scholarly journals A convolutional neural network for estimating synaptic connectivity from spike trains

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daisuke Endo ◽  
Ryota Kobayashi ◽  
Ramon Bartolo ◽  
Bruno B. Averbeck ◽  
Yasuko Sugase-Miyamoto ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Generalized Linear Model ◽  
Simulated Data ◽  
Spike Trains ◽  
Synaptic Connectivity ◽  
Neuronal Circuits ◽  
Cortical Areas ◽  
Neuronal Spike Trains ◽  
Individual Dataset

AbstractThe recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains. After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.

scholarly journals CoNNECT: Convolutional Neural Network for Estimating synaptic Connectivity from spike Trains

2020 ◽  
Author(s):  
Daisuke Endo ◽  
Ryota Kobayashi ◽  
Ramon Bartolo ◽  
Bruno B. Averbeck ◽  
Yasuko Sugase-Miyamoto ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Generalized Linear Model ◽  
Simulated Data ◽  
Spike Trains ◽  
Synaptic Connectivity ◽  
Neuronal Circuits ◽  
Cortical Areas ◽  
Neuronal Spike Trains ◽  
Individual Dataset

The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians, because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms, GLMCC. Although the GLMCC algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another algorithm using a convolutional neural network for estimating synaptic connectivity from spike trains, CoNNECT. After adaptation to very large amounts of simulated data, this algorithm robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new algorithm, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.

scholarly journals Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 949
Author(s):  
Jiangyi Wang ◽  
Min Liu ◽  
Xinwu Zeng ◽  
Xiaoqiang Hua
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Detection Method ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Data Sets ◽  
Feature Maps ◽  
Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

scholarly journals Identification of Natural Selection in Genomic Data with Deep Convolutional Neural Network

2021 ◽  
Author(s):  
Arnaud Nguembang Fadja ◽  
Fabrizio Riguzzi ◽  
Giorgio Bertorelle ◽  
Emiliano Trucchi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Natural Selection ◽  
Convolutional Neural Network ◽  
Simulated Data ◽  
Genomic Data ◽  
Relevant Information ◽  
Supervised Machine Learning ◽  
Selection Processes ◽  
Adaptive Processes

Abstract Background: With the increase in the size of genomic datasets describing variability in populations, extracting relevant information becomes increasingly useful as well as complex. Recently, computational methodologies such as Supervised Machine Learning and specifically Convolutional Neural Networks have been proposed to order to make inferences on demographic and adaptive processes using genomic data, Even though it was already shown to be powerful and efficient in different fields of investigation, Supervised Machine Learning has still to be explored as to unfold its enormous potential in evolutionary genomics. Results: The paper proposes a method based on Supervised Machine Learning for classifying genomic data, represented as windows of genomic sequences from a sample of individuals belonging to the same population. A Convolutional Neural Network is used to test whether a genomic window shows the signature of natural selection. Experiments performed on simulated data show that the proposed model can accurately predict neutral and selection processes on genomic data with more than 99% accuracy.

scholarly journals End-to-end machine learning for experimental physics: using simulated data to train a neural network for object detection in video microscopy

Soft Matter ◽  
2020 ◽  
Vol 16 (7) ◽  
pp. 1751-1759 ◽  
Author(s):  
Eric N. Minor ◽  
Stian D. Howard ◽  
Adam A. S. Green ◽  
Matthew A. Glaser ◽  
Cheol S. Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Experimental Data ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real World ◽  
Simulated Data ◽  
Video Microscopy ◽  
Experimental Physics ◽  
Simulated Images

We demonstrate a method for training a convolutional neural network with simulated images for usage on real-world experimental data.

scholarly journals Identification of natural selection in genomic data with deep convolutional neural network

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Arnaud Nguembang Fadja ◽  
Fabrizio Riguzzi ◽  
Giorgio Bertorelle ◽  
Emiliano Trucchi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Natural Selection ◽  
Convolutional Neural Network ◽  
Simulated Data ◽  
Genomic Data ◽  
Relevant Information ◽  
Supervised Machine Learning ◽  
Selection Processes ◽  
Adaptive Processes

Abstract Background With the increase in the size of genomic datasets describing variability in populations, extracting relevant information becomes increasingly useful as well as complex. Recently, computational methodologies such as Supervised Machine Learning and specifically Convolutional Neural Networks have been proposed to make inferences on demographic and adaptive processes using genomic data. Even though it was already shown to be powerful and efficient in different fields of investigation, Supervised Machine Learning has still to be explored as to unfold its enormous potential in evolutionary genomics. Results The paper proposes a method based on Supervised Machine Learning for classifying genomic data, represented as windows of genomic sequences from a sample of individuals belonging to the same population. A Convolutional Neural Network is used to test whether a genomic window shows the signature of natural selection. Training performed on simulated data show that the proposed model can accurately predict neutral and selection processes on portions of genomes taken from real populations with almost 90% accuracy.

scholarly journals Particle identification with an electromagnetic calorimeter using a Convolutional Neural Network

2021 ◽  
Vol 251 ◽  
pp. 04032
Author(s):  
Alex Rua Herrera ◽  
Míriam Calvo Gómez ◽  
Xavier Vilasís Cardona
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Simulated Data ◽  
Particle Identification ◽  
Electromagnetic Calorimeter

The LHCb’s Electromagentic Calorimeter (ECAL) measures the energy that any particle leaves behind when it travels through its sensors. However, with the current granularity, it is not possible to exploit the shape of the shower produced by the particle when it interacts with the ECAL, which is an information that could be enough to conclude what particle is being detected. In an attempt to find out whether it would be possible to classify them in future runs of the LHC, simulated data is generated with Geant4, giving an idea of what SPACAL, an updated version of the current calorimeter with better resolution, is capable of. Convolutional Neural Networks are applied so that the algorithm can understand the shapes and energy deposits produced by each kind of particle. Results obtained demonstrate that bigger resolution in ECAL allows over 95% precision in some classifications such as photons against neutrons.

Source deghosting of coarsely-sampled common-receiver data using a convolutional neural network

Geophysics ◽  
2021 ◽  
pp. 1-48
Author(s):  
Jan-Willem Vrolijk ◽  
Gerrit Blacquiere
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Field Data ◽  
Simulated Data ◽  
Training Data ◽  
Learning Approach ◽  
Receiver Domain ◽  
Machine Learning Approach ◽  
The Common

It is well known that source deghosting can best be applied to common-receiver gathers, while receiver deghosting can best be applied to common-shot records. The source-ghost wavefield observed in the common-shot domain contains the imprint of the subsurface, which complicates source deghosting in common-shot domain, in particular when the subsurface is complex. Unfortunately, the alternative, i.e., the common-receiver domain, is often coarsely sampled, which complicates source deghosting in this domain as well. To solve the latter issue, we propose to train a convolutional neural network to apply source deghosting in this domain. We subsample all shot records with and without the receiver ghost wavefield to obtain the training data. Due to reciprocity this training data is a representative data set for source deghosting in the coarse common-receiver domain. We validate the machine-learning approach on simulated data and on field data. The machine learning approach gives a significant uplift to the simulated data compared to conventional source deghosting. The field-data results confirm that the proposed machine-learning approach is able to remove the source-ghost wavefield from the coarsely-sampled common-receiver gathers.

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