ArticlePLoS computational biology2025
Repetitive training enhances the pattern recognition capability of cultured neural networks.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Emergence of Deviance Detection in Cortical Cultures Through Maturation, Criticality, and Early Experience.The European journal of neuroscience · 2026Article
- Brain-Inspired Multisensory Learning: A Systematic Review of Neuroplasticity and Cognitive Outcomes in Adult Multicultural and Second Language Acquisition.Biomimetics (Basel, Switzerland) · 2025Review
- Biomimetic Visual Information Spatiotemporal Encoding Method for In Vitro Biological Neural Networks.Biomimetics (Basel, Switzerland) · 2025Article
- From microelectrode arrays to all-optical and multimodal neural interfaces: emerging platforms for spatiotemporal interrogation ofFrontiers in synaptic neuroscience · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Cultured neural networks in vitro have demonstrated the biocomputing capability to recognize patterns. However, the underlying mechanisms behind information processing and pattern recognition remain less understood. Here, we developed an in vitro neural network integrated with microelectrode arrays (MEAs) to explore the network's classification capability and elucidate the mechanisms underlying this classification. After applying different stimulation patterns using MEAs, the network exhibited structural alterations and distinct electrical responses that recognized various stimulation patterns. Alongside the reshaping of network structures, repeated training increased recognition accuracy for each stimulation pattern. Additionally, it was reported for the first time that spontaneous networks after stimulation are more closely related to the structures of evoked networks. This work provides new insights into the structural changes underlying information processing and contributes to our understanding of how cultured neural networks respond to different patterns.
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Registered trials
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