Very small pairwise correlations that have been reported as evidence for asynchrony (e.g., Ecker et al., 2010) can in fact belie large total input correlation (Rossant et al., 2011; Schneidman et al., 2006). The origins of synchronous spiking dictate MAPK inhibitor whether synchrony represents signal or noise. Realistic stimuli have spatiotemporal structure that enables them to coactivate neurons with adjacent or overlapping receptive fields; consequently, coactivation patterns can contain information about the stimulus (Brette, 2012; Dan et al., 1998; Meister et al., 1995).

If coactivation patterns contain information, synchrony represents part of the signal. Although this does not prove that synchrony-encoded MLN2238 mouse signals are decoded, nor can synchrony be labeled noise simply because it reduces the information decodable from rate-encoded signals; indeed, it would be equally unfair to label rate-encoded signals as noise because they compromise the decoding of synchrony-encoded signals (see below). That said, the aforementioned points do not rule out stimulus-independent synchrony that is truly noise (Mastronarde, 1989). What is arguably more important is that

correlated spiking in higher brain areas has been observed to be stimulus dependent (Alonso et al., 1996; deCharms and Merzenich, 1996; Kohn and Smith, 2005; Temereanca et al., 2008), consistent with synchrony-encoded signals being successfully transmitted to the cortex. Requirement 3 is satisfied insofar as synchrony-encoded signals are decodable depending on which type of cells whatever carries the message. It has been suggested that synchrony decoding is implausible because of an “inextricable” link between output correlation and spike rate (de la Rocha et al., 2007). If synchrony transfer were to vary with spike rate, input correlation could not be unambiguously decoded from output correlation without that rate sensitivity being factored in, and indeed the synchrony-encoded information could be lost unrecoverably. However, although synchrony transfer is rate dependent among integrators (except under more extreme

stimulus conditions; Schultze-Kraft et al., 2013), the same is not true for coincidence detectors (Figure 3B) (Hong et al., 2012; Tchumatchenko et al., 2010), which argues that synchrony-encoded messages carried by coincidence detectors are decodable. Hence, pyramidal neurons with coincidence detector traits should be able to produce synchronous output that is decodable. These three requirements reflect upon the encoding, transmission, and decoding of synchrony-based signals. Encoding requires the structured coactivation of neurons. Decoding requires that synchrony-encoded signals are not conflated with other signals; in that respect, decodability depends on reliable transmission. Reliable transmission requires robust synchrony transfer. We must, therefore, understand what makes synchrony transfer robust.