High-order interactions explain the collective behavior of cortical populations in executive but not sensory areas

Chelaru, Mircea I.; Eagleman, Sarah; Andrei, Ariana R.; Milton, Russell; Kharas, Natasha; Dragoi, Valentin

doi:10.1016/j.neuron.2021.09.042

Cited by 8 publications

(4 citation statements)

References 30 publications

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“…We also performed this evaluation according to different measures of performance. Like many previous studies [6,7,[23][24][25][26][27], we find that the pairwise model exhibits excellent performance for small N.…”

Section: Discussionsupporting

confidence: 86%

“…According to this measure, the PME model is found to be almost perfect for small populations (N * 10) of retinal neurons [6,7,23]. Similarly promising results have been found in the cortex [24][25][26][27], but still for small populations. Studies on larger populations used other performance measures or modified versions of the model [8,23,[28][29][30][31].…”

Section: Introductionmentioning

confidence: 55%

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The quality and complexity of pairwise maximum entropy models for large cortical populations

Olsen,

Whitlock,

Roudi

2024

PLoS Comput Biol

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We investigate the ability of the pairwise maximum entropy (PME) model to describe the spiking activity of large populations of neurons recorded from the visual, auditory, motor, and somatosensory cortices. To quantify this performance, we use (1) Kullback-Leibler (KL) divergences, (2) the extent to which the pairwise model predicts third-order correlations, and (3) its ability to predict the probability that multiple neurons are simultaneously active. We compare these with the performance of a model with independent neurons and study the relationship between the different performance measures, while varying the population size, mean firing rate of the chosen population, and the bin size used for binarizing the data. We confirm the previously reported excellent performance of the PME model for small population sizes N < 20. But we also find that larger mean firing rates and bin sizes generally decreases performance. The performance for larger populations were generally not as good. For large populations, pairwise models may be good in terms of predicting third-order correlations and the probability of multiple neurons being active, but still significantly worse than small populations in terms of their improvement over the independent model in KL-divergence. We show that these results are independent of the cortical area and of whether approximate methods or Boltzmann learning are used for inferring the pairwise couplings. We compared the scaling of the inferred couplings with N and find it to be well explained by the Sherrington-Kirkpatrick (SK) model, whose strong coupling regime shows a complex phase with many metastable states. We find that, up to the maximum population size studied here, the fitted PME model remains outside its complex phase. However, the standard deviation of the couplings compared to their mean increases, and the model gets closer to the boundary of the complex phase as the population size grows.

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Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 55%

The quality and complexity of pairwise maximum entropy models for large cortical populations

Olsen,

Whitlock,

Roudi

2024

PLoS Comput Biol

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“…While graphs model pairwise interactions, hypergraphs generalize this concept by capturing higher-order interactions involving more than two elements. This extension provides a more expressive framework for modeling intricate dependencies and interactions in various fields, ranging from social network analysis (early acknowledged in Simmel, 1950) or co-authorship relations (Roy and Ravindran, 2015) to ecological systems (Muyinda et al, 2020), neurosciences (Chelaru et al, 2021) or even chemistry (Flamm et al, 2015). We refer to Battiston et al (2020); Bick et al (2023); Torres et al (2021) for recent reviews on higher-order interactions.…”

Section: Introductionmentioning

confidence: 99%

A theoretical and empirical evaluation of modularities for hypergraphs

Cazabet

2024

PCI Network Science

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Statistical analysis and node clustering in hypergraphs constitute an emerging topic suffering from a lack of standardization. In contrast to the case of graphs, the concept of nodes' community in hypergraphs is not unique and encompasses various distinct situations. In this work, we conducted a comparative analysis of the performance of modularity-based methods for clustering nodes in binary hypergraphs.To address this, we begin by presenting, within a unified framework, the various hypergraph modularity criteria proposed in the literature, emphasizing their differences and respective focuses. Subsequently, we provide an overview of the state-of-the-art codes available to maximize hypergraph modularities for detecting node communities in hypergraphs. Through exploration of various simulation settings with controlled ground truth clustering, we offer a comparison of these methods using different quality measures, including true clustering recovery, running time, (local) maximization of the objective, and the number of clusters detected.Our contribution marks the first attempt to clarify the advantages and drawbacks of these newly available methods. This effort lays the foundation for a better understanding of the primary objectives of modularity-based node clustering methods for binary hypergraphs.

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“…This framework, in turn, could be used as a tool to infer the hidden architecture from population statistics of observed postsynaptic neurons. Here we aim to gain insight into the underlying architecture from higher-order neuronal interactions, i.e., interactions among three or more neurons, because occurrence of significant higher-order interactions have been found ubiquitously in vitro 39 – 41 and in vivo 42 – 45 , and predicted by theoretical studies 46 – 50 . Furthermore, it has been reported that the higher-order interactions encode stimulus information 43 , 51 (see also refs.…”

Section: Introductionmentioning

confidence: 99%

Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons

et al. 2023

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Identifying network architecture from observed neural activities is crucial in neuroscience studies. A key requirement is knowledge of the statistical input-output relation of single neurons in vivo. By utilizing an exact analytical solution of the spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near the threshold, we construct a framework that links synaptic type, strength, and spiking nonlinearity with the statistics of neuronal population activity. The framework explains structured pairwise and higher-order interactions of neurons receiving common inputs under different architectures. We compared the theoretical predictions with the activity of monkey and mouse V1 neurons and found that excitatory inputs given to pairs explained the observed sparse activity characterized by strong negative triple-wise interactions, thereby ruling out the alternative explanation by shared inhibition. Moreover, we showed that the strong interactions are a signature of excitatory rather than inhibitory inputs whenever the spontaneous rate is low. We present a guide map of neural interactions that help researchers to specify the hidden neuronal motifs underlying observed interactions found in empirical data.

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High-order interactions explain the collective behavior of cortical populations in executive but not sensory areas

Cited by 8 publications

References 30 publications

The quality and complexity of pairwise maximum entropy models for large cortical populations

The quality and complexity of pairwise maximum entropy models for large cortical populations

A theoretical and empirical evaluation of modularities for hypergraphs

Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons

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