Temporal recurrence as a general mechanism to explain neural responses in the auditory system
সংরক্ষণ করুন:
| প্রকাশিত: | bioRxiv (Feb 4, 2025) |
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| প্রধান লেখক: | |
| অন্যান্য লেখক: | , |
| প্রকাশিত: |
Cold Spring Harbor Laboratory Press
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | Citation/Abstract Full Text - PDF Full text outside of ProQuest |
| ট্যাগগুলো: |
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MARC
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| 001 | 3153304477 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2692-8205 | ||
| 024 | 7 | |a 10.1101/2025.01.08.631909 |2 doi | |
| 035 | |a 3153304477 | ||
| 045 | 0 | |b d20250204 | |
| 100 | 1 | |a Ulysse Rançon | |
| 245 | 1 | |a Temporal recurrence as a general mechanism to explain neural responses in the auditory system | |
| 260 | |b Cold Spring Harbor Laboratory Press |c Feb 4, 2025 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Computational models of neural processing in the auditory cortex usually ignore that neurons have an internal memory: they characterize their responses from simple convolutions with a finite temporal window of arbitrary duration. To circumvent this limitation, we propose here a new, simple and fully recurrent neural network (RNN) architecture incorporating cutting-edge computational blocks from the deep learning community and constituting the first attempt to model auditory responses with deep RNNs. We evaluated the ability of this approach to fit neural responses from 8 publicly available datasets, spanning 3 animal species and 6 auditory brain areas, representing the largest compilation of this kind. Our recurrent models significantly outperform previous methods and a new Transformer-based architecture of our design on this task, suggesting that temporal recurrence is the key to explain auditory responses. Finally, we developed a novel interpretation technique to reverse-engineer any pretrained model, regardless of their stateful or stateless nature. Largely inspired by works from explainable artificial intelligence (xAI), our method suggests that auditory neurons have much longer memory (several seconds) than indicated by current STRF techniques. Together, these results highly motivate the use of deep RNNs within computational models of sensory neurons, as protean building blocks capable of assuming any function.Competing Interest StatementThe authors have declared no competing interest.Footnotes* update reports of performance on AA1 datasets after a correction in the data preprocessing methods.* https://github.com/urancon/deepSTRF | |
| 653 | |a Sensory neurons | ||
| 653 | |a Brain architecture | ||
| 653 | |a Somatosensory cortex | ||
| 653 | |a Hearing | ||
| 653 | |a Cortex (auditory) | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Computational neuroscience | ||
| 653 | |a Information processing | ||
| 653 | |a Auditory discrimination learning | ||
| 653 | |a Auditory system | ||
| 653 | |a Deep learning | ||
| 653 | |a Temporal lobe | ||
| 653 | |a Neural networks | ||
| 700 | 1 | |a Masquelier, Timothée | |
| 700 | 1 | |a Cottereau, Benoit R | |
| 773 | 0 | |t bioRxiv |g (Feb 4, 2025) | |
| 786 | 0 | |d ProQuest |t Biological Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3153304477/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3153304477/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u https://www.biorxiv.org/content/10.1101/2025.01.08.631909v2 |