Prefrontal attentional saccades explore space rhythmically

Gaillard,; Hassen, Sameh Ben Hadj; Bello, Fabio Di; Bihan-Poudec, Yann; VanRullen, Rufin; Hamed, Suliann Ben

doi:10.1101/637975

Cited by 4 publications

(9 citation statements)

References 61 publications

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“…However, beta processes seem to play a distinct functional role as compared to the alpha processes, as their effect is more marked in the superficial than in the deeper cortical layers. These observations coincide with recent evidence that cognition is rhythmic 80, 81, 87 and that noise correlations play a key role in optimizing behavior to the ongoing time-varying cognitive demands 27 .…”

Section: Discussionsupporting

confidence: 89%

Interneuronal correlations dynamically adjust to task demands at multiple time-scales

Hassen¹,

Gaillard²,

Åstrand³

et al. 2019

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17Functional neuronal correlations between pairs of neurons are thought to play an important 18 role in neuronal information processing and optimal neuronal computations during attention, 19 perception, decision-making and learning. Here, we report dynamic changes in prefrontal 20 neuronal noise correlations at multiple time-scales, as a function of task contingencies. 21 Specifically, we record neuronal activity from the macaque frontal eye fields, a cortical region 22 at the source of spatial attention top-down control, while the animals are engaged in tasks of 23 varying cognitive demands. We show that the higher the task demand and cognitive 24 engagement the lower noise correlations. We further report that within a given task, noise 25 correlations significantly decrease in epoch of higher response probability. Last we show that 26 the power of the rhythmic modulations of noise correlations in the alpha and beta frequency 27 ranges also decreases in the most demanding tasks. All of these changes in noise correlations 28 are associated with layer specific modulations in spikes-LFP phase coupling, suggesting both 29 a long-range and a local intra-areal origin. Over all, this indicates a highly dynamic 30 adjustment of noise correlations to ongoing task requirements and suggests a strong functional 31 role of noise correlations in cognitive flexibility. 32 33 Significance statement 34 Cortical neurons are densely interconnected. As a result, pairs of neurons share some degree 35 of variability in their neuronal responses. This impacts how much information is present 36 within a neuronal population and is critical to attention, decision-making and learning. Here 37 we show that, in the prefrontal cortex, this shared inter-neuronal variability is highly flexible, 38 decreasing across tasks as cognitive demands increase and within trials in epochs of maximal 39 behavioral demand. It also fluctuates in time at a specific rhythm, the power of which 40 decreases for higher cognitive demand. All of these changes in noise correlations are 41 associated with layer specific modulations in spikes-LFP phase coupling. Over all, this 42 suggests a strong functional role of noise correlations in cognitive flexibility. 43 44 45 46 Optimal behavior is the result of interactions between neurons both within and across 47 brain areas. Identifying how these neuronal interactions flexibly adjust to the ongoing 48 behavioral demand is key to understand the neuronal processes and computations underlying 49 optimal behavior. Several studies have demonstrated that functional neuronal correlations 50 between pairs of neurons, otherwise known as noise correlations, play an important role in 51 perception and decision-making 1-9 . Specifically, several experimental and theoretical studies 52 show that noise correlations have an impact on the amount of information that can be decoded 53 for neuronal populations 4,10-12 as well as on overt behavioral performance 4,10-15 . As a result, 54 understanding how noise correlations dynamica...

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Section: Discussionsupporting

confidence: 89%

Interneuronal correlations dynamically adjust to task demands at multiple time-scales

Hassen¹,

Gaillard²,

Åstrand³

et al. 2019

Preprint

Self Cite

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“…Importantly, and in contrast with previous studies, behavioral and neuronal responses are analyzed either, 1) as a function of the physical configuration of the task, thus defining the task-based spatial priority map, or 2) as a function of the position of the AS from the FEF population activity, just prior to stimulus presentation, at an unprecedented spatial and temporal resolution, thus defining the dynamic spatial filtering of visual information by the AS. This approach is based on machine-learning decoding procedures applied to the PFC activity ( 13, 14 ), and allows to estimate the position of the AS in the visual workspace.…”

Section: Resultsmentioning

confidence: 99%

“…This attention-based spatial filtering is often equated with spatial prioritization by cue instruction. However, recent studies demonstrate that spatial attention is not stable and samples space rhythmically including following a spatial cue ( 14, 44–46 ). In the following, we demonstrate the existence of a dynamic prioritization of space by the attentional spotlight, independently of the task driven trial prioritization by cue instruction described in the previous section.…”

