Periodic solutions in next generation neural field models
Biol Cybern. 2023 Aug 3. doi: 10.1007/s00422-023-00969-6. Online ahead of print.ABSTRACTWe consider a next generation neural field model which describes the dynamics of a network of theta neurons on a ring. For some parameters the network supports stable time-periodic solutions. Using the fact that the dynamics at each spatial location are described by a complex-valued Riccati equation we derive a self-consistency equation that such periodic solutions must satisfy. We determine the stability of these solutions, and present numerical results to illustrate the usefulness of this technique. The generality of this approach is ...
Source: Biological Cybernetics - August 3, 2023 Category: Science Authors: Carlo R Laing Oleh E Omel'chenko Source Type: research
Periodic solutions in next generation neural field models
Biol Cybern. 2023 Aug 3. doi: 10.1007/s00422-023-00969-6. Online ahead of print.ABSTRACTWe consider a next generation neural field model which describes the dynamics of a network of theta neurons on a ring. For some parameters the network supports stable time-periodic solutions. Using the fact that the dynamics at each spatial location are described by a complex-valued Riccati equation we derive a self-consistency equation that such periodic solutions must satisfy. We determine the stability of these solutions, and present numerical results to illustrate the usefulness of this technique. The generality of this approach is ...
Source: Biological Cybernetics - August 3, 2023 Category: Science Authors: Carlo R Laing Oleh E Omel'chenko Source Type: research
Periodic solutions in next generation neural field models
Biol Cybern. 2023 Aug 3. doi: 10.1007/s00422-023-00969-6. Online ahead of print.ABSTRACTWe consider a next generation neural field model which describes the dynamics of a network of theta neurons on a ring. For some parameters the network supports stable time-periodic solutions. Using the fact that the dynamics at each spatial location are described by a complex-valued Riccati equation we derive a self-consistency equation that such periodic solutions must satisfy. We determine the stability of these solutions, and present numerical results to illustrate the usefulness of this technique. The generality of this approach is ...
Source: Biological Cybernetics - August 3, 2023 Category: Science Authors: Carlo R Laing Oleh E Omel'chenko Source Type: research
Periodic solutions in next generation neural field models
Biol Cybern. 2023 Aug 3. doi: 10.1007/s00422-023-00969-6. Online ahead of print.ABSTRACTWe consider a next generation neural field model which describes the dynamics of a network of theta neurons on a ring. For some parameters the network supports stable time-periodic solutions. Using the fact that the dynamics at each spatial location are described by a complex-valued Riccati equation we derive a self-consistency equation that such periodic solutions must satisfy. We determine the stability of these solutions, and present numerical results to illustrate the usefulness of this technique. The generality of this approach is ...
Source: Biological Cybernetics - August 3, 2023 Category: Science Authors: Carlo R Laing Oleh E Omel'chenko Source Type: research
Periodic solutions in next generation neural field models
Biol Cybern. 2023 Aug 3. doi: 10.1007/s00422-023-00969-6. Online ahead of print.ABSTRACTWe consider a next generation neural field model which describes the dynamics of a network of theta neurons on a ring. For some parameters the network supports stable time-periodic solutions. Using the fact that the dynamics at each spatial location are described by a complex-valued Riccati equation we derive a self-consistency equation that such periodic solutions must satisfy. We determine the stability of these solutions, and present numerical results to illustrate the usefulness of this technique. The generality of this approach is ...
Source: Biological Cybernetics - August 3, 2023 Category: Science Authors: Carlo R Laing Oleh E Omel'chenko Source Type: research
Periodic solutions in next generation neural field models
Biol Cybern. 2023 Aug 3. doi: 10.1007/s00422-023-00969-6. Online ahead of print.ABSTRACTWe consider a next generation neural field model which describes the dynamics of a network of theta neurons on a ring. For some parameters the network supports stable time-periodic solutions. Using the fact that the dynamics at each spatial location are described by a complex-valued Riccati equation we derive a self-consistency equation that such periodic solutions must satisfy. We determine the stability of these solutions, and present numerical results to illustrate the usefulness of this technique. The generality of this approach is ...
Source: Biological Cybernetics - August 3, 2023 Category: Science Authors: Carlo R Laing Oleh E Omel'chenko Source Type: research
Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
Biol Cybern. 2023 Jul 4. doi: 10.1007/s00422-023-00967-8. Online ahead of print.ABSTRACTWe propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity wi...
Source: Biological Cybernetics - July 4, 2023 Category: Science Authors: Michael Briden Narges Norouzi Source Type: research
Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
Biol Cybern. 2023 Jul 4. doi: 10.1007/s00422-023-00967-8. Online ahead of print.ABSTRACTWe propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity wi...
Source: Biological Cybernetics - July 4, 2023 Category: Science Authors: Michael Briden Narges Norouzi Source Type: research
Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
Biol Cybern. 2023 Jul 4. doi: 10.1007/s00422-023-00967-8. Online ahead of print.ABSTRACTWe propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity wi...
Source: Biological Cybernetics - July 4, 2023 Category: Science Authors: Michael Briden Narges Norouzi Source Type: research
Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
Biol Cybern. 2023 Jul 4. doi: 10.1007/s00422-023-00967-8. Online ahead of print.ABSTRACTWe propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity wi...
Source: Biological Cybernetics - July 4, 2023 Category: Science Authors: Michael Briden Narges Norouzi Source Type: research
Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
Biol Cybern. 2023 Jul 4. doi: 10.1007/s00422-023-00967-8. Online ahead of print.ABSTRACTWe propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity wi...
Source: Biological Cybernetics - July 4, 2023 Category: Science Authors: Michael Briden Narges Norouzi Source Type: research
Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
Biol Cybern. 2023 Jul 4. doi: 10.1007/s00422-023-00967-8. Online ahead of print.ABSTRACTWe propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity wi...
Source: Biological Cybernetics - July 4, 2023 Category: Science Authors: Michael Briden Narges Norouzi Source Type: research
Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
Biol Cybern. 2023 Jul 4. doi: 10.1007/s00422-023-00967-8. Online ahead of print.ABSTRACTWe propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity wi...
Source: Biological Cybernetics - July 4, 2023 Category: Science Authors: Michael Briden Narges Norouzi Source Type: research
Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
Biol Cybern. 2023 Jul 4. doi: 10.1007/s00422-023-00967-8. Online ahead of print.ABSTRACTWe propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity wi...
Source: Biological Cybernetics - July 4, 2023 Category: Science Authors: Michael Briden Narges Norouzi Source Type: research
Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
Biol Cybern. 2023 Jul 4. doi: 10.1007/s00422-023-00967-8. Online ahead of print.ABSTRACTWe propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity wi...
Source: Biological Cybernetics - July 4, 2023 Category: Science Authors: Michael Briden Narges Norouzi Source Type: research
