ArticleAdvances in neural information processing systems2019
Universality and individuality in neural dynamics across large populations of recurrent networks.
Article in Advances in neural information processing systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed.
- Pretraining of Embodied Recurrent Networks Bridges the Gap Between Artificial and Cortical Neural Activities.Biomimetics (Basel, Switzerland) · 2026Article
- Inferring brain-wide interactions using data-constrained recurrent neural network models.Neuron · 2026Article
- Data-driven reduced modeling of neural dynamics.Nature communications · 2026Article
- Neural timescales from a computational perspective.Nature neuroscience · 2026Review
- Comparing Dynamical Models Through Diffeomorphic Vector Field Alignment.Neural computation · 2026Article
- Illuminating the black box of reservoir computing.Scientific reports · 2026Article
- Structure, disorder, and dynamics in task-trained recurrent neural circuits.bioRxiv : the preprint server for biology · 2026Article
- Emergent representations of graphical structure in mechanistic neural models of causal judgment.bioRxiv : the preprint server for biology · 2026Article
- Exploring neural manifolds across a wide range of intrinsic dimensions.PLoS computational biology · 2026Article
- Neuromodulators Generate Multiple Context-Relevant Behaviors in Recurrent Neural Networks.Neural computation · 2026Article
- InputDSA : Demixing then comparing recurrent and externally driven dynamics.... International Conference on Learning Representations · 2026Article
- Structural and dynamical strategies to prevent runaway excitation in reservoir computing.Frontiers in computational neuroscience · 2026Review
- Article
- Task-Parametrized Dynamics: Representation of Time and Decisions in Recurrent Neural Networks.bioRxiv : the preprint server for biology · 2025Article
- Embodied sensorimotor control: computational modeling of the neural control of movement.ArXiv · 2025Article
- Cooperative coding of continuous variables in networks with sparsity constraint.PLoS computational biology · 2025Article
- MARBLE: interpretable representations of neural population dynamics using geometric deep learning.Nature methods · 2025Article
- Control of Inhibition-Stabilized Oscillations in Wilson-Cowan Networks with Homeostatic Plasticity.Entropy (Basel, Switzerland) · 2025Article
- Dynamic tracking of objects in the macaque dorsomedial frontal cortex.Nature communications · 2025Article
- Universal differential equations as a unifying modeling language for neuroscience.Frontiers in computational neuroscience · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Task-based modeling with recurrent neural networks (RNNs) has emerged as a popular way to infer the computational function of different brain regions. These models are quantitatively assessed by comparing the low-dimensional neural representations of the model with the brain, for example using canonical correlation analysis (CCA). However, the nature of the detailed neurobiological inferences one can draw from such efforts remains elusive. For example, to what extent does training neural networks to solve common tasks uniquely determine the network dynamics, independent of modeling architectural choices? Or alternatively, are the learned dynamics highly sensitive to different model choices? Knowing the answer to these questions has strong implications for whether and how we should use task-based RNN modeling to understand brain dynamics. To address these foundational questions, we study populations of thousands of networks, with commonly used RNN architectures, trained to solve neuroscientifically motivated tasks and characterize their nonlinear dynamics. We find the geometry of the RNN representations can be highly sensitive to different network architectures, yielding a cautionary tale for measures of similarity that rely on representational geometry, such as CCA. Moreover, we find that while the geometry of neural dynamics can vary greatly across architectures, the underlying computational scaffold-the topological structure of fixed points, transitions between them, limit cycles, and linearized dynamics-often appears universal across all architectures.
Identifiers
32782422PMC7416639What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.