Spatiotemporal properties of sensory responses in vivo are strongly dependent on network context

Sensory responses in neocortex are strongly modulated by changes in brain state, such as those observed between sleep stages or attentional levels. However, the specific effects of network state changes on the spatiotemporal properties of sensory responses are poorly understood. The slow oscillation, which is observed in neocortex under ketamine-xylazine anesthesia and is characterized by alternating depolarizing (up-states) and hyperpolarizing (down-states) phases, provides an opportunity to study the state-dependence of primary sensory responses in large networks. Here we used voltage sensitive dye (VSD) imaging to record the spatiotemporal properties of sensory responses and local field potential (LFP) and multiunit activity (MUA) recordings to monitor the ongoing brain state in which the sensory responses occurred. Despite a rich variability of slow oscillation patterns, sensory responses showed a consistent relationship with the ongoing oscillation and triggered a new up-state only after the termination of the refractory period that followed the preceding oscillatory cycle. We show that spatiotemporal properties of whisker-evoked responses are highly dependent on their timing with regard to the ongoing oscillation. In both the up- and down-states, responses spread across large portions of the barrel field, although the up-state responses were reduced in total area due to their sparseness. The depolarizing response in the up-state showed a tendency to propagate along the rows, with an amplitude and slope favoring the higher-numbered arcs. In the up-state, but not in the down-state, the depolarizing response was followed by a hyperpolarizing wave with a consistent spatial structure. We measured the suppression of whisker-evoked responses by a preceding response at 100 ms, and found that suppression showed the same spatial asymmetry as the depolarization. Because the resting level of cells in the up-state is likely to be closer to that in the awake animal, we suggest that the polarities in signal propagation which we observed in the up-state could be used as computational mechanisms in the behaving animal. These results demonstrate the critical importance of ongoing network activity on the dynamics of sensory responses and their integration.