Biology subjects

Riehle, A.

Publications and source records attributed to Riehle, A..

9 recordsLinked to original sources

bioRxiv2026

Reciprocal Fronto-Parietal Interactions Support Motor Anticipation during Sequential Reaching

Motor planning is a highly flexible process that adapts to changing behavioral demands. Frontal and parietal circuits are considered as critical nodes that support planning processes. However, it remains unclear whether the coordination of motor and parietal areas is preserved or adjusted in response to varying behavioral contexts. To address this open question, we trained two rhesus macaques to perform a visually-guided sequential reaching task, in which they could adapt their behavior to varying degrees of target predictability within the sequence. Analysis of eye and hand movements revealed distinct visuomotor strategies that were reflected in corresponding neural activity patterns. During motor anticipation, the direction of the upcoming reach toward predictable targets could be decoded from preparatory neural activity prior to target onset both in motor regions (PMd/M1) and parietal area 7A, whereas during reactive movements directional information emerged only after target onset. Using feature-specific information transfer analysis, we found that information about the upcoming movement direction was transmitted between 7A and PMd/M1 through bidirectional interactions. This pattern of interactions was preserved during motor anticipation and shifted earlier in time with respect to target onset. Our findings thus support a reciprocal fronto-parietal network that flexibly adjusts the timing of preparatory activity as different strategies are employed under varying behavioral constraints. Contrary to classical hierarchical models predicting serial activation across parietal and motor areas, parietal-to-motor interactions did not precede motor-to-parietal interactions, consistent with a heterarchical organization of cortical processing during flexible motor planning.

neuroscience↗

bioRxiv2025

Spontaneous spiking statistics form unique area-specific fingerprints and reflect the hierarchy of cerebral cortex

The cerebral cortex is hierarchically organised from sensory to higher cognitive areas1-4. Several dynamical5-8 and anatomical1-4,8 measures, such as timescales and neurotransmitter receptor expression, have independently been linked to the cortical hierarchy. However, a systematic and quantitative characterisation of the relationship between spontaneous spiking activity and the cortical hierarchy remains elusive. Here, we test the hypothesis that single-neuron spontaneous spiking statistics uniquely characterise each cortical area, and that they quantitatively correlate with the cortical hierarchy. We study the spontaneous activity of neurons in seven macaque cortical areas (V1, V4, DP, 7A, M1, PMd, PFC)9-12 in the eyes-open and eyes-closed conditions recorded in a dim-lit room. First, we uncover that the firing rate, inter-spike interval variation, and cross-correlation form a unique fingerprint of the cortical areas, but only when considering them in combination. Second, we show that the differences between the spiking statistics correlate with multiple anatomical markers1,2,4,13-17 of the cortical hierarchy. This effect is much stronger in the eyes-closed condition, suggesting that visual input or the expectation thereof modulates the hierarchical organisation of spontaneous activity. We also observe an increase in timescales up the hierarchy, in agreement with previous findings5,18,19. In conclusion, we demonstrate that spontaneous single-neuron spiking activity reflects the hierarchical organisation of the cerebral cortex: distinct spiking statistics for hierarchically distant areas; similar statistics for nearby areas. Our results thus add a new dynamical dimension to the concept of the cortical hierarchy.

neuroscience↗

bioRxiv2023

Non-instructed Motor Skill Learning in Monkeys: Insights from Deep Reinforcement Learning Models