Section: Resultsmentioning

confidence: 99%

“…Task-relevant items can also be prioritized by a voluntary allocation of the attentional spotlight (AS - ( 8, 9 )). This voluntary allocation of attention, also referred to as covert or endogenous attention, is highly dynamic in time and space ( 10–14 ), and is distinct from learning-based spatial prioritization that operates on a larger time scale ( 15 ). How these two prioritization mechanisms interact in order to drive perception is unknown.…”

Section: Introductionmentioning

confidence: 99%

“…This cortical region is thought to act as a spatial priority map that integrates task goals and stimuli characteristics ( 28–31 ). This brain area also plays a key role in the voluntary ( 32, 33 ) and dynamic allocation of spatial attention ( 14, 34 ) towards the behaviorally relevant incoming visual information. Neural recordings were performed while monkeys were engaged in a forced choice cued visual detection task, in the presence of task-relevant and task-irrelevant visual stimuli.…”

Section: Introductionmentioning

confidence: 99%

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Selection and suppression of visual information in the macaque prefrontal cortex

Bello

Hassen

Åstrand

et al. 2020

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In everyday life, we are continuously struggling at focusing on our current goals while at the same time avoiding distractions. Attention is the neuro-cognitive process devoted to the selection of behaviorally relevant sensory information while at the same time preventing distraction by irrelevant information. Visual selection can be implemented by both long-term (learning-based spatial prioritization) and short term (dynamic spatial attention) mechanisms. On the other hand, distraction can be prevented proactively, by strategically prioritizing task-relevant information at the expense of irrelevant information, or reactively, by actively suppressing the processing of distractors. The distinctive neuronal signature of each of these four processes is largely unknown. Likewise, how selection and suppression mechanisms interact to drive perception has never been explored neither at the behavioral nor at the neuronal level. Here, we apply machine-learning decoding methods to prefrontal cortical (PFC) activity to monitor dynamic spatial attention with an unprecedented spatial and temporal resolution. This leads to several novel observations. We first identify independent behavioral and neuronal signatures for learning-based attention prioritization and dynamic attentional selection. Second, we identify distinct behavioral and neuronal signatures for proactive and reactive suppression mechanisms. We find that while distracting task-relevant information is suppressed proactively, task-irrelevant information is suppressed reactively. Critically, we show that distractor suppression, whether proactive or reactive, strongly depends on both learning-based attention prioritization and dynamic attentional selection. Overall, we thus provide a unified neuro-cognitive framework describing how the prefrontal cortex implements spatial selection and distractor suppression in order to flexibly optimize behavior in dynamic environments.

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Stimulus dependence of theta rhythmic activity in primate V1 and its potential relevance for visual perception

Fanyiwi¹,

Agayby²,

Kienitz³

et al. 2021

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A growing body of psychophysical research reports theta (3-8 Hz) rhythmic fluctuations in visual perception that are often attributed to an attentional sampling mechanism arising from theta rhythmic neural activity in mid- to high-level cortical association areas. However, it remains unclear to what extent such neuronal theta oscillations might already emerge at early sensory cortex like the primary visual cortex (V1), e.g. from the stimulus filter properties of neurons. To address this question, we recorded multi-unit neural activity from V1 of two macaque monkeys viewing a static visual stimulus with variable sizes, orientations and contrasts. We found that among the visually responsive electrode sites, more than 50 % showed a spectral peak at theta frequencies. Theta power varied with varying basic stimulus properties. Within each of these stimulus property domains (e.g. size), there was usually a single stimulus value that induced the strongest theta activity. In addition to these variations in theta power, the peak frequency of theta oscillations increased with increasing stimulus size and also changed depending on the stimulus position in the visual field. Further analysis confirmed that this neural theta rhythm was indeed stimulus-induced and did not arise from small fixational eye movements (microsaccades). When the monkeys performed a detection task of a target embedded in a theta-generating visual stimulus, reaction times also tended to fluctuate at the same theta frequency as the one observed in the neural activity. The present study shows that a highly stimulus-dependent neuronal theta oscillation can be elicited in V1 that appears to influence the temporal dynamics of visual perception.

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Prefrontal attentional saccades explore space rhythmically

Cited by 4 publications

References 61 publications

Interneuronal correlations dynamically adjust to task demands at multiple time-scales

Interneuronal correlations dynamically adjust to task demands at multiple time-scales

Selection and suppression of visual information in the macaque prefrontal cortex

Stimulus dependence of theta rhythmic activity in primate V1 and its potential relevance for visual perception

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