Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI

• URL: http://arxiv.org/abs/2005.09687v1
• Date: Tue, 19 May 2020 18:10:35 GMT
• Title: Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI
• Authors: Jesse A. Livezey and Joshua I. Glaser
• Abstract summary: Decoding behavior, perception, or cognitive state directly from neural signals has applications in brain-computer interface research. In the last decade, deep learning has become the state-of-the-art method in many machine learning tasks. Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks.
• Score: 2.0178765779788495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decoding behavior, perception, or cognitive state directly from neural signals has applications in brain-computer interface research as well as implications for systems neuroscience. In the last decade, deep learning has become the state-of-the-art method in many machine learning tasks ranging from speech recognition to image segmentation. The success of deep networks in other domains has led to a new wave of applications in neuroscience. In this article, we review deep learning approaches to neural decoding. We describe the architectures used for extracting useful features from neural recording modalities ranging from spikes to EEG. Furthermore, we explore how deep learning has been leveraged to predict common outputs including movement, speech, and vision, with a focus on how pretrained deep networks can be incorporated as priors for complex decoding targets like acoustic speech or images. Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks, and we point out areas for future scientific development.

Related papers

• Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks [74.3099028063756]
  We develop a new method with neuronal operations based on lateral connections and Hebbian learning. We show that Hebbian and anti-Hebbian learning on recurrent lateral connections can effectively extract the principal subspace of neural activities. Our method consistently solves for spiking neural networks with nearly zero forgetting.
  arXiv  Detail & Related papers  (2024-02-19T09:29:37Z)
• Deep Learning for real-time neural decoding of grasp [0.0]
  We present a Deep Learning-based approach to the decoding of neural signals for grasp type classification. The main goal of the presented approach is to improve over state-of-the-art decoding accuracy without relying on any prior neuroscience knowledge.
  arXiv  Detail & Related papers  (2023-11-02T08:26:29Z)
• A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks [53.31941519245432]
  Brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. These SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes.
  arXiv  Detail & Related papers  (2023-05-26T02:52:12Z)
• Functional Connectome: Approximating Brain Networks with Artificial Neural Networks [1.952097552284465]
  We show that trained deep neural networks are able to capture the computations performed by synthetic biological networks with high accuracy. We show that trained deep neural networks are able to perform zero-shot generalisation in novel environments. Our study reveals a novel and promising direction in systems neuroscience, and can be expanded upon with a multitude of downstream applications.
  arXiv  Detail & Related papers  (2022-11-23T13:12:13Z)
• DeepA: A Deep Neural Analyzer For Speech And Singing Vocoding [71.73405116189531]
  We propose a neural vocoder that extracts F0 and timbre/aperiodicity encoding from the input speech that emulates those defined in conventional vocoders. As the deep neural analyzer is learnable, it is expected to be more accurate for signal reconstruction and manipulation, and generalizable from speech to singing.
  arXiv  Detail & Related papers  (2021-10-13T01:39:57Z)
• Towards efficient end-to-end speech recognition with biologically-inspired neural networks [10.457580011403289]
  We introduce neural connectivity concepts emulating the axo-somatic and the axo-axonic synapses. We demonstrate for the first time, that a biologically realistic implementation of a large-scale ASR model can yield competitive performance levels.
  arXiv  Detail & Related papers  (2021-10-04T21:24:10Z)
• Training Spiking Neural Networks Using Lessons From Deep Learning [28.827506468167652]
  The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. Some ideas are well accepted and commonly used amongst the neuromorphic engineering community, while others are presented or justified for the first time here. A series of companion interactive tutorials complementary to this paper using our Python package, snnTorch, are also made available.
  arXiv  Detail & Related papers  (2021-09-27T09:28:04Z)
• Deep Spiking Convolutional Neural Network for Single Object Localization Based On Deep Continuous Local Learning [0.0]
  We propose a deep convolutional spiking neural network for the localization of a single object in a grayscale image. Results reported on Oxford-IIIT-Pet validates the exploitation of spiking neural networks with a supervised learning approach.
  arXiv  Detail & Related papers  (2021-05-12T12:02:05Z)
• Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks [82.54695985117783]
  We investigate the suitability of state-of-the-art deep learning architectures for continuous emotion recognition using long video sequences captured in-the-wild. We have developed and evaluated convolutional recurrent neural networks combining 2D-CNNs and long short term-memory units, and inflated 3D-CNN models, which are built by inflating the weights of a pre-trained 2D-CNN model during fine-tuning.
  arXiv  Detail & Related papers  (2020-11-18T13:42:05Z)
• Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting [135.0863818867184]
  artificial neural variability (ANV) helps artificial neural networks learn some advantages from natural'' neural networks. ANV plays as an implicit regularizer of the mutual information between the training data and the learned model. It can effectively relieve overfitting, label noise memorization, and catastrophic forgetting at negligible costs.
  arXiv  Detail & Related papers  (2020-11-12T06:06:33Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)

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.