Deep Learning-based Stress Determinator for Mouse Psychiatric Analysis using Hippocampus Activity

• URL: http://arxiv.org/abs/2006.06862v2
• Date: Sat, 27 Jun 2020 21:31:14 GMT
• Title: Deep Learning-based Stress Determinator for Mouse Psychiatric Analysis using Hippocampus Activity
• Authors: Donghan Liu, Benjamin C. M. Fung, Tak Pan Wong
• Abstract summary: We combine the state-of-the-art deep learning techniques with the theory of neuron decoding to discuss its potential of accomplishment. The experiments suggest that our state-of-the-art deep learning-based stress determinator provides good performance with respect to its model prediction accuracy.
• Score: 5.564705758320338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decoding neurons to extract information from transmission and employ them into other use is the goal of neuroscientists' study. Due to that the field of neuroscience is utilizing the traditional methods presently, we hence combine the state-of-the-art deep learning techniques with the theory of neuron decoding to discuss its potential of accomplishment. Besides, the stress level that is related to neuron activity in hippocampus is statistically examined as well. The experiments suggest that our state-of-the-art deep learning-based stress determinator provides good performance with respect to its model prediction accuracy and additionally, there is strong evidence against equivalence of mouse stress level under diverse environments.

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)
• Towards a Foundation Model for Brain Age Prediction using coVariance Neural Networks [102.75954614946258]
  Increasing brain age with respect to chronological age can reflect increased vulnerability to neurodegeneration and cognitive decline. NeuroVNN is pre-trained as a regression model on healthy population to predict chronological age. NeuroVNN adds anatomical interpretability to brain age and has a scale-free' characteristic that allows its transference to datasets curated according to any arbitrary brain atlas.
  arXiv  Detail & Related papers  (2024-02-12T14:46:31Z)
• Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data [3.46029409929709]
  State-of-the-art systems neuroscience experiments yield large-scale multimodal data, and these data sets require new tools for analysis. Inspired by the success of large pretrained models in vision and language domains, we reframe the analysis of large-scale, cellular-resolution neuronal spiking data into an autoregressive generation problem. We first trained Neuroformer on simulated datasets, and found that it both accurately predicted intrinsically simulated neuronal circuit activity, and also inferred the underlying neural circuit connectivity, including direction.
  arXiv  Detail & Related papers  (2023-10-31T20:17:32Z)
• Constraints on the design of neuromorphic circuits set by the properties of neural population codes [61.15277741147157]
  In the brain, information is encoded, transmitted and used to inform behaviour. Neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain.
  arXiv  Detail & Related papers  (2022-12-08T15:16:04Z)
• Modeling cognitive load as a self-supervised brain rate with electroencephalography and deep learning [2.741266294612776]
  This research presents a novel self-supervised method for mental workload modelling from EEG data. The method is a convolutional recurrent neural network trainable with spatially preserving spectral topographic head-maps from EEG data to fit the brain rate variable. Findings point to the existence of quasi-stable blocks of learnt high-level representations of cognitive activation because they can be induced through convolution and seem not to be dependent on each other over time, intuitively matching the non-stationary nature of brain responses.
  arXiv  Detail & Related papers  (2022-09-21T07:44:21Z)
• Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps [75.84770193489639]
  We examine the impact of test loss, training corpus and model architecture on the prediction of functional Magnetic Resonance Imaging timecourses of participants listening to an audiobook. We find that untrained versions of each model already explain significant amount of signal in the brain by capturing similarity in brain responses across identical words. We suggest good practices for future studies aiming at explaining the human language system using neural language models.
  arXiv  Detail & Related papers  (2022-07-07T15:37:17Z)
• Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations [60.47807856873544]
  A fundamental goal in neuroscience is to understand the relationship between neural activity and behavior. We generated a new multimodal dataset consisting of the spontaneous behaviors generated by fruit flies. This dataset and our new set of augmentations promise to accelerate the application of self-supervised learning methods in neuroscience.
  arXiv  Detail & Related papers  (2021-11-29T15:27:51Z)
• Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi [77.34726150561087]
  We investigate the potential of Intel's fifth generation neuromorphic chip - Loihi' Loihi is based on the novel idea of Spiking Neural Networks (SNNs) emulating the neurons in the brain. We find that Loihi replicates classical simulations very efficiently and scales notably well in terms of both time and energy performance as the networks get larger.
  arXiv  Detail & Related papers  (2021-09-22T16:52:51Z)
• On the Evolution of Neuron Communities in a Deep Learning Architecture [0.7106986689736827]
  This paper examines the neuron activation patterns of deep learning-based classification models. We show that both the community quality (modularity) and entropy are closely related to the deep learning models' performances.
  arXiv  Detail & Related papers  (2021-06-08T21:09:55Z)
• Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE [10.529943544385585]
  We propose a method that integrates key ingredients from latent models and traditional neural encoding models. Our method, pi-VAE, is inspired by recent progress on identifiable variational auto-encoder. We validate pi-VAE using synthetic data, and apply it to analyze neurophysiological datasets from rat hippocampus and macaque motor cortex.
  arXiv  Detail & Related papers  (2020-11-09T22:00:38Z)
• Deep Reinforcement Learning and its Neuroscientific Implications [19.478332877763417]
  The emergence of powerful artificial intelligence is defining new research directions in neuroscience. Deep reinforcement learning (Deep RL) offers a framework for studying the interplay among learning, representation and decision-making. Deep RL offers a new set of research tools and a wide range of novel hypotheses.
  arXiv  Detail & Related papers  (2020-07-07T19:27:54Z)

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.