Inhibition and excitation form two fundamental modes of neuronal interaction, yet we understand relatively little about their distinct roles in service of perceptual and cognitive processes. We developed a multidimensional waveform analysis to identify fast-spiking (putative inhibitory) and regular-spiking (putative excitatory) neurons in vivo and used this method to analyze how attention affects these two cell classes in visual area V4 of the extrastriate cortex of rhesus macaques. We found that putative inhibitory neurons had both greater increases in firing rate and decreases in correlated variability with attention compared with putative excitatory neurons. Moreover, the time course of attention effects for putative inhibitory neurons more closely tracked the temporal statistics of target probability in our task. Finally, the session-to-session variability in a behavioral measure of attention covaried with the magnitude of this effect. Together, these results suggest that selective targeting of inhibitory neurons and networks is a critical mechanism for attentional modulation.

The trial-to-trial response variability of nearby cortical neurons is correlated. These correlations may strongly influence population coding performance. Numerous studies have shown that correlations can be dynamically modified by attention, adaptation, learning, and potent stimulus drive. However, the mechanisms that influence correlation strength remain poorly understood. Here we test whether correlations are influenced by presenting stimuli outside the classical receptive field (RF) of visual neurons, where they recruit a normalization signal termed surround suppression. We recorded simultaneously the activity of dozens of cells using microelectrode arrays implanted in the superficial layers of V1 in anesthetized, paralyzed macaque monkeys. We presented annular stimuli that encircled-but did not impinge upon-the RFs of the recorded cells. We found that these "extra-classical" stimuli reduced correlations in the absence of stimulation of the RF, closely resembling the decorrelating effects of stimulating the RFs directly. Our results suggest that normalization signals may be an important mechanism for modulating correlations.

A central neuroscientific pursuit is understanding neuronal interactions that support computations underlying cognition and behavior. Although neurons interact across disparate scales – from cortical columns to whole-brain networks – research has been restricted to one scale at a time. We measured local interactions through multi-neuronal recordings while accessing global networks using scalp EEG in rhesus macaques. We measured spike count correlation, an index of functional connectivity with computational relevance, and EEG oscillations, which have been linked to various cognitive functions. We found a surprising non-monotonic relationship between EEG oscillation amplitude and spike count correlation, contrary to the intuitive expectation of a direct relationship. With a widely-used network model we replicated these findings by incorporating a private signal targeting inhibitory neurons, a common mechanism proposed for gain modulation. Finally, we report that spike count correlation explains nonlinearities in the relationship between EEG oscillations and response time in a spatial selective attention task.

People's beliefs are in uenced by interactions within their communities. The propagation of this in uence through conversational social networks should impact the degree to which community members synchronize their beliefs. To investigate, we recruited a sample of 140 participants and constructed fourteen 10-member communities. Participants rst rated the accuracy of a set of statements (pre-test) and were then provided with relevant evidence about them. Then, participants discussed the statements in a series of conversational interactions, following predetermined network structures (clustered/nonclustered). Finally, they rated the accuracy of the statements again (post-test). The results show that belief synchronization, measuring the increase in belief similarity among individuals within a community from pre-test to post-test, is in uenced by the community's conversational network structure. This synchronization is circumscribed by a degree of separation effect and is equivalent in the clustered and non-clustered networks. We also nd that conversational content predicts belief change from pre-test to post-test.