excitatory to inhibitory, and leads to synaptic weights that are not log-normally distributed, contradicting experimental data. Here, starting from first principles of optimisation theory, we present an alternative learning algorithm, exponentiated gradient (EG), that respects Dales Law and produces log-normal weights, without losing the power of learning with gradients. We also show that in biologically relevant settings EG outperforms GD, including learning from sparsely relevant signals and dealing with synaptic pruning. Altogether, our results show that EG is a superior learning algorithm for modelling the brain with ANNs.

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bioRxiv2024

Brain-like neural dynamics for behavioral control develop through reinforcement learning

During development, neural circuits are shaped continuously as we learn to control our bodies. The ultimate goal of this process is to produce neural dynamics that enable the rich repertoire of behaviors we perform. What begins as a series of "babbles" coalesces into skilled motor output as the brain rapidly learns to control the body. However, the nature of the teaching signal underlying this normative learning process remains elusive. Here, we test two well-established and biologically plausible theories--supervised learning (SL) and reinforcement learning (RL)--that could explain how neural circuits develop the capacity for skilled movements. We trained recurrent neural networks to control a biomechanical model of a primate arm using either SL or RL and compared the resulting neural dynamics to populations of neurons recorded from the motor cortex of monkeys performing the same movements. Intriguingly, only RL-trained networks produced neural activity that matched their biological counterparts in terms of both the geometry and dynamics of population activity. We show that this similarity with biological brains depends critically on matching biomechanical properties of the limb. Dynamical analysis on network activity revealed that our RL-trained networks operate at the "edge of chaos", a dynamical regime known for its computational richness, greater memory capacity, and robust plasticity properties. We then demonstrated that monkeys and RL-trained networks, but not SL-trained networks, show a strikingly similar capacity for robust short-term behavioral adaptation to a movement perturbation, indicating a fundamental and general commonality in the neural control policy. Together, our results support the hypothesis that neural dynamics for behavioral control emerge through a process akin to reinforcement learning. The resulting neural circuits offer numerous advantages for adaptable behavioral control over simpler and more efficient learning rules and expand our understanding of how developmental processes shape neural dynamics.

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bioRxiv2023

MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks

Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely on two different platforms and (2) biomechanical effectors are not generally differentiable, constraining researchers to reinforcement learning algorithms despite the existence and potential biological relevance of faster training methods. To address these limitations, we developed MotorNet, an open-source Python toolbox for creating arbitrarily complex, differentiable, and biomechanically realistic effectors that can be trained on user-defined motor tasks using ANNs. MotorNet is designed to meet several goals: ease of installation, ease of use, a high-level user-friendly API, and a modular architecture to allow for flexibility in model building. MotorNet requires no dependencies outside Python, making it easy to get started with. For instance, it allows training ANNs on typically used motor control models such as a two joint, six muscle, planar arm within minutes on a typical desktop computer. MotorNet is built on PyTorch and therefore can implement any network architecture that is possible using the PyTorch framework. Consequently, it will immediately benefit from advances in artificial intelligence through PyTorch updates. Finally, it is open source, enabling users to create and share their own improvements, such as new effector and network architectures or custom task designs. MotorNets focus on higher order model and task design will alleviate overhead cost to initiate computational projects for new researchers by providing a standalone, ready-to-go framework, and speed up efforts of established computational teams by enabling a focus on concepts and ideas over implementation.

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bioRxiv2021

Sensorimotor feedback loops are selectively sensitive to reward

Although it is well established that motivational factors such as earning more money for performing well improve motor performance, how the motor system implements this improvement remains unclear. For instance, feedback-based control, which uses sensory feedback from the body to correct for errors in movement, improves with greater reward. But feedback control encompasses many feedback loops with diverse characteristics such as the brain regions involved and their response time. Which specific loops drive these performance improvements with reward is unknown, even though their diversity makes it unlikely that they are contributing uniformly. We systematically tested the effect of reward on the latency (how long for a corrective response to arise?) and gain (how large is the corrective response?) of seven distinct sensorimotor feedback loops in humans. Only the fastest feedback loops were insensitive to reward, and the earliest reward-driven changes were consistently an increase in feedback gains, not a reduction in latency. Rather, a reduction of response latencies only tended to occur in slower feedback loops. These observations were similar across sensory modalities (vision and proprioception). Our results may have implications regarding feedback control performance in athletic coaching. For instance, coaching methodologies that rely on reinforcement or "reward shaping" may need to specifically target aspects of movement that rely on reward-sensitive feedback responses.

