Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks
Understanding cognitive processes in the brain demands sophisticated models capable of replicating neural dynamics at large scales. We present a physiologically inspired speech recognition architecture, compatible and scalable with deep learning frameworks, and demonstrate that end-to-end gradient d...
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Frontiers Media S.A.
2024-09-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1449181/full |
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| author | Alexandre Bittar Alexandre Bittar Philip N. Garner |
| author_facet | Alexandre Bittar Alexandre Bittar Philip N. Garner |
| author_sort | Alexandre Bittar |
| collection | DOAJ |
| description | Understanding cognitive processes in the brain demands sophisticated models capable of replicating neural dynamics at large scales. We present a physiologically inspired speech recognition architecture, compatible and scalable with deep learning frameworks, and demonstrate that end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Significant cross-frequency couplings, indicative of these oscillations, are measured within and across network layers during speech processing, whereas no such interactions are observed when handling background noise inputs. Furthermore, our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronizing neural activity to improve recognition performance. Overall, on top of developing our understanding of synchronization phenomena notably observed in the human auditory pathway, our architecture exhibits dynamic and efficient information processing, with relevance to neuromorphic technology. |
| format | Article |
| id | doaj-art-e05d8260d3284aaaafc1e4ee3897ddde |
| institution | OA Journals |
| issn | 1662-453X |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-e05d8260d3284aaaafc1e4ee3897ddde2025-08-20T01:54:46ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2024-09-011810.3389/fnins.2024.14491811449181Exploring neural oscillations during speech perception via surrogate gradient spiking neural networksAlexandre Bittar0Alexandre Bittar1Philip N. Garner2Idiap Research Institute, Audio Inference, Martigny, SwitzerlandÉcole Polytechnique Fédérale de Lausanne, Lausanne, SwitzerlandÉcole Polytechnique Fédérale de Lausanne, Lausanne, SwitzerlandUnderstanding cognitive processes in the brain demands sophisticated models capable of replicating neural dynamics at large scales. We present a physiologically inspired speech recognition architecture, compatible and scalable with deep learning frameworks, and demonstrate that end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Significant cross-frequency couplings, indicative of these oscillations, are measured within and across network layers during speech processing, whereas no such interactions are observed when handling background noise inputs. Furthermore, our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronizing neural activity to improve recognition performance. Overall, on top of developing our understanding of synchronization phenomena notably observed in the human auditory pathway, our architecture exhibits dynamic and efficient information processing, with relevance to neuromorphic technology.https://www.frontiersin.org/articles/10.3389/fnins.2024.1449181/fullneural oscillationsspiking neural networksspeech recognitionbrain-inspired computingdeep learningsurrogate gradient |
| spellingShingle | Alexandre Bittar Alexandre Bittar Philip N. Garner Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks Frontiers in Neuroscience neural oscillations spiking neural networks speech recognition brain-inspired computing deep learning surrogate gradient |
| title | Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks |
| title_full | Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks |
| title_fullStr | Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks |
| title_full_unstemmed | Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks |
| title_short | Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks |
| title_sort | exploring neural oscillations during speech perception via surrogate gradient spiking neural networks |
| topic | neural oscillations spiking neural networks speech recognition brain-inspired computing deep learning surrogate gradient |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2024.1449181/full |
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