significantly contribute to the conversion parameters constitute the assembly sentence (Figure 5). In the next stage, these extracted parameters are used as a ‘transform algorithm’ (i.e., a ‘statistical reader’) to control an actuator by brain activity. In the second approach, one or more neurons are chosen and their spiking activity is used to define the various degrees of freedom of the actuator (e.g., 2 neurons for 2-D cursor). These effector neurons are then ‘trained’ to generate the desired spike patterns needed to move the cursor. In this latter approach, it is left to upstream networks to ‘figure out’ the successful, intention-controlled neuronal trajectories, without the need of an experimenter-designed complex transformation algorithm (Donoghue 2002; Fetz, 1969; 2007; Kennedy and Bakay, 1998; Legenstein et al., 2010). The readers, in this case, are the effector neurons and their spiking activity defines the cell assembly sentences that lead to their patterned discharge. During the course of training, the natural proprioceptive feedback is substituted by visual observation of the movements of the effector device. By assigning a new goal, the relationship among the recorded neurons is modified and the muscular movements previously elicited by the firing patterns of the neurons can disappear (e.g., Fetz,
