Understanding Auditory Evoked Brain Signal via Physics-informed Embedding Network with Multi-Task Transformer

• URL: http://arxiv.org/abs/2406.02014v1
• Date: Tue, 4 Jun 2024 06:53:32 GMT
• Title: Understanding Auditory Evoked Brain Signal via Physics-informed Embedding Network with Multi-Task Transformer
• Authors: Wanli Ma, Xuegang Tang, Jin Gu, Ying Wang, Yuling Xia,
• Abstract summary: We propose an innovative multi-task learning model, Physics-informed Embedding Network with Multi-Task Transformer (PEMT-Net) PEMT-Net enhances decoding performance through physics-informed embedding and deep learning techniques. Experiments on a specific dataset demonstrate PEMT-Net's significant performance in multi-task auditory signal decoding.
• Score: 3.261870217889503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the fields of brain-computer interaction and cognitive neuroscience, effective decoding of auditory signals from task-based functional magnetic resonance imaging (fMRI) is key to understanding how the brain processes complex auditory information. Although existing methods have enhanced decoding capabilities, limitations remain in information utilization and model representation. To overcome these challenges, we propose an innovative multi-task learning model, Physics-informed Embedding Network with Multi-Task Transformer (PEMT-Net), which enhances decoding performance through physics-informed embedding and deep learning techniques. PEMT-Net consists of two principal components: feature augmentation and classification. For feature augmentation, we propose a novel approach by creating neural embedding graphs via node embedding, utilizing random walks to simulate the physical diffusion of neural information. This method captures both local and non-local information overflow and proposes a position encoding based on relative physical coordinates. In the classification segment, we propose adaptive embedding fusion to maximally capture linear and non-linear characteristics. Furthermore, we propose an innovative parameter-sharing mechanism to optimize the retention and learning of extracted features. Experiments on a specific dataset demonstrate PEMT-Net's significant performance in multi-task auditory signal decoding, surpassing existing methods and offering new insights into the brain's mechanisms for processing complex auditory information.

Related papers

• MindFormer: A Transformer Architecture for Multi-Subject Brain Decoding via fMRI [50.55024115943266]
  We introduce a new Transformer architecture called MindFormer to generate fMRI-conditioned feature vectors. MindFormer incorporates two key innovations: 1) a novel training strategy based on the IP-Adapter to extract semantically meaningful features from fMRI signals, and 2) a subject specific token and linear layer that effectively capture individual differences in fMRI signals.
  arXiv  Detail & Related papers  (2024-05-28T00:36:25Z)
• Sparse Multitask Learning for Efficient Neural Representation of Motor Imagery and Execution [30.186917337606477]
  We introduce a sparse multitask learning framework for motor imagery (MI) and motor execution (ME) tasks. Given a dual-task CNN model for MI-ME classification, we apply a saliency-based sparsification approach to prune superfluous connections. Our results indicate that this tailored sparsity can mitigate the overfitting problem and improve the test performance with small amount of data.
  arXiv  Detail & Related papers  (2023-12-10T09:06:16Z)
• Neuromorphic Auditory Perception by Neural Spiketrum [27.871072042280712]
  We introduce a neural spike coding model called spiketrumtemporal, to transform the time-varying analog signals into efficient spike patterns. The model provides a sparse and efficient coding scheme with precisely controllable spike rate that facilitates training of spiking neural networks in various auditory perception tasks.
  arXiv  Detail & Related papers  (2023-09-11T13:06:19Z)
• Dynamic Encoding and Decoding of Information for Split Learning in Mobile-Edge Computing: Leveraging Information Bottleneck Theory [1.1151919978983582]
  Split learning is a privacy-preserving distributed learning paradigm in which an ML model is split into two parts (i.e., an encoder and a decoder) In mobile-edge computing, network functions can be trained via split learning where an encoder resides in a user equipment (UE) and a decoder resides in the edge network. We present a new framework and training mechanism to enable a dynamic balancing of the transmission resource consumption with the informativeness of the shared latent representations.
  arXiv  Detail & Related papers  (2023-09-06T07:04:37Z)
• Contrastive-Signal-Dependent Plasticity: Forward-Forward Learning of Spiking Neural Systems [73.18020682258606]
  We develop a neuro-mimetic architecture, composed of spiking neuronal units, where individual layers of neurons operate in parallel. We propose an event-based generalization of forward-forward learning, which we call contrastive-signal-dependent plasticity (CSDP) Our experimental results on several pattern datasets demonstrate that the CSDP process works well for training a dynamic recurrent spiking network capable of both classification and reconstruction.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• The Predictive Forward-Forward Algorithm [79.07468367923619]
  We propose the predictive forward-forward (PFF) algorithm for conducting credit assignment in neural systems. We design a novel, dynamic recurrent neural system that learns a directed generative circuit jointly and simultaneously with a representation circuit. PFF efficiently learns to propagate learning signals and updates synapses with forward passes only.
  arXiv  Detail & Related papers  (2023-01-04T05:34:48Z)
• Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks [107.8565143456161]
  We investigate the information processing strategies adopted by simple artificial neural networks performing a variety of cognitive tasks. Results show that synergy increases as neural networks learn multiple diverse tasks. randomly turning off neurons during training through dropout increases network redundancy, corresponding to an increase in robustness.
  arXiv  Detail & Related papers  (2022-10-06T15:36:27Z)
• Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining [24.641328814546842]
  We present Neuro-BERT, a self-supervised pre-training framework of neurological signals based on masked autoencoding in the Fourier domain. We propose a novel pre-training task dubbed Fourier Inversion Prediction (FIP), which randomly masks out a portion of the input signal and then predicts the missing information. By evaluating our method on several benchmark datasets, we show that Neuro-BERT improves downstream neurological-related tasks by a large margin.
  arXiv  Detail & Related papers  (2022-04-20T16:48:18Z)
• Spiking Neural Networks Hardware Implementations and Challenges: a Survey [53.429871539789445]
  Spiking Neural Networks are cognitive algorithms mimicking neuron and synapse operational principles. We present the state of the art of hardware implementations of spiking neural networks. We discuss the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level.
  arXiv  Detail & Related papers  (2020-05-04T13:24:00Z)
• Triple Memory Networks: a Brain-Inspired Method for Continual Learning [35.40452724755021]
  A neural network adjusts its parameters when learning a new task, but then fails to conduct the old tasks well. The brain has a powerful ability to continually learn new experience without catastrophic interference. Inspired by such brain strategy, we propose a novel approach named triple memory networks (TMNs) for continual learning.
  arXiv  Detail & Related papers  (2020-03-06T11:35:24Z)
• Multilinear Compressive Learning with Prior Knowledge [106.12874293597754]
  Multilinear Compressive Learning (MCL) framework combines Multilinear Compressive Sensing and Machine Learning into an end-to-end system. Key idea behind MCL is the assumption of the existence of a tensor subspace which can capture the essential features from the signal for the downstream learning task. In this paper, we propose a novel solution to address both of the aforementioned requirements, i.e., How to find those tensor subspaces in which the signals of interest are highly separable?
  arXiv  Detail & Related papers  (2020-02-17T19:06:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.