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

10.7287/peerj.preprints.27340v1 ◽

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.

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

10.7287/peerj.preprints.27340 ◽

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.

Download Full-text

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

10.7287/peerj.preprints.27340v2 ◽

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.

Download Full-text

Model Selection and Hypothesis Testing for Large-Scale Network Models with Overlapping Groups

Physical Review X ◽

10.1103/physrevx.5.011033 ◽

2015 ◽

Vol 5 (1) ◽

Cited By ~ 26

Author(s):

Tiago P. Peixoto

Keyword(s):

Model Selection ◽

Hypothesis Testing ◽

Large Scale ◽

Network Models ◽

Large Scale Network ◽

Scale Network

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The SONATA Data Format for Efficient Description of Large-Scale Network Models

10.1101/625491 ◽

2019 ◽

Cited By ~ 2

Author(s):

Kael Dai ◽

Juan Hernando ◽

Yazan N. Billeh ◽

Sergey L. Gratiy ◽

Judit Planas ◽

...

Keyword(s):

Network Architecture ◽

High Performance ◽

Large Scale ◽

Computational Models ◽

Model Building ◽

Network Models ◽

Data Format ◽

Large Scale Network ◽

Scale Network ◽

Biological Realism

AbstractIncreasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.

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Local axonal conduction delays underlie precise timing of a neural sequence

10.1101/864231 ◽

2019 ◽

Cited By ~ 1

Author(s):

Robert Egger ◽

Yevhen Tupikov ◽

Kalman A. Katlowitz ◽

Sam E. Benezra ◽

Michel A. Picardo ◽

...

Keyword(s):

Cognitive Processes ◽

Large Scale ◽

Critical Role ◽

Network Models ◽

Axonal Conduction ◽

Large Scale Network ◽

Rat Neocortex ◽

Scale Network ◽

Sequential Activation ◽

Local Circuits

SUMMARYSequential activation of neurons has been observed during various behavioral and cognitive processes and is thought to play a critical role in their generation. Here, we studied a circuit in the songbird forebrain that drives the performance of adult courtship song. In this region, known as HVC, neurons are sequentially active with millisecond precision in relation to behavior. Using large-scale network models, we found that HVC sequences could only be accurately produced if sequentially active neurons were linked with long and heterogeneous axonal conduction delays. Although such latencies are often thought to be negligible in local microcircuits, we empirically determined that HVC interconnections were surprisingly slow, generating delays up to 22 ms. An analysis of anatomical reconstructions suggests that similar processes may also occur in rat neocortex, supporting the notion that axonal conduction delays can sculpt the dynamical repertoire of a range of local circuits.

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