“The segregation of continuously varying stimuli into discrete, behaviorally relevant groups, a process referred to as categorization, is central to perception, stimulus identification, and decision making (Freedman and Assad, 2006, Freedman et al., 2001, Leopold and Logothetis, 1999 and Niessing and Friedrich, 2010). In some cases, the boundary between categories is fixed (Prather et al., 2009). In most cases, however, the boundary needs to adjust according to context, a process referred to as flexible categorization. Recent research suggests that such flexible categorization also contributes to competitive stimulus selection for gaze
and attention (Mysore and Knudsen, 2011b). A midbrain network that plays an essential role in gaze and PI3K inhibitor attention (Cavanaugh and Wurtz, 2004,
Lovejoy and Krauzlis, 2010, McPeek and Keller, 2004 and Müller et al., 2005) OSI-744 nmr segregates stimuli into “strongest” and “others” (Mysore and Knudsen, 2011a). The midbrain network includes the optic tectum (called the superior colliculus in mammals) and several nuclei in the midbrain tegmentum, referred to as the isthmic nuclei (Knudsen, 2011). Categorization by this network tracks the location of the strongest stimulus in real time as a precursor to the selection of the next target for gaze and attention. Despite the importance of flexible categorization to a broad range of functions, how the brain implements it is not known. Categorization by the midbrain network arises from special response properties of a subset
of neurons located in the intermediate and deep layers of the owl optic tectum (OTid) (Mysore et al., 2011 and Mysore and Knudsen, 2011a). These neurons display “switch-like” responses, firing at a high rate when the stimulus inside not their classical receptive field (RF) is the strongest (highest intensity or speed) but switching abruptly to a lower firing rate when a distant, competing stimulus becomes the strongest. This switch-like property causes the encoding of categories by the OTid to be explicit: the category can be read out directly from the population activity pattern without any further transformations beyond simple linear operations, such as averaging (Gollisch and Meister, 2010). In addition, if the strength of the stimulus inside the RF is increased, a switch-like neuron requires a correspondingly stronger competing stimulus to suppress its responses. This property causes the category boundary to be flexible, enabling network responses to reliably identify the strongest stimulus at each moment in time. Explicit and flexible categorization by this network dramatically improves the discriminability of the strongest stimulus among multiple competing stimuli of similar strength (Mysore et al.