High-order coordination of cortical spiking activity modulates perceptual accuracy

Shahidi, Neda; Andrei, Ariana R.; Hu, Ming; Dragoi, Valentin

doi:10.1038/s41593-019-0406-3

Cited by 44 publications

(50 citation statements)

References 52 publications

(78 reference statements)

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“…Studies have found that these fluctuations correlate with behavior, including pupil diameter [70,103,63,42,24,59,74,100], locomotion [27,81], attention [77,96,66,38], task difficulty [86] and learning [13,71]. In addition, these fluctuations may have a computational purpose, such as changing the structure of noise correlations to improve the fidelity of stimulus encoding [18,65,8,33], passing information from one brain area to another [85,87,88,95,89,93], or priming neurons to certain features of the stimulus [72,9].…”

Section: Discussionmentioning

confidence: 99%

Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

Cowley

Snyder

Acar

et al. 2020

Preprint

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An animal's decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This internal state is a product of cognitive factors, such as fatigue, motivation, and arousal, but it is unclear how these factors influence the neural processes that encode the sensory stimulus and form a decision. We discovered that, over the timescale of tens of minutes during a perceptual decision-making task, animals slowly shifted their likelihood of reporting stimulus changes. They did this unprompted by task conditions. We recorded neural population activity from visual area V4 as well as prefrontal cortex, and found that the activity of both areas slowly drifted together with the behavioral fluctuations. We reasoned that such slow fluctuations in behavior could either be due to slow changes in how the sensory stimulus is processed or due to a process that acts independently of sensory processing. By analyzing the recorded activity in conjunction with models of perceptual decision-making, we found evidence for the slow drift in neural activity acting as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in the population activity across multiple brain areas, hidden from typical trial-averaged analyses and revealed only when considering the passage of time within each experimental session. Knowledge of this cognitive factor was critical in elucidating how sensory signals and the internal state together contribute to the decision-making process.

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Section: Discussionmentioning

confidence: 99%

Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

Cowley

Snyder

Acar

et al. 2020

Preprint

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“…390 The necessity of higher-order patterns for stable activity has strong implications for 391 neural coding. Previous work has already demonstrated that correlations enhance 392 coding, with triplet correlations having an advantage over pairwise 393 [5,16,18,[21][22][23][52][53][54]. The neural code must rest upon a foundation of stable 394 propagation of spikes, which we have shown in turn rests on higher-order motifs and 395 coordinated integration.…”

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confidence: 77%

“…The term 'motif' refers to a pattern formed by a group of units in a network. Previously 170 we found that triplet motifs were informative of synaptic integration [19] and also 171 increased the power of encoding models [18,[21][22][23]. Here we focused our analysis on 172 similar patterns of connectivity in the recruitment network, involving groups of three 173 units [43].…”

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confidence: 98%

Cyclic transitions between higher order motifs underlie sustained activity in asynchronous sparse recurrent networks

Bojanek

Zhu

MacLean

2019

Preprint

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Many studies have demonstrated the prominence of higher-order patterns in excitatory synaptic connectivity as well as activity in neocortex. Surveyed as a whole, these results suggest that there may be an essential role for higher-order patterns in neocortical function. In order to stably propagate signal within and between regions of neocortex, the most basic -yet nontrivial -function which neocortical circuitry must satisfy is the ability to maintain stable spiking activity over time. Here we algorithmically construct spiking neural network models comprised of 5000 neurons using topological statistics from neocortex and a set of objective functions that identify networks which produce naturalistic low-rate, asynchronous, and critical activity. We find that the same network topology can exhibit either sustained activity under one set of initial membrane voltages or truncated activity under a different set. Yet these two outcomes are not readily differentiated by rate or criticality. By summarizing the statistical dependencies in the pairwise activity of neurons as directed weighted functional networks, we examined the transient manifestations of higher-order motifs in the functional networks across time. We find that stereotyped low variance cyclic transitions between three isomorphic triangle motifs, quantified as a Markov process, are required for sustained activity. If the network fails to engage the dynamical regime characterized by a recurring stable pattern of motif dominance, spiking activity ceased. Motif cycling generalized across manipulations of synaptic weights and across topologies, demonstrating the robustness of this dynamical regime for sustained spiking in critical asynchronous network activity. Our results point to the necessity of higher-order patterns amongst excitatory connections for sustaining activity in sparse recurrent networks. They also provide a possible explanation as to why such excitatory synaptic connectivity and activity patterns have been prominently reported in neocortex. Author summaryHere we address two questions. First, it remains unclear how activity propagates stably through a network since neurons are leaky and connectivity is sparse and weak. Second, higher order patterns abound in neocortex, hinting at potential functional relevance for their presence. Several lines of evidence suggest that higher-order network interactions September 16, 2019 1/23 may be instrumental for spike propagation. For example, excitatory synaptic connectivity shows a prevalence of local neuronal cliques and patterns, and propagating activity in vivo displays elevated clustering dominated by specific triplet motifs. In this study we demonstrate a mechanistic link between activity propagation and higher-order motifs at the level of individual neurons and across networks. We algorithmically build spiking neural network (SNN) models to mirror the topological and dynamical statistics of neocortex. Using a combination of graph theory, information theory, and probabilistic tools, we show that higher ...

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“…Unlike attention-related changes in pre-target spike rates, the present results demonstrate that local and between-region patterns of pre-target spike-LFP phase coupling are predictive of behavioral outcomes. Spike-LFP phase coupling is an indication of spike-timing control and represents a possible mechanism for temporally organizing neural activity to optimize (i) local computations and (ii) the transfer of information between brain regions Lisman and Idiart, 1995;Shahidi et al, 2019;Siegel et al, 2009;Voloh et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Spike timing in the attention network predicts behavioral outcome prior to target selection

Fiebelkorn

Kästner

2020

Preprint

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There has been little evidence linking changes in spiking activity that occur prior to a spatially predictable target (i.e., prior to target selection) to behavioral outcomes, despite such preparatory changes being widely assumed to enhance the sensitivity of sensory processing. We simultaneously recorded from frontal and parietal nodes of the attention network, while macaques performed a spatial-cueing task. When anticipating a spatially predictable target, different patterns of coupling between spike timing and oscillatory phase in local field potentials-but not changes in spike rate-were predictive of different behavioral outcomes. These behaviorally relevant differences in local and between-region synchronization occurred among specific cell types that were defined based on their sensory and motor properties, providing insight into the mechanisms underlying enhanced sensory processing prior to target selection. We propose that these changes in neural synchronization reflect differential, anticipatory engagement of the network nodes and functional units that shape attention-related sampling.

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High-order coordination of cortical spiking activity modulates perceptual accuracy

Cited by 44 publications

References 52 publications

Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

Cyclic transitions between higher order motifs underlie sustained activity in asynchronous sparse recurrent networks

Spike timing in the attention network predicts behavioral outcome prior to target selection

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