In addition to their current input connectivity vector, readers may be also sensitive to the preceding states of the assembly sequence (Figure 3B; Gütig and Sompolinsky, 2006; Truccolo et al., 2010). As discussed above for BMI actuators, extracting the most accurate information about e.g., arm position is a daunting task when the statistical classifier mechanisms have to monitor a high-dimensional sample of the active state of neurons. In contrast, computational considerations suggest that the high-dimensionality of the input vector, in fact, can often facilitate the extraction of information by neuronal readers (Cover, 1965; Haeusler et al., 2003; Maass et al., 2002; Pulvermüller and Knoblauch, 2009), and separation of trajectories becomes progressively easier with increasing dimensionality of state space (Legenstein and Maass, 2007; Legenstein et al., 2010; c.f., Buonomano and Maass, 2009). This may explain why natural readers, such as neurons, have a high flexibility and can adapt to very subtle differences between neuronal trajectories (Fetz, 2007; Logothetis and Pauls, 1995; Poggio and Edelman, 1990). These examples indicate that extracting useful information from temporally evolving neuronal trajectories of long series of
