Modeling Retinal Ganglion Cells with Neural Differential Equations

• URL: http://arxiv.org/abs/2511.18014v1
• Date: Sat, 22 Nov 2025 10:28:36 GMT
• Title: Modeling Retinal Ganglion Cells with Neural Differential Equations
• Abstract summary: This work explores Liquid Time-Constant Networks (LTCs) and Closed-form Continuous-time Networks (CfCs) for modeling retinal ganglion cell activity in tiger salamanders.<n>Compared to a convolutional baseline and an LSTM, both architectures achieved lower MAE, faster convergence, smaller model sizes, and favorable query times.<n>Their efficiency and adaptability make them well suited for scenarios with limited data and frequent retraining, such as edge deployments in vision prosthetics.
• Score: 1.7205106391379026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work explores Liquid Time-Constant Networks (LTCs) and Closed-form Continuous-time Networks (CfCs) for modeling retinal ganglion cell activity in tiger salamanders across three datasets. Compared to a convolutional baseline and an LSTM, both architectures achieved lower MAE, faster convergence, smaller model sizes, and favorable query times, though with slightly lower Pearson correlation. Their efficiency and adaptability make them well suited for scenarios with limited data and frequent retraining, such as edge deployments in vision prosthetics.

Related papers

• MUL-T: Decoding Spatial Cellular Architecture in Multiplexed Tissue Images [2.2726869886741388]
  MUL-T is a lightweight transformer framework that reframes tissue architecture as a masked contextual prediction task.<n>By learning contextualised embeddings without task-specific supervision, the model captures higher-order cellular interactions.<n>We evaluate MUL-T on several clinically relevant downstream tasks, including core-level tumour pattern classification, patient-level grading, PD-L1 positivity prediction, and cross-dataset treatment response prediction.
  arXiv  Detail & Related papers  (2026-07-30T11:15:12Z)
• MesaNet: Sequence Modeling by Locally Optimal Test-Time Training [67.45211108321203]
  We introduce a numerically stable, chunkwise parallelizable version of the recently proposed Mesa layer.<n>We show that optimal test-time training enables reaching lower language modeling perplexity and higher downstream benchmark performance than previous RNNs.
  arXiv  Detail & Related papers  (2025-06-05T16:50:23Z)
• Trajectory Flow Matching with Applications to Clinical Time Series Modeling [77.58277281319253]
  Trajectory Flow Matching (TFM) trains a Neural SDE in a simulation-free manner, bypassing backpropagation through the dynamics.<n>We demonstrate improved performance on three clinical time series datasets in terms of absolute performance and uncertainty prediction.
  arXiv  Detail & Related papers  (2024-10-28T15:54:50Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Deep Latent State Space Models for Time-Series Generation [68.45746489575032]
  We propose LS4, a generative model for sequences with latent variables evolving according to a state space ODE. Inspired by recent deep state space models (S4), we achieve speedups by leveraging a convolutional representation of LS4. We show that LS4 significantly outperforms previous continuous-time generative models in terms of marginal distribution, classification, and prediction scores on real-world datasets.
  arXiv  Detail & Related papers  (2022-12-24T15:17:42Z)
• Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study [1.1602089225841632]
  We compare recurrent neural networks (RNNs) with different types of gated cells for forecasting time series with multiple seasonality. The proposed RNN produces both point forecasts and predictive intervals (PIs) for them. An empirical study concerning short-term electrical load forecasting for 35 European countries confirmed that the new gated cells with dilation and attention performed best.
  arXiv  Detail & Related papers  (2022-03-17T08:47:49Z)
• ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting [1.4502611532302039]
  Short-term load forecasting (STLF) is challenging due to complex time series (TS) This paper proposes a novel hybrid hierarchical deep learning model that deals with multiple seasonality. It combines exponential smoothing (ES) and a recurrent neural network (RNN)
  arXiv  Detail & Related papers  (2021-12-05T19:38:42Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)
• Latent Time-Adaptive Drift-Diffusion Model [0.0]
  We present the latent time-adaptive drift-diffusion model (LTDDM), a model for animal learning of timing that exhibits behavioural properties consistent with experimental data from animals. It is shown how LTDDM can learn these events time series orders of magnitude faster than recurrent neural networks.
  arXiv  Detail & Related papers  (2021-06-04T22:18:16Z)
• Sentiment Analysis Using Simplified Long Short-term Memory Recurrent Neural Networks [1.5146765382501612]
  We perform sentiment analysis on a GOP Debate Twitter dataset. To speed up training and reduce the computational cost and time, six different parameter reduced slim versions of the LSTM model are proposed.
  arXiv  Detail & Related papers  (2020-05-08T12:50:10Z)
• A Hybrid Residual Dilated LSTM end Exponential Smoothing Model for Mid-Term Electric Load Forecasting [1.1602089225841632]
  The model combines exponential smoothing (ETS), advanced Long Short-Term Memory (LSTM) and ensembling. A simulation study performed on the monthly electricity demand time series for 35 European countries confirmed the high performance of the proposed model.
  arXiv  Detail & Related papers  (2020-03-29T10:53:50Z)
• Convolutional Tensor-Train LSTM for Spatio-temporal Learning [116.24172387469994]
  We propose a higher-order LSTM model that can efficiently learn long-term correlations in the video sequence. This is accomplished through a novel tensor train module that performs prediction by combining convolutional features across time. Our results achieve state-of-the-art performance-art in a wide range of applications and datasets.
  arXiv  Detail & Related papers  (2020-02-21T05:00:01Z)

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.