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1.
Neuron ; 112(12): 2045-2061.e10, 2024 Jun 19.
Artigo em Inglês | MEDLINE | ID: mdl-38636524

RESUMO

Cholecystokinin-expressing interneurons (CCKIs) are hypothesized to shape pyramidal cell-firing patterns and regulate network oscillations and related network state transitions. To directly probe their role in the CA1 region, we silenced their activity using optogenetic and chemogenetic tools in mice. Opto-tagged CCKIs revealed a heterogeneous population, and their optogenetic silencing triggered wide disinhibitory network changes affecting both pyramidal cells and other interneurons. CCKI silencing enhanced pyramidal cell burst firing and altered the temporal coding of place cells: theta phase precession was disrupted, whereas sequence reactivation was enhanced. Chemogenetic CCKI silencing did not alter the acquisition of spatial reference memories on the Morris water maze but enhanced the recall of contextual fear memories and enabled selective recall when similar environments were tested. This work suggests the key involvement of CCKIs in the control of place-cell temporal coding and the formation of contextual memories.


Assuntos
Colecistocinina , Hipocampo , Interneurônios , Optogenética , Células Piramidais , Animais , Masculino , Camundongos , Região CA1 Hipocampal/fisiologia , Região CA1 Hipocampal/citologia , Região CA1 Hipocampal/metabolismo , Colecistocinina/metabolismo , Colecistocinina/genética , Medo/fisiologia , Hipocampo/fisiologia , Interneurônios/fisiologia , Interneurônios/metabolismo , Aprendizagem/fisiologia , Aprendizagem em Labirinto/fisiologia , Memória/fisiologia , Rememoração Mental/fisiologia , Camundongos Endogâmicos C57BL , Camundongos Transgênicos , Células Piramidais/fisiologia , Células Piramidais/metabolismo , Ritmo Teta/fisiologia
2.
Biosensors (Basel) ; 10(9)2020 Sep 13.
Artigo em Inglês | MEDLINE | ID: mdl-32933146

RESUMO

The risk of personal data exposure through unauthorized access has never been as imminent as today. To counter this, biometric authentication has been proposed: the use of distinctive physiological and behavioral characteristics as a form of identification and access control. One of the recent developments is electroencephalography (EEG)-based authentication. It builds on the subject-specific nature of brain responses which are difficult to recreate artificially. We propose an authentication system based on EEG signals recorded in response to a simple motor paradigm. Authentication is achieved with a novel two-stage decoder. In the first stage, EEG signal features are extracted using an inception- and a VGG-like deep learning neural network (NN) both of which we compare with principal component analysis (PCA). In the second stage, a support vector machine (SVM) is used for binary classification to authenticate the subject based on the extracted features. All decoders are trained on EEG motor-movement data recorded from 105 subjects. We achieved with the VGG-like NN-SVM decoder a false-acceptance rate (FAR) of 2.55% with an overall accuracy of 88.29%, a FAR of 3.33% with an accuracy of 87.47%, and a FAR of 2.89% with an accuracy of 90.68% for 8, 16, and 64 channels, respectively. With the Inception-like NN-SVM decoder we achieved a false-acceptance rate (FAR) of 4.08% with an overall accuracy of 87.29%, a FAR of 3.53% with an accuracy of 85.31%, and a FAR of 1.27% with an accuracy of 93.40% for 8, 16, and 64 channels, respectively. The PCA-SVM decoder achieved accuracies of 92.09%, 92.36%, and 95.64% with FARs of 2.19%, 2.17%, and 1.26% for 8, 16, and 64 channels, respectively.


Assuntos
Identificação Biométrica/métodos , Eletroencefalografia , Algoritmos , Encéfalo , Humanos , Redes Neurais de Computação , Análise de Componente Principal , Processamento de Sinais Assistido por Computador , Máquina de Vetores de Suporte
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