ArticleCommunications biology2021
A convolutional neural-network framework for modelling auditory sensory cells and synapses.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A DEEP NEURAL NETWORK FOR PREDICTING CONTINUOUS HUMAN EEG ACROSS THE AUDITORY PATHWAY IN RESPONSE TO SOUND.ArXiv · 2026Article
- Principal bundle geometry of qualia: Understanding the quality of consciousness from symmetry.PNAS nexus · 2026Article
- Activation of silent synapses driven by emerging technologies: mechanisms, disease associations, and prospects for clinical translation.Frontiers in synaptic neuroscience · 2026Review
- Wordsworth: A generative word dataset for comparison of speech representations in humans and neural networks.Scientific data · 2025Article
- Modelling neural coding in the auditory midbrain with high resolution and accuracy.Nature machine intelligence · 2025Article
- Pruning and quantization algorithm with applications in memristor-based convolutional neural network.Cognitive neurodynamics · 2024Article
- Hearing temperatures: employing machine learning for elucidating the cross-modal perception of thermal properties through audition.Frontiers in psychology · 2024Article
- Design and evaluation of a global workspace agent embodied in a realistic multimodal environment.Frontiers in computational neuroscience · 2024Article
- Evaluation of the Hierarchical Correspondence between the Human Brain and Artificial Neural Networks: A Review.Biology · 2023Review
- Unraveling Spatial-Spectral Dynamics of Speech Categorization Speed Using Convolutional Neural Networks.Brain sciences · 2022Article
- Brain signal predictions from multi-scale networks using a linearized framework.PLoS computational biology · 2022Article
- A comparative study of eight human auditory models of monaural processing.Acta acustica. European Acoustics Association · 2022Article
- Enhanced Place Specificity of the Parallel Auditory Brainstem Response: A Modeling Study.Trends in hearingArticle
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Authors and funding
3 authors.
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Abstract
In classical computational neuroscience, analytical model descriptions are derived from neuronal recordings to mimic the underlying biological system. These neuronal models are typically slow to compute and cannot be integrated within large-scale neuronal simulation frameworks. We present a hybrid, machine-learning and computational-neuroscience approach that transforms analytical models of sensory neurons and synapses into deep-neural-network (DNN) neuronal units with the same biophysical properties. Our DNN-model architecture comprises parallel and differentiable equations that can be used for backpropagation in neuro-engineering applications, and offers a simulation run-time improvement factor of 70 and 280 on CPU or GPU systems respectively. We focussed our development on auditory neurons and synapses, and show that our DNN-model architecture can be extended to a variety of existing analytical models. We describe how our approach for auditory models can be applied to other neuron and synapse types to help accelerate the development of large-scale brain networks and DNN-based treatments of the pathological system.
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