scholarly journals Inhibition-Dominated Rich-Club Shapes Dynamics in Cortical Microcircuits of Awake Behaving Mice

2021 ◽  
Author(s):  
Hadi Hafizi ◽  
Sunny Nigam ◽  
Josh Barnathan ◽  
Ian Stevenson ◽  
Sotiris C Masmanidis ◽  
...  
Keyword(s):  
Cortical Neurons ◽  
Large Scale ◽  
Population Level ◽  
Inhibitory Neurons ◽  
Population Activity ◽  
Rich Club ◽  
Large Scale Network ◽  
Type Composition ◽  
Scale Network ◽  
The Rich

Functional networks of cortical neurons contain highly interconnected hubs, forming a rich-club structure. However, the cell type composition within this distinct subnetwork and how it influences large-scale network dynamics is unclear. Using spontaneous activity recorded from hundreds of cortical neurons in orbitofrontal cortex of awake behaving mice we show that the rich-club is disproportionately composed of inhibitory neurons, and that inhibitory neurons within the rich-club are significantly more synchronous than other neurons. At the population level, Granger causality showed that neurons in the rich-club are the dominant drivers of overall population activity and do so in a frequency-specific manner. Moreover, early activity ofinhibitory neurons, along with excitatory neurons within the rich-club, synergistically predicts the duration of neuronal cascades. Together, these results reveal an unexpected role of a highly connected core of inhibitory neurons in driving and sustaining activity in local cortical networks.

scholarly journals Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction

2018 ◽  
Author(s):  
Ryan C Williamson ◽  
Brent Doiron ◽  
Matt A Smith ◽  
Byron M Yu
Keyword(s):  
Dimensionality Reduction ◽  
Large Scale ◽  
Common Ground ◽  
Network Models ◽  
Neuronal Population ◽  
Dimensional Structure ◽  
Neural Network Modeling ◽  
Population Activity ◽  
Large Scale Network ◽  
Scale Network

A long-standing goal in neuroscience has been to bring together neuronal recordings and neural network modeling to understand brain function. Neuronal recordings can inform the development of network models, and network models can in turn provide predictions for subsequent experiments. Traditionally, neuronal recordings and network models have been related using single-neuron and pairwise spike train statistics. We review here recent studies that have begun to relate neuronal recordings and network models based on the multi-dimensional structure of neuronal population activity, as identified using dimensionality reduction. This approach has been used to study working memory, decision making, motor control, and more. Dimensionality reduction has provided common ground for incisive comparisons and tight interplay between neuronal recordings and network models.

scholarly journals Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction

2019 ◽  
Author(s):  
Ryan C Williamson ◽  
Brent Doiron ◽  
Matt A Smith ◽  
Byron M Yu
Keyword(s):  
Dimensionality Reduction ◽  
Large Scale ◽  
Common Ground ◽  
Network Models ◽  
Neuronal Population ◽  
Dimensional Structure ◽  
Neural Network Modeling ◽  
Population Activity ◽  
Large Scale Network ◽  
Scale Network

A long-standing goal in neuroscience has been to bring together neuronal recordings and neural network modeling to understand brain function. Neuronal recordings can inform the development of network models, and network models can in turn provide predictions for subsequent experiments. Traditionally, neuronal recordings and network models have been related using single-neuron and pairwise spike train statistics. We review here recent studies that have begun to relate neuronal recordings and network models based on the multi-dimensional structure of neuronal population activity, as identified using dimensionality reduction. This approach has been used to study working memory, decision making, motor control, and more. Dimensionality reduction has provided common ground for incisive comparisons and tight interplay between neuronal recordings and network models.

scholarly journals Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction

2019 ◽  
Author(s):  
Ryan C Williamson ◽  
Brent Doiron ◽  
Matt A Smith ◽  
Byron M Yu
Keyword(s):  
Dimensionality Reduction ◽  
Large Scale ◽  
Common Ground ◽  
Network Models ◽  
Neuronal Population ◽  
Dimensional Structure ◽  
Neural Network Modeling ◽  
Population Activity ◽  
Large Scale Network ◽  
Scale Network

A long-standing goal in neuroscience has been to bring together neuronal recordings and neural network modeling to understand brain function. Neuronal recordings can inform the development of network models, and network models can in turn provide predictions for subsequent experiments. Traditionally, neuronal recordings and network models have been related using single-neuron and pairwise spike train statistics. We review here recent studies that have begun to relate neuronal recordings and network models based on the multi-dimensional structure of neuronal population activity, as identified using dimensionality reduction. This approach has been used to study working memory, decision making, motor control, and more. Dimensionality reduction has provided common ground for incisive comparisons and tight interplay between neuronal recordings and network models.

Large-Scale Network Analysis for Online Social Brand Advertising

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
◽  
Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
◽  
...  
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Large Scale Network ◽  
Brand Advertising ◽  
Scale Network

Attention Biases Visual Activity in Visual Short-term Memory

2014 ◽  
Vol 26 (7) ◽  
pp. 1377-1389 ◽  
Author(s):  
Bo-Cheng Kuo ◽  
Mark G. Stokes ◽  
Alexandra M. Murray ◽  
Anna Christina Nobre
Keyword(s):  
Large Scale ◽  
Short Term Memory ◽  
Control Mechanisms ◽  
Top Down ◽  
Short Term ◽  
Visual Activity ◽  
Visual Short Term Memory ◽  
Large Scale Network ◽  
Visual Areas ◽  
Scale Network

In the current study, we tested whether representations in visual STM (VSTM) can be biased via top–down attentional modulation of visual activity in retinotopically specific locations. We manipulated attention using retrospective cues presented during the retention interval of a VSTM task. Retrospective cues triggered activity in a large-scale network implicated in attentional control and led to retinotopically specific modulation of activity in early visual areas V1–V4. Importantly, shifts of attention during VSTM maintenance were associated with changes in functional connectivity between pFC and retinotopic regions within V4. Our findings provide new insights into top–down control mechanisms that modulate VSTM representations for flexible and goal-directed maintenance of the most relevant memoranda.

Sign in / Sign up

Export Citation Format

Share Document