# Building population models for large-scale neural recordings: opportunities and pitfalls

arXiv: https://arxiv.org/abs/2102.01807 · HTML: https://mlsanity.com/paper/2102.01807/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 121 references that are in the map.
- First posted on arXiv 3 Feb 2021.

## Builds on (most cited first, 17 of 17)

- [Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE](https://mlsanity.com/paper/2011.04798/) (2020)
- [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)
- [Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains](https://mlsanity.com/paper/1604.03053/) (2016)
- [Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters](https://mlsanity.com/paper/1410.6031/) (2014)
- [Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks](https://mlsanity.com/paper/1907.00650/) (2019)
- [Interpretable Nonlinear Dynamic Modeling of Neural Trajectories](https://mlsanity.com/paper/1608.06546/) (2016)
- [LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations](https://mlsanity.com/paper/1905.07499/) (2019)
- [A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons](https://mlsanity.com/paper/2006.06497/) (2020)
- [Correlations and functional connections in a population of grid cells](https://mlsanity.com/paper/1405.0044/) (2014)
- [A study of dependency features of spike trains through copulas](https://mlsanity.com/paper/1903.08460/) (2019)
- [Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference](https://mlsanity.com/paper/1905.12375/) (2019)
- [Inference of a mesoscopic population model from population spike trains](https://mlsanity.com/paper/1910.01618/) (2019)
- [Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships](https://mlsanity.com/paper/2008.01007/) (2020)
- [A tractable method for describing complex couplings between neurons and population rate](https://mlsanity.com/paper/1606.08889/) (2016)
- [Predicting how and when hidden neurons skew measured synaptic interactions](https://mlsanity.com/paper/1702.00865/) (2017)
- [Scaling Spike Detection and Sorting for Next Generation Electrophysiology](https://mlsanity.com/paper/1809.01051/) (2018)

## Built on by (most cited first, 9 of 9)

- [A Unified, Scalable Framework for Neural Population Decoding](https://mlsanity.com/paper/2310.16046/) (2023)
- [Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity](https://mlsanity.com/paper/2109.04463/) (2021)
- [Neural Encoding and Decoding at Scale](https://mlsanity.com/paper/2504.08201/) (2025)
- [Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation](https://mlsanity.com/paper/2202.06159/) (2022)
- [Self-supervised contrastive learning performs non-linear system identification](https://mlsanity.com/paper/2410.14673/) (2024)
- [Expressive architectures enhance interpretability of dynamics-based neural population models](https://mlsanity.com/paper/2212.03771/) (2022)
- [Adaptive Intelligence: leveraging insights from adaptive behavior in animals to build flexible AI systems](https://mlsanity.com/paper/2411.15234/) (2024)
- [Generative Modeling of Neural Dynamics via Latent Stochastic Differential Equations](https://mlsanity.com/paper/2412.12112/) (2024)
- [Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles](https://mlsanity.com/paper/2205.09829/) (2022)

## Abstract (arXiv)

Modern recording technologies now enable simultaneous recording from large numbers of neurons. This has driven the development of new statistical models for analyzing and interpreting neural population activity. Here we provide a broad overview of recent developments in this area. We compare and contrast different approaches, highlight strengths and limitations, and discuss biological and mechanistic insights that these methods provide.