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bioRxiv2020

Differential dopamine receptor-dependent sensitivity improves action selection in the basal ganglia

The problem of selecting one action from a set of different possible actions, simply referred to as the problem of action selection, is a ubiquitous challenge in the animal world. For vertebrates, the basal ganglia (BG) are widely thought to implement the core computation to solve this problem, as the anatomy and physiology of the BG are well-suited to this end. However, the BG still displays physiological features whose role in achieving efficient action selection remains unclear. In particular, it is known that the two types of dopaminergic receptors (D1 and D2) present in the BG give rise to mechanistically different responses. The overall effect will be a difference in sensitivity to dopamine which may have ramifications for action selection. However, which receptor type leads to a stronger response is, a priori, unclear, due to the complexity of the intracellular mechanisms involved. In this study, we use the action selection hypothesis to predict which of D1 or D2 has the greater sensitivity. Thus, we ask - what sensitivity ratio would result in enhanced action selection functionality in the basal ganglia? To do this, we incorporated differential D1 and D2 sensitivity in an existing, high level computational model of the macro-architecture of the basal ganglia, via a simple weighting variable. We then quantitatively assessed the models capacity to perform action selection as we parametrically manipulated the new feature. We show that differential (rather than equal) D1 and D2 sensitivity to dopaminergic input improves action selection, and specifically, that greater D1 sensitivity (compared to that for D2) leads to these improvements.

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bioRxiv2020

Reward-driven enhancements in motor control are robust to TMS manipulation

A wealth of evidence describes the strong positive impact that reward has on motor control at the behavioural level. However, surprisingly little is known regarding the neural mechanisms which underpin these effects, beyond a reliance on the dopaminergic system. In recent work, we developed a task that enabled the dissociation of the selection and execution components of an upper limb reaching movement. Our results demonstrated that both selection and execution are concommitently enhanced by immediate reward availability. Here, we investigate what the neural underpinnings of each component may be. To this end, we disrupted activity of the ventromedial prefrontal cortex and supplementary motor area using continuous theta-burst transcranial magnetic stimulation (cTBS) in a within-participant design (N=23). Both cortical areas are involved in reward processing and motor control, and we hypothesised that disruption of their activity would result in disruption of the reward-driven effects on action selection and execution, respectively. To increase statistical power, participants were pre-selected based on their sensitivity to reward in the reaching task. While reward did lead to enhanced perforance during the cTBS sessions and a control sham session, cTBS was ineffective in altering these effects. These results may provide evidence that other areas, such as the primary motor cortex or the premotor area, may drive the reward-based enhancements of motor performance.

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bioRxiv2019

Reward-based improvements in motor control are driven by multiple error-reducing mechanisms

Reward has a remarkable ability to invigorate motor behaviour, enabling individuals to select and execute actions with greater precision and speed. However, if reward is to be exploited in applied settings such as rehabilitation, a thorough understanding of its underlying mechanisms is required. Although reward-driven enhancement of movement execution has been proposed to occur through enhanced feedback control, an untested alternative is that it is driven by increased arm stiffness, an energy-consuming process that increases limb stability. First, we demonstrate that during reaching reward improves selection and execution performance concomitantly without interference. Computational analysis revealed that reward led to both an increase in feedback correction during movement and a reduction in motor noise near the target. We provide novel evidence that this noise reduction is driven by a reward-dependent increase in arm stiffness. Therefore, reward drives multiple error-reduction mechanisms which enable individuals to invigorate motor performance without compromising accuracy.

