Indeed, in one of our recent investigations of rapid adaptation using laminar probes in V1 we found more gamma-band (30–80 Hz) synchronization between individual spikes and LFPs in the granular layer than in deep and superficial layers (Hansen and Dragoi, 2011). However, despite these differences, synchronous gamma-band activity was observed across all layers, unlike the current study revealing the absence of correlated variability in the middle layers of V1. Nonetheless, although measures of noise correlations and synchronization vary significantly in terms of both mathematical formalism and functional PR-171 nmr implication, they are both measures of local

network processing. Indeed, individual neurons in local networks possess increased spike timing synchronization with local field potentials, which may increase network information flow (Fries et al., 2001). It is entirely possible that the same network could exhibit low trial-to-trial correlated variability as a way to reduce network redundancy

(Abbott and Dayan, 1999; Averbeck and Lee, 2004; Ecker et al., 2010; Shadlen and Newsome, 1998; Gutnisky and Dragoi, 2008) and increased Navitoclax research buy synchronization in order to improve information flow. This possibility is supported by recent evidence (Womelsdorf et al., 2012) reporting that gamma-band synchronization produces spiking activity that is related to minimal noise correlation in firing rates. The network mechanism that we described (Figures 5 and 6) predicts secondly that a broad tuning of intracortical inputs, as in the granular layer, decorrelates responses of nearby neurons, whereas a sharper tuning of intracortical inputs due to long-range horizontal connections, as in the supragranular and infragranular layers, causes strong response correlations (i.e., a larger fraction of common

inputs will originate from iso-oriented cells). This idea critically rests on experimental evidence that the spatial spread of connections in the granular layers is small, whereas in supragranular and infragranular layers neurons receive recurrent input over larger distances (up to several mm) via horizontal and feedback circuitry. The one-layer model described in Figure 5 represents the extension of a recurrent model recently presented by Renart et al. (2010) showing that an “asynchronous state” characterized by low noise correlations emerges spontaneously in cortical circuits when the activity of excitatory and inhibitory populations track each other. The key assumption in the Renart et al. (2010) model is uniform excitatory and inhibitory connection probabilities, (i.e., the probability that two neurons are connected is independent on the neurons’ position in the network). We were able to replicate the findings of Renart et al. (2010) (i.e.