# Neural timescales from a computational perspective

arXiv: https://arxiv.org/abs/2409.02684 · HTML: https://mlsanity.com/paper/2409.02684/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 234 references that are in the map.
- First posted on arXiv 4 Sep 2024.

## Builds on (most cited first, 30 of 48)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Long Range Arena: A Benchmark for Efficient Transformers](https://mlsanity.com/paper/2011.04006/) (2020)
- [Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers](https://mlsanity.com/paper/2110.13985/) (2021)
- [The frontier of simulation-based inference](https://mlsanity.com/paper/1911.01429/) (2019)
- [Resurrecting Recurrent Neural Networks for Long Sequences](https://mlsanity.com/paper/2303.06349/) (2023)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Liquid Structural State-Space Models](https://mlsanity.com/paper/2209.12951/) (2022)
- [What Makes Convolutional Models Great on Long Sequence Modeling?](https://mlsanity.com/paper/2210.09298/) (2022)
- [Universality and individuality in neural dynamics across large populations of recurrent networks](https://mlsanity.com/paper/1907.08549/) (2019)
- [Attractor and integrator networks in the brain](https://mlsanity.com/paper/2112.03978/) (2021)
- [Can recurrent neural networks warp time?](https://mlsanity.com/paper/1804.11188/) (2018)
- [Linking connectivity, dynamics and computations in low-rank recurrent neural networks](https://mlsanity.com/paper/1711.09672/) (2017)
- [Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics](https://mlsanity.com/paper/1906.10720/) (2019)
- [Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems](https://mlsanity.com/paper/2207.02542/) (2022)
- [Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors](https://mlsanity.com/paper/2310.02980/) (2023)
- [Artificial neural networks for neuroscientists: A primer](https://mlsanity.com/paper/2006.01001/) (2020)
- [Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis](https://mlsanity.com/paper/2306.10168/) (2023)
- [Theory of gating in recurrent neural networks](https://mlsanity.com/paper/2007.14823/) (2020)
- [Gated recurrent units viewed through the lens of continuous time dynamical systems](https://mlsanity.com/paper/1906.01005/) (2019)
- [Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation](https://mlsanity.com/paper/2305.15208/) (2023)
- [Inferring stochastic low-rank recurrent neural networks from neural data](https://mlsanity.com/paper/2406.16749/) (2024)
- [Multi-timescale Representation Learning in LSTM Language Models](https://mlsanity.com/paper/2009.12727/) (2020)
- [Transition to chaos in random neuronal networks](https://mlsanity.com/paper/1508.06486/) (2015)
- [Measuring behavior across scales](https://mlsanity.com/paper/1712.05784/) (2017)
- [Are Vision Transformers More Data Hungry Than Newborn Visual Systems?](https://mlsanity.com/paper/2312.02843/) (2023)
- [Transition to chaos in random networks with cell-type-specific connectivity](https://mlsanity.com/paper/1407.2297/) (2014)
- [Contextual guidance: An integrated theory for astrocytes function in brain circuits and behavior](https://mlsanity.com/paper/2211.09906/) (2022)
- [Persistent learning signals and working memory without continuous attractors](https://mlsanity.com/paper/2308.12585/) (2023)

## Built on by (most cited first, 3 of 3)

- [Riemannian Geometry for the classification of brain states with intracortical brain-computer interfaces](https://mlsanity.com/paper/2504.05534/) (2025)
- [Fast dynamical similarity analysis](https://mlsanity.com/paper/2511.22828/) (2025)
- [Inferring Multi-Timescale Neural Dynamics with Switching Linear Dynamical Systems](https://mlsanity.com/paper/2610.01786/) (2026)

## Abstract (arXiv)

Neural activity fluctuates over a wide range of timescales within and across brain areas. Experimental observations suggest that diverse neural timescales reflect information in dynamic environments. However, how timescales are defined and measured from brain recordings vary across the literature. Moreover, these observations do not specify the mechanisms underlying timescale variations, nor whether specific timescales are necessary for neural computation and brain function. Here, we synthesize three directions where computational approaches can distill the broad set of empirical observations into quantitative and testable theories: We review (i) how different data analysis methods quantify timescales across distinct behavioral states and recording modalities, (ii) how biophysical models provide mechanistic explanations for the emergence of diverse timescales, and (iii) how task-performing networks and machine learning models uncover the functional relevance of neural timescales. This integrative computational perspective thus complements experimental investigations, providing a holistic view on how neural timescales reflect the relationship between brain structure, dynamics, and behavior.