scholarly journals Uncertainpy: A Python toolbox for uncertainty quantification and sensitivity analysis in computational neuroscience

2018 ◽  
Author(s):  
Simen Tennøe ◽  
Geir Halnes ◽  
Gaute T. Einevoll
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Action Potential ◽  
Uncertainty Quantification ◽  
Open Source ◽  
Computational Neuroscience ◽  
Network Models ◽  
Model Parameters ◽  
Model Output ◽  
Neural Network Models

AbstractComputational models in neuroscience typically contain many parameters that are poorly constrained by experimental data. Uncertainty quantification and sensitivity analysis provide rigorous procedures to quantify how the model output depends on this parameter uncertainty. Unfortunately, the application of such methods is not yet standard within the field of neuroscience.Here we present Uncertainpy, an open-source Python toolbox, tailored to perform uncertainty quantification and sensitivity analysis of neuroscience models. Uncertainpy aims to make it easy and quick to get started with uncertainty analysis, without any need for detailed prior knowledge. The toolbox allows uncertainty quantification and sensitivity analysis to be performed on already existing models without needing to modify the model equations or model implementation. Uncertainpy bases its analysis on polynomial chaos expansions, which are more efficient than the more standard Monte-Carlo based approaches.Uncertainpy is tailored for neuroscience applications by its built-in capability for calculating characteristic features in the model output. The toolbox does not merely perform a point-to- point comparison of the “raw” model output (e.g. membrane voltage traces), but can also calculate the uncertainty and sensitivity of salient model response features such as spike timing, action potential width, mean interspike interval, and other features relevant for various neural and neural network models. Uncertainpy comes with several common models and features built in, and including custom models and new features is easy.The aim of the current paper is to present Uncertainpy for the neuroscience community in a user- oriented manner. To demonstrate its broad applicability, we perform an uncertainty quantification and sensitivity analysis on three case studies relevant for neuroscience: the original Hodgkin-Huxley point-neuron model for action potential generation, a multi-compartmental model of a thalamic interneuron implemented in the NEURON simulator, and a sparsely connected recurrent network model implemented in the NEST simulator.SIGNIFICANCE STATEMENTA major challenge in computational neuroscience is to specify the often large number of parameters that define the neuron and neural network models. Many of these parameters have an inherent variability, and some may even be actively regulated and change with time. It is important to know how the uncertainty in model parameters affects the model predictions. To address this need we here present Uncertainpy, an open-source Python toolbox tailored to perform uncertainty quantification and sensitivity analysis of neuroscience models.

Evaluation of Neural Network Models with Generalized Sensitivity Analysis

2000 ◽  
Vol 72 (20) ◽  
pp. 5004-5013 ◽  
Author(s):  
Peter de B. Harrington ◽  
Aaron Urbas ◽  
Chuanhao Wan
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Network Models ◽  
Neural Network Models ◽  
Generalized Sensitivity

scholarly journals Sensitivity Analysis of Neural Network Models: Applying Methods of Analysis of Finite Fluctuations

2019 ◽  
Vol 63 (4) ◽  
pp. 306-311 ◽  
Author(s):  
Anton Sysoev ◽  
Alessandro Ciurlia ◽  
Roman Sheglevatych ◽  
Semen Blyumin
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Social Systems ◽  
Network Models ◽  
Mean Value ◽  
Mean Value Theorem ◽  
Data Preparation ◽  
Neural Network Models ◽  
Factors Affecting ◽  
Initial Stage

As an initial stage prior to Mathematical Modeling, the information processing should provide qualitative data preparation for the construction of consistent models of technical, economic, social systems and technological processes. The question, concerning choosing the most significant input factors affecting the function of the system, is a very actual and important. This problem could be solved with the application of methods of Sensitivity Analysis. The presented paper has the purpose to show a possible approach to this problem through the method of the Analysis of Finite Fluctuations, based on Lagrange mean value theorem, to study the sensitivity of the model under consideration. The numerical example of comparing the results obtained by Sobol sensitivity coefficients, Garson algorithm and proposed approach showed the sustainability of the introduced method. There is shown, that the proposed approach is stable in the sense of applying different input datasets. In particular, the proposed approach has been applied to the construction of a neural network model identifying any anomalies present in certain medical insurances, in order to define the most significant input factors in the anomaly's detecting, discard the others and get a slim and efficient model.

