ArticleProceedings of the National Academy of Sciences of the United States of America2022
Pruning recurrent neural networks replicates adolescent changes in working memory and reinforcement learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 29 citations in OpenAlex.
- Early psychosocial deprivation alters the refinement of neural dynamics across adolescence.Proceedings of the National Academy of Sciences of the United States of America · 2026Trial
- Trajectories of Response Inhibition Development in Adolescence.bioRxiv : the preprint server for biology · 2026Article
- A computational framework for epigenetic plasticity in memory.Brain : a journal of neurology · 2026Review
- Synaptic pruning, myelination and the emergence of psychiatric disorders in late adolescence.bioRxiv : the preprint server for biology · 2026Article
- Age-Related Differences in Response Time Across Adolescence Reflect Premotor, but Not Motor, Processing Speed.Psychophysiology · 2026Article
- Population-level encoding of task information is stronger in the adolescent compared to adult frontal cortex.Developmental cognitive neuroscience · 2026Article
- The contributions of biological maturity and experience to fine motor development in adolescence.Scientific reports · 2026Article
- The Impact of Nutrition, Genetics, Environment, and Common Issues on Adolescent Growth and Development.Advances in experimental medicine and biology · 2026Review
- Longitudinal measures of monkey brain structure and activity through adolescence predict cognitive maturation.Nature neuroscience · 2025Article
- Developmental decorrelation of local cortical activity through adolescence supports high-dimensional encoding and working memory.Developmental cognitive neuroscience · 2025Article
- More than just a phase: adolescence as a window into how the brain generates behavior.Current directions in psychological science · 2025Article
- Shaping dynamical neural computations using spatiotemporal constraints.Biochemical and biophysical research communications · 2024Review
- Cognitive Control and Neural Activity during Human Development: Evidence for Synaptic Pruning.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Review
- Navigating Pubertal Goldilocks: The Optimal Pace for Hierarchical Brain Organization.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Article
- Changes in Behavior and Neural Dynamics across Adolescent Development.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Article
- Attractor dynamics reflect decision confidence in macaque prefrontal cortex.Nature neuroscience · 2023Article
- Strength of Excitatory Inputs to Layer 3 Pyramidal Neurons During Synaptic Pruning in the Monkey Prefrontal Cortex: Relevance for the Pathogenesis of Schizophrenia.Biological psychiatry · 2023Article
- The neuroconnectionist research programme.Nature reviews. Neuroscience · 2023Review
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Abstract
Adolescent development is characterized by an improvement in multiple cognitive processes. While performance on cognitive operations improves during this period, the ability to learn new skills quickly, for example, a new language, decreases. During this time, there is substantial pruning of excitatory synapses in cortex and specifically in prefrontal cortex. We have trained a series of recurrent neural networks to solve a working memory task and a reinforcement learning (RL) task. Performance on both of these tasks is known to improve during adolescence. After training, we pruned the networks by removing weak synapses. Pruning was done incrementally, and the networks were retrained during pruning. We found that pruned networks trained on the working memory task were more resistant to distraction. The pruned RL networks were able to produce more accurate value estimates and also make optimal choices more consistently. Both results are consistent with developmental improvements on these tasks. Pruned networks, however, learned some, but not all, new problems more slowly. Thus, improvements in task performance can come at the cost of flexibility. Our results show that overproduction and subsequent pruning of synapses is a computationally advantageous approach to building a competent brain.
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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.