However, the function of the premovement preparatory activity seems not to be simply to rise to a point close to this boundary, as the rise-to-threshold hypothesis would suggest. Instead we note that the firing rates of some neurons fall (rather than rise) after the go cue, and—crucially—do so even if the firing of those same neurons had increased during the preparatory VE-822 research buy phase (Churchland et al., 2010a and Churchland and Shenoy, 2007b).

Thus, the path by which the system approaches this crossing point may be indirect. This observation was reflected in our RT predictions in two ways. First, the directions of the mean neural trajectory before and after the go cue ( p¯go−Δt′ and p¯go+Δt) differed, so that taking both into account improved RT predictions.

Second, three alternative schemes (see Experimental Procedures) that used the extent of rise or change in firing rates from their baseline values at the time of the go cue to predict RT did not perform as well; results from the best performing of the three are shown in Figure 5. Thus, we conclude that neural activity during movement preparation does not simply rise to, or directly approach, a movement-initiation threshold. The optimal subspace hypothesis suggests that for each possible desired movement goal there is a set of consistent preparatory network states, which all lead to movements C646 research buy that achieve that goal. The role of preparation is then to find one such state, and the computation necessary to do so is reflected in the dynamical evolution of the network state from its relatively uncontrolled pretask value to a point in the optimal subregion. Our results augment this view of preparation. There are many possible mechanisms by

which the preparatory activity may determine the activity associated with the execution of the movement, and thus the parameters of the movement itself. Our results suggest that the mechanism is, in fact, embodied in the dynamics of the network. It seems that the network activity evolves smoothly away from the optimal preparatory states when the movement is triggered. Thus, the optimality of the subregion may simply reflect the fact that all states within it form suitable initial conditions from which the dynamics of the network may evolve to generate the appropriate muscular control signals to generate Methisazone the corresponding movement. All such points may lead to movements that achieve the task goals adequately well. However, those states that happen to fall farther in the direction along which neural activity needs to evolve to generate the movement, and which reflect continued movement in that direction, allow the movement to begin sooner. Previous results that have provided evidence for the optimal subspace hypothesis remain consistent with this view. Preparatory activity must still reach the subregion of adequate initial conditions, leading to a fall in neural variance across trials during motor preparation (Churchland et al., 2006c).