Despite the robust correlation between assembly sequences and behavior, only limited evidence is available to support their critical importance in guiding overt behavior. Perhaps the best examples of the reader-centric definition of cell assembly sequences come from ‘brain-machine interface’ (BMI) studies, where the reader-actuator mechanisms are explicitly defined. There are fundamentally two approaches to control cursors, robotic arms or other actuators by volitional control. In the first approach, large numbers of multiple units or LFP patterns from various cortical areas are recorded from and their assembly sequence activity is first correlated with a chosen natural behavior (e.g., arm movement). In this process, various statistical extraction methods are used to identify the conversion parameters that best describe the executed movement (Carmena et al., 2003; Chapin et al., 1999; Hochberg et al., 2006; Taylor et al, 2002). Spiking patterns of neurons that 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,
