![]() Our findings enrich the model of simple and complex cells and further understand the way that attention influences the neurons’ activities.Įpisodic memory enables humans to encode and later vividly retrieve information about our rich experiences, yet the neural representations that support this mental capacity are poorly understood. Thus, we found that spatial attention increased the functional communications and competing connectivities when attending to the neurons’ RFs, which impacts the interactions only between simple and complex cells. Furthermore, we found that the attentional modulation of neuronal interactions changed with neuronal pairs’ preferred directions differences. ![]() The results showed that spatial attention significantly influenced only the interactions between rather than within simple and complex cells. An attention-related increase in spike count correlations and a decrease in Granger causality were found. We compared attentional modulation from the perspective of spike count correlations and Granger causality among simple and complex cells. Here, we investigated the activity of anatomically distant neurons in two behaving monkeys’ primary visual cortex (V1), when they performed a spatial attention task detecting color change. However, the attentional modulation of the interaction between pairs of neurons with non-overlapping receptive fields (RFs) is not well known. It resolves the process of competing for sensory information about objects perceived as targets and distractors. The influence of spatial attention on neural interactions has been revealed even in early visual information processing stages. In conclusion, the inverse relationship between perceptual performance and correlated variability can be explained by observers using a general decoding strategy, capable of decoding neuronal responses to the variety of stimuli encountered in natural vision. We found that general decoders optimized for broad rather than narrow sets of visual stimuli better matched the animals' decoding strategy, and that their performance was more related to the magnitude of correlated variability. We tested this using a combination of multineuron recordings in the visual cortex of behaving rhesus monkeys and a cortical circuit model. We hypothesize that this relationship arises because instead of using optimal strategies for decoding the specific stimuli at hand, observers prioritize generality: a single set of neuronal weights to decode any stimuli. This relationship is puzzling because overall changes in correlated variability should minimally affect optimal information coding. Improvements in perception are frequently accompanied by decreases in correlated variability in sensory cortex. (i) Cumulative distributions of attentional indices in beta power for the control (solid line) and lesion (dashed line) hemisphere. (g,h) Normalized LFP beta power averaged between 15–30 Hz in the control (g) and lesion (h) hemisphere. (f) Cumulative distributions of attentional latencies in gamma power in the control (solid line) and lesion (dashed line) hemisphere. (e) Cumulative distributions of attentional indices in gamma power for the control (solid line) and lesion (dashed line) hemisphere. (c,d) Normalized LFP gamma power averaged between 50–90 Hz in the control (c) and lesion (d) hemisphere. (b) Population average of attentional effects (attention inside RF-attention outside RF) on LFP power in the lesion-affected hemisphere as a function of trial time. (a) Population average of attentional effects (attention inside RF-attention outside RF) on LFP power in the control hemisphere as a function of trial time. Effect of attention on V4 LFP power in the control and lesion-affected hemisphere.
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