Inhibitory Units: An Organizing Nidus for Feature-Selective Sub-Networks in Area V1

Palagina, Ganna; Meyer, J.; Smirnakis, Stelios M.

doi:10.1101/282988

Cited by 3 publications

(2 citation statements)

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“…10. Namely, for W = 3 all values which are smaller than 1 6 are turned into zero. Values in space 1 6 , 1 2 are 10 turned into 1 3 and values in space 1 2 , 5 6 are turned into…”

Section: Parameter Setupmentioning

confidence: 99%

“…By using 2-photon imaging, we monitored the spontaneous population bursts of activity in pyramidal cells and interneurons of mouse in L2/3 V1. We found that the sizes of spontaneous population bursts and the degree of connectivity of the neurons in specific fields of view (FOVs) formed scale-free distributions, suggestive of a hierarchical small-world net architecture [1]. The existence of such groups of "linked" units inevitably shapes the profile of spontaneous events $ Fully documented templates are available in the elsarticle package on CTAN. observed in V1 networks [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

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Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity

et al. 2020

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The field of neuroscience is experiencing rapid growth in the complexity and quantity of the recorded neural activity allowing us unprecedented access to its dynamics in different brain areas. The objective of this work is to discover directly from the experimental data rich and comprehensible models for brain function that will be concurrently robust to noise. Considering this task from the perspective of dimensionality reduction, we develop an innovative, robust to noise dictionary learning framework based on adversarial training methods for the identification of patterns of synchronous firing activity as well as within a time lag. We employ real-world binary datasets describing the spontaneous neuronal activity of laboratory mice over time, and we aim to their efficient low-dimensional representation. The results on the classification accuracy for the discrimination between the clean and the adversarial-noisy activation patterns obtained by an SVM classifier highlight the efficacy of the proposed scheme compared to other methods, and the visualization of the dictionary's distribution demonstrates the multifarious information that we obtain from it.

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“…10. Namely, for W = 3 all values which are smaller than 1 6 are turned into zero. Values in space 1 6 , 1 2 are 10 turned into 1 3 and values in space 1 2 , 5 6 are turned into…”

Section: Parameter Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity

et al. 2020

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Brain orchestra under spontaneous conditions: Identifying communication modules from the functional architecture of area V1

Papadopouli,

Smyrnakis,

Koniotakis

et al. 2024

Preprint

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We used two-photon imaging to record from granular and supragranular layers in mouse primary visual cortex (V1) under spontaneous conditions and applied an extension of the spike time tiling coefficient (STTC; introduced by Cutts and Eglen) to map functional connectivity architecture within and across layers. We made several observations: Approximately, 19-34% of neuronal pairs within 300µm of each other exhibit statistically significant functional connections, compared to 10% at distances of 1mm or more. As expected, neuronal pairs with similar tuning functions exhibit a significant, though relatively small, increase in the fraction of functional inter-neuronal correlations. In contrast, internal state as reflected by pupillary diameter or aggregate neuronal activity appears to play a much stronger role in determining inter-neuronal correlation distributions and topography. Overall, inter-neuronal correlations appear to be slightly more prominent in layer 4. The first-order functionally connected (i.e., direct) neighbors of neurons determine the hub structure of the V1 microcircuit. Layer 4 exhibits a nearly flat degree of connectivity distribution, extending to higher values than seen in supragranular layers, whose distribution drops exponentially. In all layers, functional connectivity exhibits small-world characteristics and network robustness. The probability of firing of layer 2/3 pyramidal neurons can be predicted as a function of the aggregate activity in their first-order functionally connected partners within layer 4, which represent their putative input group. The functional form of this prediction conforms well to a ReLU function, reaching up to firing probability one in some neurons. Interestingly, the properties of layer 2/3 pyramidal neurons differ based on the size of their layer 4 functional connectivity group. Specifically, layer 2/3 neurons with small layer-4 degrees of connectivity appear to be more sensitive to the firing of their layer 4 functional connectivity partners, suggesting they may be more effective at transmitting synchronous activity downstream from layer 4. They also appear to fire largely independently from each other, compared to neurons with high layer-4 degrees of connectivity, and are less modulated by changes in pupil size and aggregate population dynamics. Information transmission is best viewed as occurring from neuronal ensembles in layer 4 to neuronal ensembles in layer 2/3. Under spontaneous conditions, we were able to identify such candidate neuronal ensembles, which exhibit high sensitivity, precision, and specificity for L4 to L2/3 information transmission. In sum, functional connectivity analysis under spontaneous activity conditions reveals a modular neuronal ensemble architecture within and across granular and supragranular layers of mouse primary visual cortex. Furthermore, modules with different degrees of connectivity appear to obey different rules of engagement and communication across the V1 columnar circuit.

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Detection of Stimuli Changes in Neural Eventograms Using the Line of Synchronization of Global Recurrence Plots

Tzagkarakis

Palagina

Smirnakis

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Inhibitory Units: An Organizing Nidus for Feature-Selective Sub-Networks in Area V1

Abstract: SUMMARYSensory stimuli are encoded by the joint firing of neuronal groups composed of pyramidal cells and interneurons,

Cited by 3 publications

References 51 publications

Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity

Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity

Brain orchestra under spontaneous conditions: Identifying communication modules from the functional architecture of area V1

Detection of Stimuli Changes in Neural Eventograms Using the Line of Synchronization of Global Recurrence Plots

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