, 2008b), although there is some evidence for predictive signaling even in the motor cortex (Flament and Hore, 1988). The argument for this hypothesis is as follows. During active movement, this population www.selleckchem.com/products/BKM-120.html may not be causally “driving” movement because it leads movement by only 50 ms instead of 100–150 ms, which is the typical “driving” delay seen in motor cortex during reaching movements (Ashe

and Georgopoulos, 1994, Moran and Schwartz, 1999 and Paninski et al., 2004). Instead, it could be predicting future movement direction 50 ms in advance of the actual movement (Figure 4C, right top panel, blue dashed line). The actual source of this predictive signaling could originate in some other cortical or subcortical area. During passive manipulation, one needs ABT-263 ic50 to assume that somatosensory feedback (i.e., tactile and proprioceptive input) can trigger covert motor commands much like the neural population described in the previous section that generated visually evoked covert motor commands. Somatosensory feedback would reach motor cortex with a delay of ∼50 ms (Figure 4C, right bottom panel, red curve). This input would trigger a covert

motor command leading the sensory feedback by ∼100 ms. If this population of neurons predicts the future sensory consequences of the covert motor command by 50 ms, then it would provide information preceding the sensory feedback by 50 ms (Figure 4C, right bottom panel, blue dashed line). Therefore, the predictive sensory lead in this population would offset the sensory delay in the periphery resulting in real-time tracking of movement. This hypothesis is further supported by the Ketanserin fact that the congruent subpopulation exhibited a 50% increase in peak directional information during passive movement as compared to the incongruent subpopulation indicating that the congruent subpopulation

is more faithfully capturing the detailed dynamics of movement. In the previous sections of this review, we have discussed literature demonstrating the richness and diversity in MI neural responses measured during the visual observation of familiar actions, passive movement of the limb, and voluntarily generated movements. This diversity is readily apparent in Figure 5, which shows the normalized binned firing rate as a function of time for each of the 87 neurons recorded during an experiment where monkeys generated active arm movements (blue region), observed playback of recorded movements with only visual (gold regions), proprioceptive (gray), or both types of feedback (red regions). Changes in the experimental condition were precisely correlated with substantial changes in the firing rate of individual neurons appearing as vertical striations in Figure 5. These heterogeneous responses are particularly interesting and potentially advantageous when placed in the context of a neuroprosthetic device or brain-machine interface (BMI).