Quantifying encoding redundancy induced by rate correlations in Poisson neurons

Herfurth, Tim; Tchumatchenko, Tatjana

doi:10.1103/physreve.99.042402

Cited by 4 publications

(2 citation statements)

References 77 publications

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“…As the release of neurotransmitters in the synapse is a response to a spike event rather than to the small variations in resting potential, the response of a postsynaptic cell is therefore also a function of the spike events. Analytically, the spike events are treated in a probabilistic fashion (i.e., the probability of a spike occurring) to which a Poisson process fits well [ 21 ]. The Poisson process describes a model for the estimated time between events, and so when applied in this domain to estimate the time between voltage spikes, the intercellular signals are seen as a Poisson Spike Train .…”

Section: Methodsmentioning

confidence: 99%

Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks

et al. 2022

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The classification of biological neuron types and networks poses challenges to the full understanding of the human brain’s organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morphology and electrical types and their networks, based on the attributes of neuronal communication using supervised machine learning solutions. This presents advantages compared to the existing approaches in neuroinformatics since the data related to mutual information or delay between neurons obtained from spike trains are more abundant than conventional morphological data. We constructed two open-access computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then, we investigated how we could perform network tomography with cortical neuronal circuits for the morphological, topological and electrical classification of neurons. We extracted the simulated data of 10,000 network topology combinations with five layers, 25 morphological type (m-type) cells, and 14 electrical type (e-type) cells. We applied the data to several different classifiers (including Support Vector Machine (SVM), Decision Trees, Random Forest, and Artificial Neural Networks). We achieved accuracies of up to 70%, and the inference of biological network structures using network tomography reached up to 65% of accuracy. Objective classification of biological networks can be achieved with cascaded machine learning methods using neuron communication data. SVM methods seem to perform better amongst used techniques. Our research not only contributes to existing classification efforts but sets the road-map for future usage of brain–machine interfaces towards an in vivo objective classification of neurons as a sensing mechanism of the brain’s structure.

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Section: Methodsmentioning

confidence: 99%

Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks

et al. 2022

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“…Analytically, the spike events are treated in a probabilistic fashion (i.e. the probability of a spike occurring) to which a Poisson process fits well [16]. The Poisson process describes a model for the estimated time between events, and so when applied in this domain to estimate the time between voltage spikes, the intercellular signals are seen as a Poisson Spike Train.…”

Section: A Cortical Network Characterisationmentioning

confidence: 99%

Objective Multi-variable Classification and Inference of Biological Neuronal Networks

Barros¹,

Šiljak²,

Mullen³

et al. 2020

Preprint

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Classification of biological neuron types and networks poses challenges to the full understanding of the brain's organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal types and networks based on the communication metrics of neurons. This presents advantages against the existing approaches since the mutual information or the delay between neurons obtained from spike trains are more abundant data compare to conventional morphological data. We firstly designed two open-access supporting computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then we investigate how the concept of network tomography could be achieved with cortical neuronal circuits for morphological, topological and electrical classification of neurons. We extract the simulated data to many different classifiers (including SVM, Decision Trees, Random Forest, and Artificial Neuron Networks) classifying the specific cell type (and sub-group types) achieving accuracies of up to 70%. Inference of biological network structures using network tomography reached up to 65% of accuracy. We also analysed recall, precision and F1score of the classification of five layers, 25 cell m-types, and 14 cell e-types. Our research not only contributes to existing classification efforts but sets the road-map for future usage of cellular-scaled brain-machine interfaces for in-vivo objective classification of neurons as a sensing mechanism of the brain's structure.

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Synthesis Weibull Stochastic Differential Equation: Properties and application

Abonongo,

Chidzalo

2024

Scientific African

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Quantifying encoding redundancy induced by rate correlations in Poisson neurons

Cited by 4 publications

References 77 publications

Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks

Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks

Objective Multi-variable Classification and Inference of Biological Neuronal Networks

Synthesis Weibull Stochastic Differential Equation: Properties and application

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