Biology subjects

Grün, S.

Publications and source records attributed to Grün, S..

11 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↗

bioRxiv2026

A Quality Measure for Repeating Multiple-Unit Spike Patterns

We propose a quality measure for spatio-temporal spike patterns (STPs) in multiple-neuron recordings. In such recordings, repeating STPs or pattern repetitions (PRs) are often found, with many of these generated by chance. To rule those out, statistical tests have been developed to discriminate the unlikely from the more likely PRs. This statistical problem is complicated by the fact that there are several obvious quality criteria for a PR, such as the size (the number of spikes) of the pattern and the number of its occurrences. Here, we propose a canonical way of combining several criteria (which we collect in the so-called signature of the pattern) into a single quality measure, based on the unlikeliness of the pattern. This measure is defined mathematically, and a formula for its computation is derived for stationary spike trains. It can be used to compare PRs. Since spike trains are not stationary in practice, we discuss, for two experimental data sets, how well the stationary formula correlates with the defined quality measure as determined from simulations. The results encourage the use of the stationary formula or also some simpler, related formulas as proxies for the quality, for the comparison of PRs and also for statistical tests that avoid the multiple testing problem incurred by using several quality criteria. Based on our results, we propose a few test statistics, i.e., random variables on the space of multi-unit spike trains with an appropriate null-hypothesis distribution, to evaluate STPs with less computational and sampling efforts.

neuroscience↗

bioRxiv2025

The effect of data preprocessing on spike correlation analysis results

In recent years, an increasing number of large electrophysiological data sets have become publicly available, thereby providing researchers with the opportunity to analyze spike train data without conducting their own experiments. While this is undoubtedly a positive development, it increases the need for proper documentation on how the data were collected and what preprocessing was performed on the data, since interpreting analysis results in ignorance of these pieces of information can lead to wrong conclusions. An important preprocessing step is the removal of artifacts from the recordings. Electrophysiological recordings are particularly susceptible to electrical cross-talks between recording channels, resulting in artifact spikes that are coincident in multiple channels on the time scale of the data sampling rate, i.e., 1/30 ms in popular setups. The removal by signal whitening is only possible if also the raw sampled data are available, thus to eliminate this type of artifact is to remove all coincident spikes on the recording time scale to definitely avoid artifact spikes. However, given the lack of the "ground truth", this step has the potential to eliminate, in conjunction with the artifacts, components of the data that are pertinent to the research objective. In this study, we use a modified version of the Unitary Event Analysis and demonstrate that such preprocessing results in significantly lower correlations than expected by chance even on longer time scales. We also propose a method to correct for the bias introduced by this preprocessing. Thus, slight changes in the preprocessing have potentially strong impact on analysis results and methods need to consider these effects.

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↗

bioRxiv2023

Neural manifolds in V1 change with top-down signals from V4 targeting the foveal region

High-dimensional brain activity is often organized into lower-dimensional neural manifolds. However, the neural manifolds of the visual cortex remain understudied. Here, we study large-scale multielectrode electrophysiological recordings of macaque (Macaca mulatta) areas V1, V4 and DP with a high spatio-temporal resolution. We find, for the first time, that the population activity of V1 contains two separate neural manifolds, which correlate strongly with eye closure (eyes open/closed) and have distinct dimensionalities. Moreover, we find strong top-down signals from V4 to V1, particularly to the foveal region of V1, which are significantly stronger during the eyes-open periods, a previously unknown effect. Finally, in silico simulations of a balanced spiking neuron network qualitatively reproduce the experimental findings. Taken together, our analyses and simulations suggest that top-down signals modulate the population activity of V1, causing two distinct neural manifolds. We postulate that the top-down modulation during the eyes-open periods prepares V1 for fast and efficient visual responses, resulting in a type of visual stand-by state.

neuroscience↗

bioRxiv2022

Model of multiple synfire chains explains cortical spatio-temporal spike patterns

It has been postulated that information processing in the brain is based on precise temporal correlation of neural activity across populations of neurons. In a recent study we found spatio-temporal spike patterns in experimental recordings from monkey motor cortex, and here we study if those could be explained by a synfire chain (SFC) like model. The model is composed of groups of neurons connected in feed-forward manner from one group to the next with high convergence and divergence. When activated, e.g., by a current pulse to the first group, spiking activity in the SFC is synchronous within neurons of the same group and propagates from group to group. When a few neurons from different groups are recorded from such an SFC, and the SFC is repeatedly activated, we would find a spatio-temporal spike pattern repeating across trials. Here, we take the statistics of the STPs found in the experimental data from 20 sessions as a reference to compare to a simulated network. Distributions of the data we take into account include 1) the pattern sizes, i.e. the number of neurons involved in the patterns, 2) the number of patterns a single neuron is involved in, 3) the durations of the patterns, and 4) the spatial distances of the patterns across the electrode array used to record the data. For the simulations, we embed SFC(s) in an anatomical model of the respective layer of the motor cortex, defined by its height and the density of the neurons. Model parameters are the length of the SFC, the number of neurons per group, the spatial extent of each neuronal group, and the distance between subsequent groups. Given the size and reach of the Utah array electrodes, we derive the probability of recording neurons from the SFC network. An SFC is considered detected if at least two neurons from two different groups are recorded. We find that depending on the model parameters, an embedded SFC can be detected with high probability, despite the massive subsampling of the cortex by the Utah array. Furthermore, to achieve multiple membership of a neuron in different patterns, we embed multiple SFCs that overlap. The fitting of the model to the pattern data constrains the spatial SFC parameters: the chains have to be broadly distributed in space and contain many neurons per group to match the experimental results.

