STNDT: Modeling Neural Population Activity with a Spatiotemporal Transformer

• URL: http://arxiv.org/abs/2206.04727v1
• Date: Thu, 9 Jun 2022 18:54:23 GMT
• Title: STNDT: Modeling Neural Population Activity with a Spatiotemporal Transformer
• Authors: Trung Le and Eli Shlizerman
• Abstract summary: 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.
• Score: 19.329190789275565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling neural population dynamics underlying noisy single-trial spiking activities is essential for relating neural observation and behavior. A recent non-recurrent method - Neural Data Transformers (NDT) - has shown great success in capturing neural dynamics with low inference latency without an explicit dynamical model. However, NDT focuses on modeling the temporal evolution of the population activity while neglecting the rich covariation between individual neurons. In this paper we introduce SpatioTemporal Neural Data Transformer (STNDT), an NDT-based architecture that explicitly models responses of individual neurons in the population across time and space to uncover their underlying firing rates. In addition, we propose a contrastive learning loss that works in accordance with mask modeling objective to further improve the predictive performance. We show that our model achieves state-of-the-art performance on ensemble level in estimating neural activities across four neural datasets, demonstrating its capability to capture autonomous and non-autonomous dynamics spanning different cortical regions while being completely agnostic to the specific behaviors at hand. Furthermore, STNDT spatial attention mechanism reveals consistently important subsets of neurons that play a vital role in driving the response of the entire population, providing interpretability and key insights into how the population of neurons performs computation.

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)
• Exploring Behavior-Relevant and Disentangled Neural Dynamics with Generative Diffusion Models [2.600709013150986]
  Understanding the neural basis of behavior is a fundamental goal in neuroscience. Our approach, named BeNeDiff'', first identifies a fine-grained and disentangled neural subspace. It then employs state-of-the-art generative diffusion models to synthesize behavior videos that interpret the neural dynamics of each latent factor.
  arXiv  Detail & Related papers  (2024-10-12T18:28:56Z)
• BLEND: Behavior-guided Neural Population Dynamics Modeling via Privileged Knowledge Distillation [6.3559178227943764]
  We propose BLEND, a behavior-guided neural population dynamics modeling framework via privileged knowledge distillation. By considering behavior as privileged information, we train a teacher model that takes both behavior observations (privileged features) and neural activities (regular features) as inputs. A student model is then distilled using only neural activity.
  arXiv  Detail & Related papers  (2024-10-02T12:45:59Z)
• 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)
• 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)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• 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)
• Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time [15.648009434801885]
  Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and frequency of temporal sampling. Here we demonstrate that it is possible to obtain super-resolution in neuronal time series by exploiting relationships among neurons.
  arXiv  Detail & Related papers  (2021-10-29T20:18:29Z)
• 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)
• Recurrent Neural Network Learning of Performance and Intrinsic Population Dynamics from Sparse Neural Data [77.92736596690297]
  We introduce a novel training strategy that allows learning not only the input-output behavior of an RNN but also its internal network dynamics. We test the proposed method by training an RNN to simultaneously reproduce internal dynamics and output signals of a physiologically-inspired neural model. Remarkably, we show that the reproduction of the internal dynamics is successful even when the training algorithm relies on the activities of a small subset of neurons.
  arXiv  Detail & Related papers  (2020-05-05T14:16:54Z)

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.