Time-Dependent VAE for Building Latent Representations from Visual Neural Activity with Complex Dynamics

• URL: http://arxiv.org/abs/2408.07908v2
• Date: Wed, 2 Oct 2024 16:26:17 GMT
• Title: Time-Dependent VAE for Building Latent Representations from Visual Neural Activity with Complex Dynamics
• Authors: Liwei Huang, ZhengYu Ma, Liutao Yu, Huihui Zhou, Yonghong Tian,
• Abstract summary: TiDeSPL-VAE can effectively analyze complex visual neural activity and model temporal relationships in a natural way. Results show that our model not only yields the best decoding performance on naturalistic scenes/movies but also extracts explicit neural dynamics.
• Score: 25.454851828755054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Seeking high-quality representations with latent variable models (LVMs) to reveal the intrinsic correlation between neural activity and behavior or sensory stimuli has attracted much interest. Most work has focused on analyzing motor neural activity that controls clear behavioral traces and has modeled neural temporal relationships in a way that does not conform to natural reality. For studies of visual brain regions, naturalistic visual stimuli are high-dimensional and time-dependent, making neural activity exhibit intricate dynamics. To cope with such conditions, we propose Time-Dependent Split VAE (TiDeSPL-VAE), a sequential LVM that decomposes visual neural activity into two latent representations while considering time dependence. We specify content latent representations corresponding to the component of neural activity driven by the current visual stimulus, and style latent representations corresponding to the neural dynamics influenced by the organism's internal state. To progressively generate the two latent representations over time, we introduce state factors to construct conditional distributions with time dependence and apply self-supervised contrastive learning to shape them. By this means, TiDeSPL-VAE can effectively analyze complex visual neural activity and model temporal relationships in a natural way. We compare our model with alternative approaches on synthetic data and neural data from the mouse visual cortex. The results show that our model not only yields the best decoding performance on naturalistic scenes/movies but also extracts explicit neural dynamics, demonstrating that it builds latent representations more relevant to visual stimuli.

Related papers

• Modeling dynamic neural activity by combining naturalistic video stimuli and stimulus-independent latent factors [5.967290675400836]
  We propose a probabilistic model that incorporates video inputs along with stimulus-independent latent factors to capture variability in neuronal responses. After training and testing our model on mouse V1 neuronal responses, we found that it outperforms video-only models in terms of log-likelihood. We find that the learned latent factors strongly correlate with mouse behavior, although the model was trained without behavior data.
  arXiv  Detail & Related papers  (2024-10-21T16:01:39Z)
• Artificial Kuramoto Oscillatory Neurons [65.16453738828672]
  We introduce Artificial Kuramotoy Neurons (AKOrN) as a dynamical alternative to threshold units. We show that this idea provides performance improvements across a wide spectrum of tasks. We believe that these empirical results show the importance of our assumptions at the most basic neuronal level of neural representation.
  arXiv  Detail & Related papers  (2024-10-17T17:47:54Z)
• Neural Dynamics Model of Visual Decision-Making: Learning from Human Experts [28.340344705437758]
  We implement a comprehensive visual decision-making model that spans from visual input to behavioral output. Our model aligns closely with human behavior and reflects neural activities in primates. A neuroimaging-informed fine-tuning approach was introduced and applied to the model, leading to performance improvements.
  arXiv  Detail & Related papers  (2024-09-04T02:38:52Z)
• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• On the Trade-off Between Efficiency and Precision of Neural Abstraction [62.046646433536104]
  Neural abstractions have been recently introduced as formal approximations of complex, nonlinear dynamical models. We employ formal inductive synthesis procedures to generate neural abstractions that result in dynamical models with these semantics.
  arXiv  Detail & Related papers  (2023-07-28T13:22:32Z)
• The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks [64.08042492426992]
  We introduce the Expressive Memory (ELM) neuron model, a biologically inspired model of a cortical neuron. Our ELM neuron can accurately match the aforementioned input-output relationship with under ten thousand trainable parameters. We evaluate it on various tasks with demanding temporal structures, including the Long Range Arena (LRA) datasets.
  arXiv  Detail & Related papers  (2023-06-14T13:34:13Z)
• Long-Range Feedback Spiking Network Captures Dynamic and Static Representations of the Visual Cortex under Movie Stimuli [25.454851828755054]
  There is limited insight into how the visual cortex represents natural movie stimuli that contain context-rich information. This work proposes the long-range feedback spiking network (LoRaFB-SNet), which mimics top-down connections between cortical regions. We present Time-Series Representational Similarity Analysis (TSRSA) to measure the similarity between model representations and visual cortical representations of mice.
  arXiv  Detail & Related papers  (2023-06-02T08:25:58Z)
• STNDT: Modeling Neural Population Activity with a Spatiotemporal Transformer [19.329190789275565]
  We introduce SpatioTemporal Neural Data Transformer (STNDT), an NDT-based architecture that explicitly models responses of individual neurons. We show that our model achieves state-of-the-art performance on ensemble level in estimating neural activities across four neural datasets.
  arXiv  Detail & Related papers  (2022-06-09T18:54:23Z)
• 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)
• Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity [33.06823702945747]
  We introduce a novel unsupervised approach for learning disentangled representations of neural activity called Swap-VAE. Our approach combines a generative modeling framework with an instance-specific alignment loss. We show that it is possible to build representations that disentangle neural datasets along relevant latent dimensions linked to behavior.
  arXiv  Detail & Related papers  (2021-11-03T16:39:43Z)
• Continuous Learning and Adaptation with Membrane Potential and Activation Threshold Homeostasis [91.3755431537592]
  This paper presents the Membrane Potential and Activation Threshold Homeostasis (MPATH) neuron model. The model allows neurons to maintain a form of dynamic equilibrium by automatically regulating their activity when presented with input. Experiments demonstrate the model's ability to adapt to and continually learn from its input.
  arXiv  Detail & Related papers  (2021-04-22T04:01:32Z)

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.