ArticleNature neuroscience2024
Flexible multitask computation in recurrent networks utilizes shared dynamical motifs.
Article in Nature neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers.
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Who cites it
120 citing papers in PubMed.
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- Structured experience shapes strategy learning and neural dynamics in the medial entorhinal cortex.Nature neuroscience · 2026Article
- Reusable modular architecture enables flexible cognitive operations in the mouse brain and artificial recurrent networks.Nature neuroscience · 2026Article
- Discrete synaptic states and context-modulated readouts support continual learning.bioRxiv : the preprint server for biology · 2026Article
- The compositionality continuum as a principle for studying the neural basis of intelligence.Nature neuroscience · 2026Review
- Synaptic-state-dependent plasticity routes learning signals in recurrent circuits.bioRxiv : the preprint server for biology · 2026Article
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- Selective perturbation of mirror and non-mirror neurons in an in silico model of the action observation network.iScience · 2026Article
- Sensorimotor remapping drives task specialization in prefrontal cortex.Nature communications · 2026Article
- Task-Parametrized dynamics: Representation of time and decisions in recurrent neural networks.Journal of computational neuroscience · 2026Article
- Pretraining of Embodied Recurrent Networks Bridges the Gap Between Artificial and Cortical Neural Activities.Biomimetics (Basel, Switzerland) · 2026Article
- Learning shapes neural geometry in the primate prefrontal cortex.Nature neuroscience · 2026Article
- Decoding behavior with minimal and interpretable agent models.PLoS computational biology · 2026Article
- Data-driven reduced modeling of neural dynamics.Nature communications · 2026Article
- Dynamically Updated Task Rules Reshape Sensory Processing Across Cortical Hierarchies of the Posterior Parietal Cortex and Primary Auditory Cortex.Neuroscience bulletin · 2026Article
- Article
- Sparse component analysis: A method that uncovers separable computations within neural population activity.Neuron · 2026Article
- The Dynamicist Landscape.Topics in cognitive science · 2026Article
- The curriculum effect in visual learning: The role of readout dimensionality.PLoS computational biology · 2026Article
- Disinhibitory signaling enables flexible coding of top-down information in cortical networks.PLoS biology · 2026Article
60 more citing papers are in PubMed but not listed here.
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Abstract
Flexible computation is a hallmark of intelligent behavior. However, little is known about how neural networks contextually reconfigure for different computations. In the present work, we identified an algorithmic neural substrate for modular computation through the study of multitasking artificial recurrent neural networks. Dynamical systems analyses revealed learned computational strategies mirroring the modular subtask structure of the training task set. Dynamical motifs, which are recurring patterns of neural activity that implement specific computations through dynamics, such as attractors, decision boundaries and rotations, were reused across tasks. For example, tasks requiring memory of a continuous circular variable repurposed the same ring attractor. We showed that dynamical motifs were implemented by clusters of units when the unit activation function was restricted to be positive. Cluster lesions caused modular performance deficits. Motifs were reconfigured for fast transfer learning after an initial phase of learning. This work establishes dynamical motifs as a fundamental unit of compositional computation, intermediate between neuron and network. As whole-brain studies simultaneously record activity from multiple specialized systems, the dynamical motif framework will guide questions about specialization and generalization.
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