In the field of motor learning, few studies have addressed the case of non-instructed movement sequences learning, as they require long periods of training and data acquisition, and are complex to interpret. In contrast, such problems are readily addressed in machine learning, using artificial agents in simulated environments. To understand the mechanisms that drive the learning behavior of two macaque monkeys in a free-moving multi-target reaching task, we created two Reinforcement Learning (RL) models with different penalty criteria: "Time" reflecting the time spent to perfom a trial, and "Power" integrating the energy cost. The initial phase of the learning process is characterized by a rapid improvement in motor performance for both the 2 monkeys and the 2 models, with hand trajectories becoming shorter and smoother while the velocity gradually increases along trials and sessions. This improvement in motor performance with training is associated with a simplification in the trajectory of the movements performed to achieve the task goal. The monkeys and models show a convergent evolution towards an optimal circular motor path, almost exclusively in counter-clockwise direction, and a persistent inter-trial variability. All these elements contribute to interpreting monkeys learning in the terms of a progressive updating of action-selection patterns, following a classic value iteration scheme as in reinforcement learning. However, in contrast with our models, the monkeys also show a specific variability in the choice of the motor sequences to carry out across trials. This variability reflects a form of "path selection", that is absent in the models. Furthermore, comparing models and behavioral data also reveal sub-optimality in the way monkeys manage the trade-off between optimizing movement duration ("Time") and minimizing its metabolic cost ("Power"), with a tendency to overemphasize one criterion at the detriment of the other one. Overall, this study reveals the subtle interplay between cognitive factors, biomechanical constraints, task achievement and motor efficacy management in motor learning, and highlights the relevance of modeling approaches in revealing the respective contribution of the different elements at play. Author summaryThe way in which animals and humans learn new motor skills through free exploratory movements sequences solely governed by success or failure outcomes is not yet fully understood. Recent advances in machine learning techniques for continuous action spaces led us to construct a motor learning model investigate how animals progressively enhance the efficiency of their behaviors through numerous trials and errors. This study conducts a comprehensive comparison between deep learning models and experimental data from monkey behavior. Notably, we show that the progressive refinement of motor sequences, as they are observed in the animals, do not require the implementation of a complete model of their environment. Rather, it merely requires the capacity to anticipate both movement costs and final reward a few steps ahead in the future following a value iteration principle. Furthermore, the systematic deviations exhibited by the monkeys with respect to the computational model inform us on the presence of individual preferences in either minimizing the duration or the energy consumption, and also on the involvement of alternative "cognitive" strategies.

animal behavior and cognition↗

bioRxiv2022

Parallel planning through an optimal neural subspace in motor cortex

How do patterns of neural activity in motor cortex contribute to the planning of a movement? A recent theory developed for single movements proposes that motor cortex acts as a dynamical system whose initial state is optimized during the preparatory phase of the movement. This theory makes important yet untested predictions about preparatory dynamics in more complex behavioral settings. Here, we analyzed preparatory activity in non-human primates planning not one, but two movements simultaneously. As predicted by the theory, we found that parallel planning was achieved by adjusting preparatory activity within an optimal subspace to an intermediate state reflecting a tradeoff between the two movements. The theory quantitatively accounted for the relationship between this intermediate state and fluctuations in the animals behavior down at the trial level. These results uncover a simple mechanism for planning multiple movements in parallel, and further point to motor planning as a controlled dynamical process.

neuroscience↗

bioRxiv2020

Long-range coordination patterns in cortex change withbehavioral context

Modern electrophysiological recordings simultaneously capture single-unit spiking activities of hundreds of neurons spread across large cortical distances. Yet this massively parallel activity is often confined to relatively low-dimensional manifolds. This implies strong coordination also among neurons that are most likely not even connected. Here, we combine in vivo recordings with network models and theory to characterize the nature of mesoscopic coordination patterns in macaque motor cortex and to expose their origin: We find that heterogeneity in local connectivity supports network states with complex long-range cooperation between neurons that arises from multi-synaptic, short-range connections. Our theory explains the experimentally observed spatial organization of covariances in resting state recordings as well as the behaviorally related modulation of covariance patterns during a reach-to-grasp task. The ubiquity of heterogeneity in local cortical circuits suggests that the brain uses the described mechanism to flexibly adapt neuronal coordination to momentary demands.

neuroscience↗

bioRxiv2020

On the complexity of resting state spiking activity in monkey motor cortex

Resting state has been established as a classical paradigm of brain activity studies, mostly based on large scale measurements such as fMRI or M/EEG. This term typically refers to a behavioral state characterized by the absence of any task or stimuli. The corresponding neuronal activity is often called idle or ongoing. Numerous modeling studies on spiking neural networks claim to mimic such idle states, but compare their results to task- or stimulus-driven experiments, which might lead to misleading conclusions. To provide a proper basis for comparing physiological and simulated network dynamics, we characterize simultaneously recorded single neurons spiking activity in monkey motor cortex and show the differences from spontaneous and task-induced movement conditions. The resting state shows a higher dimensionality, reduced firing rates and less balance between population level excitation and inhibition than behavior-related states. Additionally, our results stress the importance of distinguishing between rest with eyes open and closed.