scholarly journals Evaluating Representations for Gene Ontology Terms

2019 ◽  
Author(s):  
Dat Duong ◽  
Ankith Uppunda ◽  
Lisa Gai ◽  
Chelsea Ju ◽  
James Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Gene Ontology ◽  
Network Models ◽  
Language Models ◽  
Future Research ◽  
Model Parameters ◽  
Neural Network Models ◽  
Go Annotation ◽  
Go Terms ◽  
Similarity Scores

AbstractProtein functions can be described by the Gene Ontology (GO) terms, allowing us to compare the functions of two proteins by measuring the similarity of the terms assigned to them. Recent works have applied neural network models to derive the vector representations for GO terms and compute similarity scores for these terms by comparing their vector embeddings. There are two typical ways to embed GO terms into vectors; a model can either embed the definitions of the terms or the topology of the terms in the ontology. In this paper, we design three tasks to critically evaluate the GO embeddings of two recent neural network models, and further introduce additional models for embedding GO terms, adapted from three popular neural network frameworks: Graph Convolution Network (GCN), Embeddings from Language Models (ELMo), and Bidirectional Encoder Representations from Transformers (BERT), which have not yet been explored in previous works. Task 1 studies edge cases where the GO embeddings may not provide meaningful similarity scores for GO terms. We find that all neural network based methods fail to produce high similarity scores for related terms when these terms have low Information Content values. Task 2 is a canonical task which estimates how well GO embeddings can compare functions of two orthologous genes or two interacting proteins. The best neural network methods for this task are those that embed GO terms using their definitions, and the differences among such methods are small. Task 3 evaluates how GO embeddings affect the performance of GO annotation methods, which predict whether a protein should be labeled by certain GO terms. When the annotation datasets contain many samples for each GO label, GO embeddings do not improve the classification accuracy. Machine learning GO annotation methods often remove rare GO labels from the training datasets so that the model parameters can be efficiently trained. We evaluate whether GO embeddings can improve prediction of rare labels unseen in the training datasets, and find that GO embeddings based on the BERT framework achieve the best results in this setting. We present our embedding methods and three evaluation tasks as the basis for future research on this topic.

scholarly journals Coarse-Grained Pruning of Neural Network Models Based on Blocky Sparse Structure

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1042
Author(s):  
Lan Huang ◽  
Jia Zeng ◽  
Shiqi Sun ◽  
Wencong Wang ◽  
Yan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Sparse Matrix ◽  
Computational Cost ◽  
Network Models ◽  
Coarse Grained ◽  
Model Parameters ◽  
Neural Network Models ◽  
Convolutional Networks ◽  
Research Fields ◽  
Pruning Strategy

Deep neural networks may achieve excellent performance in many research fields. However, many deep neural network models are over-parameterized. The computation of weight matrices often consumes a lot of time, which requires plenty of computing resources. In order to solve these problems, a novel block-based division method and a special coarse-grained block pruning strategy are proposed in this paper to simplify and compress the fully connected structure, and the pruned weight matrices with a blocky structure are then stored in the format of Block Sparse Row (BSR) to accelerate the calculation of the weight matrices. First, the weight matrices are divided into square sub-blocks based on spatial aggregation. Second, a coarse-grained block pruning procedure is utilized to scale down the model parameters. Finally, the BSR storage format, which is much more friendly to block sparse matrix storage and computation, is employed to store these pruned dense weight blocks to speed up the calculation. In the following experiments on MNIST and Fashion-MNIST datasets, the trend of accuracies with different pruning granularities and different sparsity is explored in order to analyze our method. The experimental results show that our coarse-grained block pruning method can compress the network and can reduce the computational cost without greatly degrading the classification accuracy. The experiment on the CIFAR-10 dataset shows that our block pruning strategy can combine well with the convolutional networks.

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