# Learning Time-Invariant Representations for Individual Neurons from Population Dynamics

arXiv: https://arxiv.org/abs/2311.02258 · HTML: https://mlsanity.com/paper/2311.02258/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 3 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Learnable latent embeddings for joint behavioral and neural analysis](https://mlsanity.com/paper/2204.00673/) (2022)
- [Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity](https://mlsanity.com/paper/2109.04463/) (2021)
- [Representation learning for neural population activity with Neural Data Transformers](https://mlsanity.com/paper/2108.01210/) (2021)
- [Linear dynamical neural population models through nonlinear embeddings](https://mlsanity.com/paper/1605.08454/) (2016)
- [A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements](https://mlsanity.com/paper/1612.07846/) (2016)
- [STNDT: Modeling Neural Population Activity with a Spatiotemporal Transformer](https://mlsanity.com/paper/2206.04727/) (2022)
- [Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains](https://mlsanity.com/paper/1604.03053/) (2016)
- [Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers](https://mlsanity.com/paper/2206.06131/) (2022)
- [Automatic discovery of cell types and microcircuitry from neural connectomics](https://mlsanity.com/paper/1407.4137/) (2014)
- [Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification](https://mlsanity.com/paper/1712.00310/) (2017)
- [A coupled autoencoder approach for multi-modal analysis of cell types](https://mlsanity.com/paper/1911.05663/) (2019)

## Built on by (most cited first, 8 of 8)

- [SPINT: Spatial Permutation-Invariant Neural Transformer for Consistent Intracortical Motor Decoding](https://mlsanity.com/paper/2507.08402/) (2025)
- [BLEND: Behavior-guided Neural Population Dynamics Modeling via Privileged Knowledge Distillation](https://mlsanity.com/paper/2410.13872/) (2024)
- [Neuron Platonic Intrinsic Representation From Dynamics Using Contrastive Learning](https://mlsanity.com/paper/2502.10425/) (2025)
- [NOBLE -- Neural Operator with Biologically-informed Latent Embeddings to Capture Experimental Variability in Biological Neuron Models](https://mlsanity.com/paper/2506.04536/) (2025)
- [Know Thyself by Knowing Others: Learning Neuron Identity from Population Context](https://mlsanity.com/paper/2512.01199/) (2025)
- [Fast Fourier Correlation is a Highly Efficient and Accurate Feature Attribution Algorithm from the Perspective of Control Theory and Game Theory](https://mlsanity.com/paper/2504.02016/) (2025)
- [Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights](https://mlsanity.com/paper/2411.18343/) (2024)
- [A Benchmark Analysis of Graph and Non-Graph Methods for Caenorhabditis Elegans Neuron Classification](https://mlsanity.com/paper/2603.02241/) (2026)

## Abstract (arXiv)

Neurons can display highly variable dynamics. While such variability presumably supports the wide range of behaviors generated by the organism, their gene expressions are relatively stable in the adult brain. This suggests that neuronal activity is a combination of its time-invariant identity and the inputs the neuron receives from the rest of the circuit. Here, we propose a self-supervised learning based method to assign time-invariant representations to individual neurons based on permutation-, and population size-invariant summary of population recordings. We fit dynamical models to neuronal activity to learn a representation by considering the activity of both the individual and the neighboring population. Our self-supervised approach and use of implicit representations enable robust inference against imperfections such as partial overlap of neurons across sessions, trial-to-trial variability, and limited availability of molecular (transcriptomic) labels for downstream supervised tasks. We demonstrate our method on a public multimodal dataset of mouse cortical neuronal activity and transcriptomic labels. We report > 35% improvement in predicting the transcriptomic subclass identity and > 20% improvement in predicting class identity with respect to the state-of-the-art.