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bioRxiv2019

Domain-specific working memory, but not dopamine-related genetic variability, shapes reward-based motor learning

The addition of rewarding feedback to motor learning tasks has been shown to increase the retention of learning, spurring interest in the possible utility for rehabilitation. However, laboratory-based motor tasks employing rewarding feedback have repeatedly been shown to lead to great inter-individual variability in performance. Understanding the causes of such variability is vital for maximising the potential benefits of reward-based motor learning. Thus, in this pre-registered study, we assessed whether spatial (SWM), verbal (VWM) and mental rotation (RWM) working memory capacity as well as dopamine-related genetic profiles could predict performance in two reward-based motor tasks, using a large cohort of participants (N=241). The first task assessed participants ability to follow a hidden and slowly shifting reward region based on hit/miss (binary) feedback. The second task investigated participants capacity to preserve performance with binary feedback after adapting to the shift with full visual feedback. Our results demonstrate that SWM strongly predicts a participants capacity to reliably reproduce a successful motor action, measured as change in reach angle following reward, while RWM predicted a participants propensity to express an explicit strategy when required to make large adjustments in reach angle. Therefore, both SWM and RWM were reliable predictors of success during reward-based motor learning. Change in reach direction following failure was also a strong predictor of success rate, although we observed no consistent relationship with any type of working memory. Surprisingly, no dopamine-related genotypes predicted performance. Therefore, working memory capacity plays a pivotal in determining individual ability in reward-based motor learning.\n\nSignificance statementReward-based motor learning tasks have repeatedly been shown to lead to idiosyncratic behaviours that cause varying degrees of task success. Yet, the factors determining an individuals capacity to use reward-based feedback are unclear. Here, we assessed a wide range of possible candidate predictors, and demonstrate that domain-specific working memory plays an essential role in determining individual capacity to use reward-based feedback. Surprisingly, genetic variations in dopamine availability were not found to play a role. This is in stark contrast with seminal work in the reinforcement and decision-making literature, which show strong and replicated effects of the same dopaminergic genes in decision-making. Therefore, our results provide novel insights in reward-based motor learning, highlighting a key role for domain-specific working memory capacity.

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bioRxiv2017

The role of explicit strategies during reinforcement-based motor learning

Despite increasing interest in the role of reward in motor learning, the underlying mechanisms remain ill-defined. In particular, the relevance of explicit strategies to reward-based motor learning is unclear. To address this, we examined subjects (n=30) ability to learn to compensate for a gradually introduced 25{degrees} visuomotor rotation with only reward-based feedback (binary success/failure). Only two-thirds of subjects (n=20) were successful at the maximum angle. The remaining subjects initially follow the rotation but after a variable number of trials begin to reach at an insufficiently large angle and subsequently return to near baseline performance (n=10). Furthermore, those that were successful accomplished this largely via the use of strategies, evidenced by a large reduction in reach angle when asked to remove any strategy they employed. However, both groups display a small degree of remaining retention even after the removal of strategies. All subjects made greater and more variable changes in reach angle following incorrect (unrewarded) trials. However, subjects who failed to learn showed decreased sensitivity to errors, even in the initial period in which they followed the rotation, a pattern previously found in Parkinsonian patients. In a second experiment, the addition of a secondary mental rotation task completely abolished learning (n=10), whilst a control group replicated the results of the first experiment (n=10). These results emphasize a pivotal role of strategy-use during reinforcement-based motor learning and the susceptibility of this form of learning to disruption has important implications for its potential therapeutic benefits.

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bioRxiv2017

The relationship between reinforcement and explicit strategies during visuomotor adaptation

The motor systems ability to adapt to changes in the environment is essential for maintaining accurate movements. During such adaptation several distinct systems are recruited: cerebellar sensory-prediction error learning, success-based reinforcement, and explicit strategy-use. Although much work has focused on the relationship between cerebellar learning and strategy-use, there is little research regarding how reinforcement and strategy-use interact. To address this, participants first learnt a 20{degrees} visuomotor displacement. After reaching asymptotic performance, binary, hit-or-miss feedback (BF) was introduced either with or without visual feedback, the latter promoting reinforcement. Subsequently, retention was assessed using no-feedback trials, with half of the participants in each group being instructed to stop using any strategy. Although BF led to an increase in retention of the visuomotor displacement, instructing participants to remove their strategy nullified this effect, suggesting strategy-use is critical to BF-based reinforcement. In a second experiment, we prevented the expression or development of a strategy during BF performance, by either constraining participants to a short preparation time (expression) or by introducing the displacement gradually (development). As both strongly impaired BF performance, it suggests reinforcement requires both the development and expression of a strategy. These results emphasise a pivotal role of strategy-use during reinforcement-based motor learning.

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