SummaryGrowing evidence suggests that distributed spatial attention may invoke theta (3–9 Hz) rhythmic sampling processes. The neuronal basis of such attentional sampling is, however, not fully understood. Here we show using array recordings in visual cortical area V4 of two awake macaques that presenting separate visual stimuli to the excitatory center and suppressive surround of neuronal receptive fields (RFs) elicits rhythmic multi-unit activity (MUA) at 3–6 Hz. This neuronal rhythm did not depend on small fixational eye movements. In the context of a distributed spatial attention task, during which the monkeys detected a spatially and temporally uncertain target, reaction times (RTs) exhibited similar rhythmic fluctuations. RTs were fast or slow depending on the target occurrence during high or low MUA, resulting in rhythmic MUA-RT cross-correlations at theta frequencies. These findings show that theta rhythmic neuronal activity can arise from competitive RF interactions and that this rhythm may result in rhythmic RTs potentially subserving attentional sampling.

Electrical microstimulation and more recently optogenetics are widely used to map large-scale brain circuits. However, the neuronal specificity achieved with both methods is not well understood. Here we compare cell-targeted optogenetics and electrical microstimulation in the macaque monkey brain to functionally map the koniocellular lateral geniculate nucleus (LGN) projection to primary visual cortex (V1). Selective activation of the LGN konio neurons with CamK-specific optogenetics caused selective electrical current inflow in the supra-granular layers of V1. Electrical microstimulation targeted at LGN konio layers revealed the same supra-granular V1 activation pattern as the one elicited by optogenetics. Taken together, these findings establish a selective koniocellular LGN influence on V1 supra-granular layers, and they indicate comparable capacities of both stimulation methods to isolate thalamo-cortical circuits in the primate brain.

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Spatial attention allows us to make more accurate decisions about events in our environment. Decision confidence is thought to be intimately linked to the decision making process as confidence ratings are tightly coupled to decision accuracy. While both spatial attention and decision confidence have been subjected to extensive research, surprisingly little is known about the interaction between these two processes. Since attention increases performance it might be expected that confidence would also increase. However, two studies investigating the effects of endogenous attention on decision confidence found contradictory results. Here we investigated the effects of two distinct forms of spatial attention on decision confidence; endogenous attention and exogenous attention. We used an orientation-matching task, comparing the two attention conditions (endogenous and exogenous) to a control condition without directed attention. Participants performed better under both attention conditions than in the control condition. Higher confidence ratings than the control condition were found under endogenous attention but not under exogenous attention. This finding suggests that while attention can increase confidence ratings, it must be voluntarily deployed for this increase to take place. We discuss possible implications of this relative overconfidence found only during endogenous attention with respect to the theoretical background of decision confidence.

Feedforward deep neural networks for object recognition are a promising model of visual processing and can accurately predict firing-rate responses along the ventral stream. Yet, these networks have limitations as models of various aspects of cortical processing related to recurrent connectivity, including neuronal synchronization and the integration of sensory inputs with spatio-temporal context. We trained self-supervised, generative neural networks to predict small regions of natural images based on the spatial context (i.e. inpainting). Using these network predictions, we determined the spatial predictability of visual inputs into (macaque) V1 receptive fields (RFs), and distinguished low- from high-level predictability. Spatial predictability strongly modulated V1 activity, with distinct effects on firing rates and synchronization in gamma- (30-80Hz) and beta-bands (18-30Hz). Furthermore, firing rates, but not synchronization, were accurately predicted by a deep neural network for object recognition. Neural networks trained to specifically predict V1 gamma-band synchronization developed large, grating-like RFs in the deepest layer. These findings suggest complementary roles for firing rates and synchronization in self-supervised learning of natural-image statistics.

When a visual stimulus is repeated, average neuronal responses typically decrease, yet they might maintain or even increase their impact through increased synchronization. Previous work has found that many repetitions of a grating lead to increasing gamma-band synchronization. Here we show in awake macaque area V1 that both, repetition-related reductions in firing rate and increases in gamma are specific to the repeated stimulus. These effects showed some persistence on the timescale of minutes. Further, gamma increases were specific to the presented stimulus location. Importantly, repetition effects on gamma and on firing rates generalized to natural images. These findings suggest that gamma-band synchronization subserves the adaptive processing of repeated stimulus encounters, both for generating efficient stimulus responses and possibly for memory formation.

Summary When a visual stimulus is repeated, average neuronal responses typically decrease, yet they might maintain or even increase their impact through increased synchronization. Previous work has found that many repetitions of a grating lead to increasing gamma-band synchronization. Here, we show in awake macaque area V1 that both repetition-related reductions in firing rate and increases in gamma are specific to the repeated stimulus. These effects show some persistence on the timescale of minutes. Gamma increases are specific to the presented stimulus location. Further, repetition effects on gamma and on firing rates generalize to images of natural objects. These findings support the notion that gamma-band synchronization subserves the adaptive processing of repeated stimulus encounters.

A fundamental property of brain function is that the spiking activity of cortical neurons is variable and that some of this variability is correlated between neurons. Correlated activity not due to the stimulus arises from shared input but the neuronal circuit mechanisms that result in these noise correlations are not fully understood. Here we tested in the visual system if correlated variability in mid-level area V4 of visual cortex is altered following extensive lesions of primary visual cortex (V1). To this end we recorded longitudinally the neuronal correlations in area V4 of two behaving macaque monkeys before and after a V1 lesion while the monkeys fixated a grey screen. We found that the correlations of neuronal activity survived the lesions in both monkeys. In one monkey, the correlation of multi-unit spiking signals was strongly increased in the first week post-lesion, while in the second monkey, correlated activity was slightly increased, but not greater than some week-by-week fluctuations observed. The typical drop-off of inter-neuronal correlations with cortical distance was preserved after the lesion. Therefore, as V4 noise correlations remain without feedforward input from V1, these results suggest instead that local and/or feedback input seem to be necessary for correlated activity.