# Neural mechanisms underlying the temporal organization of naturalistic animal behavior

arXiv: https://arxiv.org/abs/2203.02151 · HTML: https://mlsanity.com/paper/2203.02151/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2022.

## Built on by (most cited first, 4 of 4)

- [Long Sequence Hopfield Memory](https://mlsanity.com/paper/2306.04532/) (2023)
- [Inverse Reinforcement Learning with Switching Rewards and History Dependency for Characterizing Animal Behaviors](https://mlsanity.com/paper/2501.12633/) (2025)
- [Expressivity of Neural Networks with Random Weights and Learned Biases](https://mlsanity.com/paper/2407.00957/) (2024)
- [Multi-Scale Temporal Homeostasis Enables Efficient and Robust Neural Networks](https://mlsanity.com/paper/2602.07009/) (2026)

## Abstract (arXiv)

Naturalistic animal behavior exhibits a strikingly complex organization in the temporal domain, whose variability stems from at least three sources: hierarchical, contextual, and stochastic. What are the neural mechanisms and computational principles generating such complex temporal features? In this review, we provide a critical assessment of the existing behavioral and neurophysiological evidence for these sources of temporal variability in naturalistic behavior. We crystallize recent studies which converge on an emergent mechanistic theory of temporal variability based on attractor neural networks and metastable dynamics, arising from the coordinated interactions between mesoscopic neural circuits. We highlight the crucial role played by structural heterogeneities and by noise arising in mesoscopic circuits. We assess the shortcomings and missing links in the current theoretical and experimental literature and propose new directions of investigations to fill these gaps.