Whether the nervous system relies on modularity to simplify acquisition and control of complex motor skills remains controversial. To date, evidence for modularity has been indirect, based on statistical regularities in the motor commands captured by muscle synergies. Here we provide direct evidence by testing the prediction that in a truly modular controller it must be harder to adapt to perturbations that are incompatible with the modules. We investigated a reaching task in which human subjects used myoelectric control to move a mass in a virtual environment. In this environment we could perturb the normal muscle-to-force mapping, as in a complex surgical rearrangement of the tendons, by altering the mapping between recorded muscle activity and simulated force applied on the mass. After identifying muscle synergies, we performed two types of virtual surgeries. After compatible virtual surgeries, a full range of movements could still be achieved recombining the synergies, whereas after incompatible virtual surgeries, new or modified synergies would be required. Adaptation rates after the two types of surgery were compared. If synergies were only a parsimonious description of the regularities in the muscle patterns generated by a nonmodular controller, we would expect adaptation rates to be similar, as both types of surgeries could be compensated with similar changes in the muscle patterns. In contrast, as predicted by modularity, we found strikingly faster adaptation after compatible surgeries than after incompatible ones. These results indicate that muscle synergies are key elements of a modular architecture underlying motor control and adaptation.

When inspecting visual scenes, primates perform on average four saccadic eye movements per second, which implies that scene segmentation, feature binding, and identification of image components is accomplished in <200 ms. Thus individual neurons can contribute only a small number of discharges for these complex computations, suggesting that information is encoded not only in the discharge rate but also in the timing of action potentials. While monkeys inspected natural scenes we registered, with multielectrodes from primary visual cortex, the discharges of simultaneously recorded neurons. Relating these signals to eye movements revealed that discharge rates peaked around 90 ms after fixation onset and then decreased to near baseline levels within 200 ms. Unitary event analysis revealed that preceding this increase in firing there was an episode of enhanced response synchronization during which discharges of spatially distributed cells coincided within 5-ms windows significantly more often than predicted by the discharge rates. This episode started 30 ms after fixation onset and ended by the time discharge rates had reached their maximum. When the animals scanned a blank screen a small change in firing rate, but no excess synchronization, was observed. The short latency of the stimulation-related synchronization phenomena suggests a fast-acting mechanism for the coordination of spike timing that may contribute to the basic operations of scene segmentation.

Muscle synergies have been proposed as a way for the central nervous system (CNS) to simplify the generation of motor commands and they have been shown to explain a large fraction of the variation in the muscle patterns across a variety of conditions. However, whether human subjects are able to control forces and movements effectively with a small set of synergies has not been tested directly. Here we show that muscle synergies can be used to generate target forces in multiple directions with the same accuracy achieved using individual muscles. We recorded electromyographic (EMG) activity from 13 arm muscles and isometric hand forces during a force reaching task in a virtual environment. From these data we estimated the force associated to each muscle by linear regression and we identified muscle synergies by non-negative matrix factorization. We compared trajectories of a virtual mass displaced by the force estimated using the entire set of recorded EMGs to trajectories obtained using 4–5 muscle synergies. While trajectories were similar, when feedback was provided according to force estimated from recorded EMGs (EMG-control) on average trajectories generated with the synergies were less accurate. However, when feedback was provided according to recorded force (force-control) we did not find significant differences in initial angle error and endpoint error. We then tested whether synergies could be used as effectively as individual muscles to control cursor movement in the force reaching task by providing feedback according to force estimated from the projection of the recorded EMGs into synergy space (synergy-control). Human subjects were able to perform the task immediately after switching from force-control to EMG-control and synergy-control and we found no differences between initial movement direction errors and endpoint errors in all control modes. These results indicate that muscle synergies provide an effective strategy for motor coordination.

Cell assemblies, defined as groups of neurons exhibiting precise spike coordination, were proposed as a model of network processing in the cortex. Fortunately, in recent years considerable progress has been made in multi-electrode recordings, which enable recording massively parallel spike trains of hundred(s) of neurons simultaneously. However, due to the challenges inherent in multivariate approaches, most studies in favor of cortical cell assemblies still resorted to analyzing pairwise interactions. However, to recover the underlying correlation structures, higher-order correlations need to be identified directly. Inspired by the Accretion method proposed by Gerstein et al. (1978) we propose a new assembly detection method based on frequent item set mining (FIM). In contrast to Accretion, FIM searches effectively and without redundancy for individual spike patterns that exceed a given support threshold. We study different search methods, with which the space of potential cell assemblies may be explored, as well as different test statistics and subset conditions with which candidate assemblies may be assessed and filtered. It turns out that a core challenge of cell assembly detection is the problem of multiple testing, which causes a large number of false discoveries. Unfortunately, criteria that address individual candidate assemblies and try to assess them with statistical tests and/or subset conditions do not help much to tackle this problem. The core idea of our new method is that in order to cope with the multiple testing problem one has to shift the focus of statistical testing from specific assemblies (consisting of a specific set of neurons) to spike patterns of a certain size (i.e., with a certain number of neurons). This significantly reduces the number of necessary tests, thus alleviating the multiple testing problem. We demonstrate that our method is able to reliably suppress false discoveries, while it is still very sensitive in discovering synchronous activity. Since we exploit high-speed computational techniques from FIM for the tests, our method is also computationally efficient.

To generate a force at the hand in a given spatial direction and with a given magnitude the central nervous system (CNS) has to coordinate the recruitment of many muscles. Because of the redundancy in the musculoskeletal system, the CNS can choose one of infinitely many possible muscle activation patterns which generate the same force. What strategies and constraints underlie such selection is an open issue. The CNS might optimize a performance criterion, such as accuracy or effort. Moreover, the CNS might simplify the solution by constraining it to be a combination of a few muscle synergies, coordinated recruitment of groups of muscles. We tested whether the CNS generates forces by minimum effort recruitment of either individual muscles or muscle synergies. We compared the activation of arm muscles observed during the generation of isometric forces at the hand across multiple three-dimensional force targets with the activation predicted by either minimizing the sum of squared muscle activations or the sum of squared synergy activations. Muscle synergies were identified from the recorded muscle pattern using non-negative matrix factorization. To perform both optimizations we assumed a linear relationship between rectified and filtered electromyographic (EMG) signal which we estimated using multiple linear regressions. We found that the minimum effort recruitment of synergies predicted the observed muscle patterns better than the minimum effort recruitment of individual muscles. However, both predictions had errors much larger than the reconstruction error obtained by the synergies, suggesting that the CNS generates three-dimensional forces by sub-optimal recruitment of muscle synergies.