Abstract
Understanding the neural computations involved in different motor behaviours is extremely challenging, and it is even more complicated to study the effect of visual background on movement-related neuronal activity in associative parietal areas. Single neuron approaches could only provide a limited view on how neural computations are carried out, without considering network connectivity and timing relationships between single neuron discharges. Networks of interconnected neurons could in principle generate a huge number of activity patterns, but Hebbian theory, anatomical connectivity, and plasticity constrain the neural population to discharge according to a rather small number of “neural states”. Here, we recorded 44 neurons from V6A, a visuomotor area of the medial posterior parietal cortex, in a Macaca fascicularis monkey while the animal performed a delayed reaching task towards 3 targets placed at different directions. Reaching movements occurred in the light and in the dark. To study the ‘neural states’, we applied an unsupervised machine learning method, the Hidden Markov Model. We also applied a neural dimensionality reduction technique to highlight differences in neural patterns. We found that both conditions (light and dark) produce similar sequence of neural states whose timing coincided with observable task events. Generalization analysis revealed that, even if the produced behavior and the neural states are apparently the same, the two contexts elicit different population activity patterns. This is also confirmed by the neural trajectories that, depending on the visual background, seem to be confined in two different subspaces with a proper temporal and spatial evolution. These results support the key role of the medial posterior parietal cortex in computing visuospatial transformations during static and dynamic phases of a reaching movement. This aspect needs to be investigated in future studies to understand how the different visual background and the vision of the hand modulates this area.