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Capturing the diversity of biological tuning curves using generative adversarial networks

https://doi.org/10.1101/167916
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Approximate Bayesian computational methods
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The Stabilized Supralinear Network: A Unifying Circuit Motif Underlying Multi-Input Integration in Sensory Cortex
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Dale’s principle
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Statistics of energy levels and eigenfunctions in disordered systems
inferredcites →https://doi.org/10.1017/9781108891530.013openalex-snapshot
Generative Adversarial Networks
inferredcites →https://doi.org/10.1038/nature04701openalex-snapshot
Weak pairwise correlations imply strongly correlated network states in a neural population
inferredcites →https://doi.org/10.1038/nature07140openalex-snapshot
Spatio-temporal correlations and visual signalling in a complete neuronal population
inferredcites →https://doi.org/10.1038/nature09880openalex-snapshot
Functional specificity of local synaptic connections in neocortical networks
inferredcites →https://doi.org/10.1038/nature12160openalex-snapshot
The importance of mixed selectivity in complex cognitive tasks
inferredcites →https://doi.org/10.1038/nn.3220openalex-snapshot
Slow dynamics and high variability in balanced cortical networks with clustered connections
inferredcites →https://doi.org/10.1073/pnas.1016051108openalex-snapshot
A synaptic organizing principle for cortical neuronal groups
inferredcites →https://doi.org/10.1101/094334openalex-snapshot
Stabilized supralinear network dynamics account for stimulus-induced changes of noise variability in the cortex
inferredcites →https://doi.org/10.1103/physreve.91.012820openalex-snapshot
Properties of networks with partially structured and partially random connectivity
inferredcites →https://doi.org/10.1126/science.1091277openalex-snapshot
Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
inferredcites →https://doi.org/10.1146/annurev-ecolsys-102209-144621openalex-snapshot
Approximate Bayesian Computation in Evolution and Ecology
inferredcites →https://doi.org/10.1162/neco_a_00472openalex-snapshot
Analysis of the Stabilized Supralinear Network
inferredcites →https://doi.org/10.1371/journal.pbio.0030068openalex-snapshot
Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits
inferredcites →https://doi.org/10.1371/journal.pcbi.1001078openalex-snapshot
Power-Law Input-Output Transfer Functions Explain the Contrast-Response and Tuning Properties of Neurons in Visual Cortex
inferredcites →https://doi.org/10.1371/journal.pcbi.1001120openalex-snapshot
The Morphology of the Rat Vibrissal Array: A Model for Quantifying Spatiotemporal Patterns of Whisker-Object Contact
inferredcites →https://doi.org/10.1371/journal.pcbi.1004083openalex-snapshot
Improved Estimation and Interpretation of Correlations in Neural Circuits
inferredcites →https://doi.org/10.1371/journal.pcbi.1004910openalex-snapshot
Tuning Curves for Arm Posture Control in Motor Cortex Are Consistent with Random Connectivity
inferredcites →https://doi.org/10.1523/jneurosci.02-11-01527.1982openalex-snapshot
On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex
inferredcites →https://doi.org/10.1523/jneurosci.1282-06.2006openalex-snapshot
The Structure of Multi-Neuron Firing Patterns in Primate Retina
inferredcites →https://doi.org/10.1523/jneurosci.1677-11.2011openalex-snapshot
On the Distribution of Firing Rates in Networks of Cortical Neurons
inferredcites →https://doi.org/10.1523/jneurosci.22-13-05639.2002openalex-snapshot
Orientation Selectivity in Macaque V1: Diversity and Laminar Dependence
inferredcites →https://doi.org/10.1523/jneurosci.2753-12.2013openalex-snapshot
The Sparseness of Mixed Selectivity Neurons Controls the Generalization–Discrimination Trade-Off
inferredcites →https://doi.org/10.1523/jneurosci.3359-07.2008openalex-snapshot
A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro
inferredcites →https://doi.org/10.1523/jneurosci.5187-08.2009openalex-snapshot
The Structure of Large-Scale Synchronized Firing in Primate Retina
inferredcites →https://doi.org/10.48550/arxiv.1211.5063openalex-snapshot
On the difficulty of training Recurrent Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1511.06434openalex-snapshot
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
inferredcites →https://doi.org/10.48550/arxiv.1606.05908openalex-snapshot
Tutorial on Variational Autoencoders
inferredcites →https://doi.org/10.48550/arxiv.1701.00160openalex-snapshot
NIPS 2016 Tutorial: Generative Adversarial Networks
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Improved Training of Wasserstein GANs
inferredcites →https://doi.org/10.48550/arxiv.1706.00550openalex-snapshot
On Unifying Deep Generative Models
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Gradient descent GAN optimization is locally stable
inferredcites →https://doi.org/10.48550/arxiv.1706.04987openalex-snapshot
Variational Approaches for Auto-Encoding Generative Adversarial Networks
inferredcites →https://doi.org/10.48550/arxiv.1706.08224openalex-snapshot
Do GANs actually learn the distribution? An empirical study

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Augmenting Data from Epileptic Brain Seizures Using Deep Generative Networks
inferred← citeshttps://doi.org/10.1016/j.celrep.2020.03.047openalex-snapshot
Network Analysis of Murine Cortical Dynamics Implicates Untuned Neurons in Visual Stimulus Coding
inferred← citeshttps://doi.org/10.1016/j.pneurobio.2019.01.008openalex-snapshot
The roles of supervised machine learning in systems neuroscience
inferred← citeshttps://doi.org/10.1186/s12868-023-00792-6openalex-snapshot
A deep generative adversarial network capturing complex spiral waves in disinhibited circuits of the cerebral cortex
inferred← citeshttps://doi.org/10.4018/978-1-7998-1581-5.ch005openalex-snapshot
Generative Adversarial Neural Networking of Agents
inferred← citeshttps://doi.org/10.4018/ijarb.2019070106openalex-snapshot
About Neural Networking of Avatar-Based Modeling as an Intellectual Tool and Technology for Biomedical Study in Bioinformatics
inferred← citeshttps://doi.org/10.48550/arxiv.1803.00338openalex-snapshot
Synthesizing realistic neural population activity patterns using Generative Adversarial Networks
inferred← citeshttps://doi.org/10.7554/elife.26724openalex-snapshot
Beyond excitation/inhibition imbalance in multidimensional models of neural circuit changes in brain disorders
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