neuroscience↗

bioRxiv2022

Overcoming the Reproducibility Crisis - Results of the first Community Survey of the German National Research Data Infrastructure for Neuroscience.

The lack of reproducibility of research results is a serious problem - known as "the reproducibility crisis". The German National Research Data Infrastructure (NFDI) initiative implemented by the German Research Foundation (DFG) aims to help overcoming this crisis by developing sustainable solutions for research data management (RDM). NFDI comprises domain specific consortia across all science disciplines. In the field of neuroscience, NFDI Neuroscience (NFDI-Neuro) contributes to the strengthening of systematic and standardized RDM in its research communities. NFDI-Neuro conducted a comprehensive survey amongst the neuroscience community to determine the current needs, challenges, and opinions with respect to RDM. The outcomes of this survey are presented here. The German neuroscience community perceives barriers with respect to RDM and data sharing mainly linked to (1) lack of data and metadata standards, (2) lack of community adopted provenance tracking methods, 3) lack of a privacy preserving research infrastructure for sensitive data (4) lack of RDM literacy and (5) lack of required time and resources for proper RDM. NFDI-Neuro aims to systematically address these barriers by leading and contributing to the development of standards, tools, and infrastructure and by providing training, education, and support, as well as additional resources for RDM to its research community. The RDM work of NFDI-Neuro is conducted in close collaboration with its partner EBRAINS AISBL, the coordinating entity of the EU Flagship Human Brain Project, and its Research Infrastructure (RI) EBRAINS with more than 5000 registered users and developers from more than 70 countries of all continents. While NFDI-Neuro aims to address the German national needs, it closely aligns with the international community and the topics of the Digital Europe Program and EU Data Spaces. Significance StatementA comprehensive survey amongst the neuroscience community in Germany determined the current needs, challenges, and opinions with respect to standardized research data management (RDM) to overcome the reproducibility crisis. Significant deficits were pointed out concerning the perceived lack of standards for data and metadata, lack of provenance tracking and versioning of data, lack of protected digital research infrastructure for sensitive data and the lack of education and resources for proper RDM. Yet, at the same time, an overwhelming majority of community members indicated that they would be willing to share their data with other researchers and are interested to increase their RDM skills. Thus, the survey results suggest that training, the provision of standards, tools, infrastructure and additional resources for RDM holds the potential to significantly facilitate reproducible research in neuroscience.

neuroscience↗

bioRxiv2021

Generating surrogates for significance estimation ofspatio-temporal spike patterns

The generation of surrogate data, i.e., the modification of original data to destroy a certain feature, is used for the implementation of a null-hypothesis whenever an analytical approach is not feasible. Thus, surrogate data generation has been extensively used to assess the significance of spike correlations in parallel spike trains. In this context, one of the main challenges is to properly construct the desired null-hypothesis distribution and to avoid a bias in the null-hypothesis by altering the spike train statistics. A classical surrogate technique is uniform dithering (UD), which displaces spikes locally and uniformly. In this study, we compare UD against five surrogate techniques (two newly introduced) in the context of the detection of significant spatio-temporal spike patterns. We evaluate the surrogates for their performance, first on spike trains based on point process models with constant firing rate, and second on modeled non-stationary artificial data serving as ground truth to assess the pattern detection in a more complex and realistic setting. We determine which statistical features of the original spike trains are modified and to which extent. Moreover, we find that UD fails as an appropriate surrogate because it leads to a loss of spikes in the context of binning and clipping, and thus to a large number of false-positive patterns. The other surrogates achieve a better performance in detecting precisely timed higher-order correlations. Based on these insights, we analyze experimental data from pre-/motor cortex of macaque monkeys during a reaching-and-grasping task for spatio-temporal spike patterns. Significance statementTemporal jittering or dithering of single spikes or subsections of spike trains is a common method of generating surrogate data for the statistical analysis of temporal spike correlations. We discovered a serious problem with the classical and widely used method of uniform dithering that can lead to an overestimation of significance, i.e., to false positives in the statistical evaluation of spatio-temporal spike patterns. Therefore we consider 5 other dithering methods, compare and evaluate their statistical properties. Finally, we apply a much better method (trial shifting) to the analysis of experimental multiple-unit recordings and find several highly significant patterns that also reflect different experimental situations.

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↗