Personalised modelling with spiking neural networks integrating temporal and static information

Doborjeh, Maryam Gholami; Kasabov, Nikola; Doborjeh, Zohreh Gholami; Enayatollahi, Reza; Tu, Enmei; Gandomi, Amir H.

doi:10.1016/j.neunet.2019.07.021

Cited by 10 publications

(9 citation statements)

References 48 publications

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“…To gain a mechanistic understanding of how tinnitus develops in the brain, we need to design a biologically plausible computational model that mimic both tinnitus formation and perception, then evaluate the preliminary models using brain and behavioral experiments (177). ANNs are computational models directly inspired by, and partially modeled on biological neural networks (160).…”

Section: Artificial Neural Network In Tinnitusmentioning

confidence: 99%

A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics

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Sanders

Doborjeh

et al. 2021

Front. Digit. Health

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Background: Digital processing has enabled the development of several generations of technology for tinnitus therapy. The first digital generation was comprised of digital Hearing Aids (HAs) and personal digital music players implementing already established sound-based therapies, as well as text based information on the internet. In the second generation Smart-phone applications (apps) alone or in conjunction with HAs resulted in more therapy options for users to select from. The 3rd generation of digital tinnitus technologies began with the emergence of many novel, largely neurophysiologically-inspired, treatment theories that drove development of processing; enabled through HAs, apps, the internet and stand-alone devices. We are now of the cusp of a 4th generation that will incorporate physiological sensors, multiple transducers and AI to personalize therapies.Aim: To review technologies that will enable the next generations of digital therapies for tinnitus.Methods: A “state-of-the-art” review was undertaken to answer the question: what digital technology could be applied to tinnitus therapy in the next 10 years? Google Scholar and PubMed were searched for the 10-year period 2011–2021. The search strategy used the following key words: “tinnitus” and [“HA,” “personalized therapy,” “AI” (and “methods” or “applications”), “Virtual reality,” “Games,” “Sensors” and “Transducers”], and “Hearables.” Snowballing was used to expand the search from the identified papers. The results of the review were cataloged and organized into themes.Results: This paper identified digital technologies and research on the development of smart therapies for tinnitus. AI methods that could have tinnitus applications are identified and discussed. The potential of personalized treatments and the benefits of being able to gather data in ecologically valid settings are outlined.Conclusions: There is a huge scope for the application of digital technology to tinnitus therapy, but the uncertain mechanisms underpinning tinnitus present a challenge and many posited therapeutic approaches may not be successful. Personalized AI modeling based on biometric measures obtained through various sensor types, and assessments of individual psychology and lifestyles should result in the development of smart therapy platforms for tinnitus.

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Section: Artificial Neural Network In Tinnitusmentioning

confidence: 99%

A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics

Searchfield

Sanders

Doborjeh

et al. 2021

Front. Digit. Health

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“…Diagnosis of any abnormal changes in the brain may indicate a disorder 14 , therefore awareness of the neuronal behavior along with the biomechanical structure can be remarkably effective 2 . Multimodal brain data, such as EEG and MRI have been collected in many studies 1 , 14 and the challenge now is to develop computational methods and tools that integrate these data for a better understanding of brain processes and for a better prediction of personal events 1 – 3 , 20 , 21 , 23 .…”

Section: Introductionmentioning

confidence: 99%

“…Biologically inspired spiking neural networks (SNN) emerge as suitable techniques for modelling spatio-temporal brain data (STBD) such as EEG 3 , 17 , 21 , 22 , or fMRI 9 , 21 , or fMRI and DTI 18 , 21 . A brain-inspired SNN architecture, called NeuCube, to model STBD, has already been introduced 15 and explored on single source brain data, such as EEG 3 , 17 , 21 – 23 , EMG 19 , fMRI 9 , 21 or even on a combination of fMRI and DTI data 18 , 21 . The basic structure of this architecture consists of a spike encoding module, a 3D SNNr reservoir module structured according to a general brain template such as Talairach or Montreal Neurological institute (MNI), and an output regression or classification module.…”

Section: Introductionmentioning

confidence: 99%

Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals

Saeedinia

Jahed‐Motlagh

Tafakhori

et al. 2021

Sci Rep

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This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning algorithm integrated with a spike-time-dependent-plasticity algorithm. The models capture informative personal patterns of interaction between EEG channels, contrary to single EEG signal modeling methods or to spike-based approaches which do not use personal MRI data to pre-structure a model. The proposed models can not only learn and model accurately measured EEG data, but they can also predict signals at 3D model locations that correspond to non-monitored brain areas, e.g. other EEG channels, from where data has not been collected. This is the first study in this respect. As an illustration of the method, personalized MRI-SNN models are created and tested on EEG data from two subjects. The models result in better prediction accuracy and a better understanding of the personalized EEG signals than traditional methods due to the MRI and EEG information integration. The models are interpretable and facilitate a better understanding of related brain processes. This approach can be applied for personalized modeling, analysis, and prediction of EEG signals across brain studies such as the study and prediction of epilepsy, peri-perceptual brain activities, brain-computer interfaces, and others.

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“…The literature suggests that SNNs are energy efficient and hardware friendly [ 2 , 3 , 4 , 5 ] compared to other artificial neural networks in machine learning (ML) systems. They have been successfully applied to various domains for classification and prediction (prognosis and diagnosis) of outcomes in temporal or spatiotemporal datasets such as classification of cognitive states using Electroencephalogram (EEG) [ 6 , 7 , 8 , 9 ], event-related potential (ERP) [ 10 , 11 , 12 ], and functional Magnetic Resonance Imaging (MRI) [ 13 , 14 , 15 , 16 ]. Several applications of SNNs are proposed in the medical domain for prognosis and diagnosis of diseases through modelling of bio-signals and biomedical images.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network

Doborjeh

Kasabov

et al. 2021

Sensors

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The paper proposes a new method for deep learning and knowledge discovery in a brain-inspired Spiking Neural Networks (SNN) architecture that enhances the model’s explainability while learning from streaming spatiotemporal brain data (STBD) in an incremental and on-line mode of operation. This led to the extraction of spatiotemporal rules from SNN models that explain why a certain decision (output prediction) was made by the model. During the learning process, the SNN created dynamic neural clusters, captured as polygons, which evolved in time and continuously changed their size and shape. The dynamic patterns of the clusters were quantitatively analyzed to identify the important STBD features that correspond to the most activated brain regions. We studied the trend of dynamically created clusters and their spike-driven events that occur together in specific space and time. The research contributes to: (1) enhanced interpretability of SNN learning behavior through dynamic neural clustering; (2) feature selection and enhanced accuracy of classification; (3) spatiotemporal rules to support model explainability; and (4) a better understanding of the dynamics in STBD in terms of feature interaction. The clustering method was applied to a case study of Electroencephalogram (EEG) data, recorded from a healthy control group (n = 21) and opiate use (n = 18) subjects while they were performing a cognitive task. The SNN models of EEG demonstrated different trends of dynamic clusters across the groups. This suggested to select a group of marker EEG features and resulted in an improved accuracy of EEG classification to 92%, when compared with all-feature classification. During learning of EEG data, the areas of neurons in the SNN model that form adjacent clusters (corresponding to neighboring EEG channels) were detected as fuzzy boundaries that explain overlapping activity of brain regions for each group of subjects.

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Personalised modelling with spiking neural networks integrating temporal and static information

Cited by 10 publications

References 48 publications

A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics

A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics

Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals

Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network

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