, 2010). Apart from these differences, a common mechanism has emerged from studies of different species: leaky coincidence detectors integrate excitatory signals from specialized synapses to produce well-timed spikes that encode the horizontal location of sound

sources with amazing accuracy. “
“The synaptic connectivity between neurons comprising a network is critical for the operation of that network but so too are the intrinsic properties of the constituent neurons. When it comes to studying network operation, RNA Synthesis inhibitor focus on the former has often trumped consideration of the latter. We will, in this Perspective, shift the focus to neuronal properties and address how those properties affect the collective activity within a network, particularly with respect to synchrony (for review of network properties affecting synchrony, see Kumar et al., 2010). To be clear, we will not consider synchrony associated with network oscillations; instead, we will focus on the sort of stimulus-driven synchrony considered to be a “trivial reflection of anatomical connectivity” insofar as it arises in neurons receiving common input (Singer, 1999). Despite its humble origins, such synchrony has fundamentally important consequences for network coding and has been the focus

of much debate (Brette, 2012; Bruno, 2011; de la Rocha et al., 2007; Diesmann et al., 1999; Ermentrout et al., 2008; Estebanez et al., 2012; Hong et al., 2012; Ikegaya et al., 2004; Josić et al., 2009; Kumar et al., 2008; Ostojic et al., 2009; Panzeri et al., 2010; Renart et al., 2010; BYL719 order Rossant et al., 2011; Salinas and Sejnowski, 2001; Sharafi

et al., 2013; Stanley, 2013). Does this synchrony help or hinder network coding? Neuronal properties are a crucial yet underappreciated component of the answer. Neurons are often said to operate as integrators or as coincidence detectors based on how they process input (Abeles, 1982; König et al., 1996). Integrators can summate temporally dispersed (asynchronous) inputs, whereas coincidence detectors respond only to temporally coincident (synchronous) all inputs. In other words, integrators and coincidence detectors are both sensitive to synchronous input, but coincidence detectors are selective for it. Selectivity is, as we will explain, derived from the dynamical mechanism responsible for transforming synaptic input into output spiking. Spike initiation dynamics also affect whether sets of neurons that receive common synchronous input spike synchronously and whether or not that output synchrony is easily disrupted ( Figure 1). Spike initiation dynamics thus control synchrony transfer—the degree to which synchronous input elicits synchronous output. The precision and robustness of synchrony transfer has critical implications for both rate- and synchrony-based coding.