neuroscience↗

bioRxiv2020

Spiking neural network model of motor cortex with joint excitatory and inhibitory clusters reflects task uncertainty, reaction times, and variability dynamics

When preparing a movement, we often rely on partial or incomplete information, which can decrement task performance. In behaving monkeys we show that the degree of cued target information is reflected in both, neural variability in motor cortex and behavioral reaction times. We study the underlying mechanisms in a spiking motor-cortical attractor model. By introducing a novel and biologically realistic network topology where excitatory neuron clusters are locally balanced with inhibitory neuron clusters we robustly achieve metastable network activity across a wide range of network parameters. In application to the monkey task, the model performs target-specific action selection and accurately reproduces the task-epoch dependent reduction of trial-to-trial variability in vivo where the degree of reduction directly reflects the amount of processed target information, while spiking irregularity remained constant throughout the task. In the context of incomplete cue information, the increased target selection time of the model can explain increased behavioral reaction times . We conclude that context-dependent neural and behavioral variability is a signum of attractor computation in the motor cortex.

neuroscience↗

bioRxiv2019

On the relationship between LFP beta oscillation amplitude and firing rate of individual neurons in monkey motor cortex

It is a long-standing controversial issue whether an intrinsic relationship between the local field potential (LFP) beta oscillation amplitude and the spike rate of individual neurons in the motor cortex exists. Beta oscillations are prominent in motor cortical LFPs, and their relationship to the local neuronal spiking activity has been extensively studied. Many studies demonstrated that the spikes of individual neurons lock to the phase of LFP beta oscillations. However, the results concerning whether there is also an intrinsic relationship between the amplitude of LFP beta oscillations and the firing rate of individual neurons are contradictory. Some studies suggest a systematic mapping of spike rates onto LFP beta amplitude, and others find no systematic relationship. To resolve this controversy, we correlated the amplitude of LFP beta oscillations recorded in motor cortex of two male macaque monkeys with spike counts of individual neurons during visuomotor behavior, in two different manners. First, in an analysis termed task-related correlation, data obtained across all behavioral task epochs was included. These task-related correlations were frequently significant, and in majority of negative sign. Second, in an analysis termed trial-by-trial correlation, only data from a fixed pre-cue task epoch was included, and correlations were calculated across trials. Such trial-by-trial correlations were weak and rarely significant. We conclude that there is no intrinsic relationship between the firing rate of individual neurons and LFP beta oscillation amplitude in macaque motor cortex, beyond each of these signals being modulated by external factors such as the behavioral task.\n\nSIGNIFICANCE STATEMENTWe addressed the long-standing controversial issue of whether there is an intrinsic relationship between the local field potential (LFP) beta oscillation amplitude and the spike rate of individual neurons in the motor cortex. In two complementary analyses of data from macaque monkeys, we first demonstrate that the unfolding behavioral task strongly affects both the LFP beta amplitude and the neuronal spike rate, creating task-related correlations between the two signals. However, when limiting the influence of the task, by restricting our analysis to a fixed task epoch, correlations between the two signals were largely eliminated. We conclude that there is no intrinsic relationship between the firing rate of individual neurons and LFP beta oscillation amplitude in motor cortex.

neuroscience↗

bioRxiv2017

Behavioral context determines network state and variability dynamics in monkey motor cortex

Variability of spiking activity is ubiquitous throughout the brain but little is known about its contextual dependence. Trial-to-trial spike count variability, estimated by the Fano Factor (FF), and within-trial spike time irregularity, quantified by the local coefficient of variation (CV2), reflect variability on long and short time scales, respectively. We co-analyzed FF and CV2 in monkey motor cortex comparing two behavioral contexts, movement preparation (wait) and execution (movement). We find that FF significantly decreases from wait to movement, while CV2 increases. The more regular firing (low CV2) during wait is related to an increased power of local field potential beta oscillations and phase locking of spikes to these oscillations. In renewal processes, a widely used model for spiking activity under stationary input conditions, both measures are related as FF{approx}CV2. This expectation was met during movement, but not during wait where FF>>CV22. We conclude that during movement preparation, ongoing brain processes result in changing network states and thus in high trial-to-trial variability (high FF). During movement execution, the network is recruited for performing the stereotyped motor task, resulting in reliable single neuron output. We discuss our results in the light of recent computational models that generate non-stationary network conditions.

neuroscience↗