Dataset for "Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback"

Dataset for the manuscript entitled: Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback. Abstract of the manuscript: Neuroprosthetics offer great hope for motor-impaired patients. One obstacle is that fine motor control requires near-instantaneous, rich somatosensory feedback. Such distributed feedback may be recreated in a brain-machine interface using distributed artificial stimulation across the cortical surface. Here, we hypothesized that neuronal stimulation must be contiguous in its spatiotemporal dynamics in order to be efficiently integrated by sensorimotor circuits. Using a closed-loop brain-machine interface, we trained head-fixed mice to control a virtual cursor by modulating the activity of motor cortex neurons. We provided artificial feedback in real time with distributed optogenetic stimulation patterns in the primary somatosensory cortex. Mice developed a specific motor strategy and succeeded to learn the task only when the optogenetic feedback pattern was spatially and temporally contiguous while it moved across the topography of the somatosensory cortex. These results reveal new properties of sensorimotor cortical integration and set new constraints on the design of neuroprosthetics. Structure of the data: Description of the variables in the data storage dictionary cursor_positions: sequence of the virtual cursor position over a session range_of_rewardable_cursor_position: range of cursor positions that can be rewarded. Upper threshold excluded. Lower threshold included. cursor_times: timing of the cursor positions provided by the cursor_positions data, in the same clock as spike and lick times. lick_times: timing of all recorded licks. reward_times:timing of the opening of the valve that releases the water reward. master_spike_times: time of the spikes of the master neurons. master_spike_shape: spike shape of each spike stored in master_spike_times. The shipe shapes are shown for 3s (30kHz sampling rate), for each 4 electrode of the corresponding tetrode. neighbor_spike_times': same as master_spike_times for neighbor neurons. neighbor_spike_shape': same as master_spike_shape for neighbor neurons. General structure of the variable Each variable is a hierarchical tree of lists: [Protocol][Mouse][Session]. Protocol takes one of the following values: 0: Bar feedback | 1: Full shuffle | 2: No Feedback | 5: Playback Structured Feedback | 7: Spontaneous activity | 8: Barrel Shuffle | 9: Frame shuffle. Mouse and Session iterate through respectively the mice that were involved in the protocole, and the sessions, generally 5 in total