ArticleNeural computation2026
Neuromodulators Generate Multiple Context-Relevant Behaviors in Recurrent Neural Networks.
Article in Neural computation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Diffusion of neuromodulators for temporal credit assignment.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Structured flexibility in recurrent neural networks via neuromodulation.Advances in neural information processing systems · 2024Article
- Exploring strategy differences between humans and monkeys with recurrent neural networks.PLoS computational biology · 2023Article
- Recent Advances at the Interface of Neuroscience and Artificial Neural Networks.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Review
- Neocortical layer 5 subclasses: From cellular properties to roles in behavior.Frontiers in synaptic neuroscience · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Neuromodulators are critical controllers of neural states, with dysfunctions linked to various neuropsychiatric disorders. Although many biological aspects of neuromodulation have been studied, the computational principles underlying how neuromodulation of distributed neural populations controls brain states remain unclear. In contrast to external contextual inputs, neuromodulation can act as a single scalar signal that is broadcast to a vast population of neurons. We model the modulation of synaptic weight in a recurrent neural network model and show that neuromodulators can dramatically alter the function of a network, even when highly simplified. We find that under structural constraints like those in brains, this provides a fundamental mechanism that can increase the computational capability and flexibility of a neural network. Diffuse synaptic weight modulation enables storage of multiple memories using a common set of synapses that are able to generate diverse, even diametrically opposed, behaviors. Our findings help explain how neuromodulators unlock specific behaviors by creating task-specific hyperchannels in neural activity space and motivate more flexible, compact and capable machine learning architectures.
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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.