, 2009). An original feature of the GNW model, absent from many other formal neural network Selleckchem NLG919 models, is the occurence of highly structured spontaneous activity (Dehaene and Changeux, 2005). Even in the absence of external inputs, the simulated GNW neurons are assumed to fire spontaneously, in a top-down manner, starting from the highest hierarchical levels of the simulation and propagating downward to form globally synchronized ignited states. When the ascending vigilance signal is large, several such spontaneous ignitions

follow each other in a neverending “stream” and can block ignition by incoming external stimuli (Dehaene and Changeux, 2005). These simulations capture some of the empirical observations on inattentional blindness (Mack and Rock, 1998) and mind wandering (Christoff et al., 2009, Mason et al., 2007 and Smallwood et al., 2008). More complex network architectures have also been simulated in which a goal state is set and continuously shapes the structured patterns of activity that are spontaneously generated, until the goal is ultimately attained (Dehaene and Changeux, 1997 and Zylberberg

et al., 2010). In these simulations, ignited states are stable only for a transient time period and can be quickly destabilized by a negative reward signal that indicates deviation from the current goal, in which case they are spontaneously and randomly replaced by another discrete combination of workspace neurons. The dynamics of such click here networks is thus characterized by a constant flow of individual coherent episodes of variable duration, selected by reward signals in order to achieve a defined goal state. Architectures based on these notions have been applied to a variety of tasks (delayed response: Dehaene and Changeux,

1989; Wisconsin card sorting: Dehaene and Changeux, 1991; Tower of London: Dehaene and Changeux, 1997; Stroop: Dehaene Dichloromethane dehalogenase et al., 1998a), although a single architecture common to all tasks is not yet in sight (but see Rougier et al., 2005). As illustrated in Figure 5, they provide a preliminary account of why GNW networks are spontaneously active, in a sustained manner, during effortful tasks that require series of conscious operations, including search, dual-task, and error processing. In summary, we propose that a core set of theoretical concepts lie at the confluence of the diverse theories that have been proposed to account for conscious access: high-level supervision; serial processing; coherent stability through re-entrant loops; and global information availability. Furthermore, once implemented in the specific neuronal architecture of the GNW model, these concepts begin to provide a schematic account of the neurophysiological signatures that, empirically, distinguish conscious access from nonconscious processing.