Summary

3. General Discussion

In this dissertation the encoding, transformation, and coordination of information across the fronto-parietal grasping network was investigated while monkeys performed two different tasks. In the first task monkeys were either instructed or free to choose to grasp a target in two different ways, allowing for an investigation of internal decision making. In the second task monkeys performed the transition of immediate and delayed grasp movements, allowing for a detail investigation of this transition. In order to analyse the exact nature of the neuronal process within and across the fronto-parietal network including area AIP and F5 (and in chapter 2.1 also M1) large populations of neurons were recorded in parallel across all areas. Especially the possible to analyses the simultaneous activity of this area-spanning neuronal population gave new insights into the encoding, transformation and coordination of the behavioural relevant information within the network. In the following paragraph the results are summarized in detail.

3.1. Summary

In chapter 2.1 it was analyzed how the information flow is coordinated across the fronto-parietal single neuron network. Large numbers of single neurons were recorded in parallel across AIP, F5, and M1 while monkeys performed a delayed grasping task and the functional connectivity between all pairs of neurons was calculated based on cross-correlation

histograms. To achieve a reliable estimate of the functional network connectivity, a new statistical procedure that corrected for multiple comparisons across different temporal delays and neuronal pairings was developed. This procedure allowed us to analyze the form of synchronization together with the functional network topology. The functional fronto-parietal single neuron network was nowhere near randomly organized, but appeared as a complex network, with a modular and small-word topology. Interestingly, the centrality distributions of all datasets were highly heterogeneous based on degree centrality as well as betweenness centrality, which could not be explained by distance-dependent connectivity.

This indicated that functional hub neurons likely coordinated the network activity. The hub neurons were equally distributed across all three areas and strongly interconnected, forming an area-spanning coordinative rich-club. Surprisingly, when we analyzed the form of

synchronization, neurons were either synchronized by oscillatory synchrony in the beta-band, in the low-frequency range, or synchronized in a non-oscillatory manner. Intriguingly, the hub neurons forming a rich-club were oscillatory synchronized nearly without exception, while large parts of the rest of the network were non-oscillatory synchronized. When we analyzed the rhythmicity of the spiking of hub neurons, they were nearly exclusively rhythmically active in the beta- or low-frequency band, defining them as oscillators. Thus, the findings of this study suggest that the information flow of the fronto-parietal grasping network is coordinated by an area-spanning oscillatory-synchronized rich-club.

In chapter 2.2 it was investigated how information is encoded and transformed in the fronto-parietal grasping network while monkeys were either visually instructed or freely choosing to grasp a handle with one of two grip types. When analyzing the neuronal

population from the classical representational view, describing activity of individual neurons as a function of various parameters, a large number of neurons were significantly tuned in AIP and F5 of the fronto-parietal grasping network and during all time points of the task.

However, tuning changed dynamically over time and tuning parameters were uniformly distributed across the population; both findings were at odds with the classical

representational view. In contrast, when considering the whole neuronal population as one strongly interconnected network, in which neural population activity evolves dynamically through space-space over time and conditions as suggested by the dynamical system perspective, a clear low dimensional structure became apparent. All task specific single trial activity could be explained by an evolution through just three independent informational subspaces representing visual, preparatory, and movement activity. Interestingly, for free-choice trials, where no specific visual information was given, all task specific activity during the decision process was explained by the preparatory space, suggesting that decision related activity and preparatory activity were the same for this task. Furthermore, changes of mind, e.g. when enforced by a later given second visual instruction, were clearly visible in the preparatory space. Crucially, contributions to all three informational spaces were

randomly distributed across neurons with no significant category structure. A regularized recurrent neuronal network trained to produce muscle activity for the two grasps could accurately reproduce the neuronal dynamics both at the single unit and the population level.

These results indicate that instead of addressing the attributes of individual neurons, neuronal activity can be more completely understood at the population level, where a

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neuronal population can encode different processes at different and overlapping times.

These processes can be dynamically transformed according to the behavioral demands, including free choices.

In chapter 2.3 the neuronal population encoding in AIP and F5 of the transition between immediate and withheld movement was examined. Single neuron responses of both areas were complex and difficult to characterise from the representation view.

However, when considered on the population-level and visualized by dimensionality reduction techniques, a clearly describable temporal and conditional population dynamics became apparent. Neuronal population dynamics of both areas first followed a grip specific defined trajectory indistinguishable for immediate up to long delayed grasps. Theses trajectories properly represented unavoidable processing from visual to preparatory information. However, after this initial phase, population activity in AIP tended to stabilize, whereas activity in F5 continued to evolve through state space, likely reflecting movement anticipation. Interestingly, population activity of both areas evolved through two distinct and significantly separate spaces for immediate movements and withhold delayed movements, suggesting a unique state for movements performed from memory. However, trajectories for the different grasp movements were maintained in separate spaces. These findings suggest that the complex interplay of dynamical and static aspects of movement

preparation, such as anticipation and planning of a particular grasp type, can be understood as an evolution of neuronal population activity through specific dimensions of a higher dimensional state space.

In the work presented in Appendix A we evaluated how representational models based on single neuron characterizations, and dynamical system models based on the neuronal population activity describing the generation of reach movements in PMd and M1, can be integrated and better tested for their validity. This study builds upon the results of Churchland M. et al. 2012 showing that population dynamics during reach movements can be described by a dynamical system model, with the preparatory state serving as an initial state of a rotation dynamic. However, by simulating simple velocity-tuned neurons for a center-out reaching task and incorporating variable latencies between kinematics and individual neuronal activities, rotational dynamics appeared on the population level. Yet, meaningful rotational dynamics should depend on the conditional population structure, while this should be irrelevant for representational models. To distinguish between these

two possibilities, we developed a covariance-matched permutation test (CMPT) that reassigned neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships. While the rotations of representational models of neuronal activity did not depend on the conditional structure, they did strongly depend on the conditional structure for recorded data as well as a RNN trained to produce kinematics. These findings speak in favour of the dynamical systems perspective in

describing motor cortex population dynamics. Interestingly, directional tuning was an emergent property of our RNN model simply as a consequence of the generated output parameters. Yet, the directional tuning was found to change over time and neuronal tuning was often only roughly matched by a cosine tuning function, similar to recorded neurons.

These observations suggest that, even if representational models can describe single neuron data to a certain extent, their results can nonetheless be misleading, and the neuronal population dynamics can potentially be better explained by a dynamical system model.

Finally, in the study described in Appendix B we showed that the reaction time to initiate a grasp movement could be predicted from the activity of large numbers of simultaneously recorded neurons in AIP and F5. Single-trial preparatory activity of both areas was predictive of reaction time, although results differed strongly based on the

method of analysis used. Population-based methods for predicting reaction time were found to give better and more reliable results then single neuron based predictions for both areas.

Interestingly, in comparing different population-based methods, those which were not based on the assumption that shorter reaction times are associated with higher firing rates performed much better. Furthermore, the predictive information was distributed across the whole population of neurons of both areas with no evidence for distinct subpopulations tuned to reaction time. However, neuronal populations of F5 were more predictive than populations of AIP, suggesting that F5 populations are more directly related to grasp initiation. These observations indicate that aspects of movement initiation are distributed across neuronal populations and even across different brain areas.