Persistent activity and the single-cell frequency–current curve in a cortical network model

2000 ◽  
Vol 11 (4) ◽  
pp. 261-280 ◽  
Author(s):  
Nicolas Brunel
Keyword(s):  
Single Cell ◽  
Network Model ◽  
Cortical Network ◽  
Persistent Activity ◽  
Cell Frequency ◽  
Current Curve ◽  
Frequency Current

scholarly journals Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1

2011 ◽  
Vol 105 (2) ◽  
pp. 757-778 ◽  
Author(s):  
Malte J. Rasch ◽  
Klaus Schuch ◽  
Nikos K. Logothetis ◽  
Wolfgang Maass
Keyword(s):  
Network Model ◽  
Network Models ◽  
Cortical Network ◽  
Inhibitory Neurons ◽  
Sensory Stimuli ◽  
Natural Stimuli ◽  
Cortical Areas ◽  
Model Response ◽  
Spiking Activity

A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N -methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network models.

scholarly journals Understanding the relationships between spike rate and delta/gamma frequency bands of LFPs and EEGs using a local cortical network model

NeuroImage ◽  
2010 ◽  
Vol 52 (3) ◽  
pp. 956-972 ◽  
Author(s):  
Alberto Mazzoni ◽  
Kevin Whittingstall ◽  
Nicolas Brunel ◽  
Nikos K Logothetis ◽  
Stefano Panzeri
Keyword(s):  
Network Model ◽  
Spike Rate ◽  
Cortical Network ◽  
Frequency Bands ◽  
Gamma Frequency

scholarly journals Inter-synaptic learning of combination rules in a cortical network model

2014 ◽  
Vol 5 ◽  
Author(s):  
Frédéric Lavigne ◽  
Francis Avnaïm ◽  
Laurent Dumercy
Keyword(s):  
Network Model ◽  
Cortical Network ◽  
Combination Rules ◽  
Synaptic Learning

Are single-cell data sufficient for testing neural network models?

1995 ◽  
Vol 18 (4) ◽  
pp. 626-627 ◽  
Author(s):  
Ehud Ahissar
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Single Unit ◽  
Network Models ◽  
Persistent Activity ◽  
Neural Network Models ◽  
Cell Data

AbstractPersistent activity can be the product of mechanisms other than attractor reverberations. The single-unit data presented by Amit cannot discriminate between the different mechanisms. In fact, single-unit data do not appear to be adequate for testing neural network models.

scholarly journals Continuous visualization of differences between biological conditions in single-cell data

2018 ◽  
Author(s):  
Tyler J. Burns ◽  
Garry P. Nolan ◽  
Nikolay Samusik
Keyword(s):  
Single Cell ◽  
Nearest Neighbor ◽  
Developmental Trajectory ◽  
Functional Markers ◽  
Mass Cytometry ◽  
K Nearest Neighbor ◽  
Cell Frequency ◽  
Low Dimensional ◽  
Marker Shift ◽  
Cell Data

In high-dimensional single cell data, comparing changes in functional markers between conditions is typically done across manual or algorithm-derived partitions based on population-defining markers. Visualizations of these partitions is commonly done on low-dimensional embeddings (eg. t-SNE), colored by per-partition changes. Here, we provide an analysis and visualization tool that performs these comparisons across overlapping k-nearest neighbor (KNN) groupings. This allows one to color low-dimensional embeddings by marker changes without hard boundaries imposed by partitioning. We devised an objective optimization of k based on minimizing functional marker KNN imputation error. Proof-of-concept work visualized the exact location of an IL-7 responsive subset in a B cell developmental trajectory on a t-SNE map independent of clustering. Per-condition cell frequency analysis revealed that KNN is sensitive to detecting artifacts due to marker shift, and therefore can also be valuable in a quality control pipeline. Overall, we found that KNN groupings lead to useful multiple condition visualizations and efficiently extract a large amount of information from mass cytometry data. Our software is publicly available through the Bioconductor package Sconify.

scholarly journals Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders

2019 ◽  
Author(s):  
Soeren Lukassen ◽  
Foo Wei Ten ◽  
Roland Eils ◽  
Christian Conrad
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Network Model ◽  
Neural Network Model ◽  
Matrix Factorization ◽  
Latent Variable ◽  
Single Cells ◽  
Sequencing Data ◽  
Gene Set ◽  
Gene Sets

AbstractRecent advances in single-cell RNA sequencing (scRNA-Seq) have driven the simultaneous measurement of the expression of 1,000s of genes in 1,000s of single cells. These growing data sets allow us to model gene sets in biological networks at an unprecedented level of detail, in spite of heterogenous cell populations. Here, we propose an unsupervised deep neural network model that is a hybrid of matrix factorization and conditional variational autoencoders (CVA), which utilizes weights as matrix factorizations to obtain gene sets, while class-specific inputs to the latent variable space facilitate a plausible identification of cell types. This artificial neural network model seamlessly integrates functional gene set inference, experimental batch effect correction, and static gene identification, which we conceptually prove here for three single-cell RNA-Seq datasets and suggest for future single-cell-gene analytics.

scholarly journals Estimating Single-Cell Lag Times via a Bayesian Scheme

2008 ◽  
Vol 74 (22) ◽  
pp. 7098-7099 ◽  
Author(s):  
P. K. Malakar ◽  
G. C. Barker
Keyword(s):  
Bayesian Network ◽  
Single Cell ◽  
Network Model ◽  
Network Models ◽  
Optical Methods ◽  
Computationally Efficient ◽  
Bayesian Network Model ◽  
Lag Times ◽  
Lag Phases

ABSTRACT Network models offer computationally efficient tools for estimating the variability of single-cell lag phases. Currently, optical methods for estimating the variability of single-cell lag phases use single-cell inocula and are technically challenging. A Bayesian network model incorporating small uncertain inocula addresses these limitations.

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