ArticleProceedings of the National Academy of Sciences of the United States of America2023
Biological neurons act as generalization filters in reservoir computing.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Microfluidic platforms for probing spontaneous functional recovery in hierarchically modular neuronal networks.Communications engineering · 2026Article
- Janus Synapses as Modular Neurointerfaces.ACS applied materials & interfaces · 2026Review
- Online supervised learning of temporal patterns in biological neural networks under feedback control.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Re-Purposing a Modular Origami Manipulator Into an Adaptive Physical Computer for Machine Learning and Robotic Perception.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Boosting reservoir computing with brain-inspired adaptive control of E-I balance.Nature communications · 2025Article
- Effects of external body vibrations on cognitive performance through brain-cardio-respiratory dynamics.Scientific reports · 2025Article
- Article
- Harnessing Intelligence from Brain Cells In Vitro.The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry · 2025Review
- Optogenetically enhanced physical reservoir computing withJournal of biomedical optics · 2025Article
- Biomimetic Visual Information Spatiotemporal Encoding Method for In Vitro Biological Neural Networks.Biomimetics (Basel, Switzerland) · 2025Article
- TheBiomimetics (Basel, Switzerland) · 2025Article
- Starting a synthetic biological intelligence lab from scratch.Patterns (New York, N.Y.) · 2025Review
- Computational memory capacity predicts aging and cognitive decline.Nature communications · 2025Article
- Article
- From microelectrode arrays to all-optical and multimodal neural interfaces: emerging platforms for spatiotemporal interrogation ofFrontiers in synaptic neuroscience · 2025Review
- Modular architecture confers robustness to damage and facilitates recovery in spiking neural networks modelingFrontiers in neuroscience · 2025Article
- Dynamic Network Plasticity and Sample Efficiency in Biological Neural Cultures: A Comparative Study with Deep Reinforcement Learning.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- Inferring structure of cortical neuronal networks from activity data: A statistical physics approach.PNAS nexus · 2025Article
- Opportunities and Challenges of Brain-on-a-Chip Interfaces.Cyborg and bionic systems (Washington, D.C.) · 2025Review
- Article
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8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reservoir computing is a machine learning paradigm that transforms the transient dynamics of high-dimensional nonlinear systems for processing time-series data. Although the paradigm was initially proposed to model information processing in the mammalian cortex, it remains unclear how the nonrandom network architecture, such as the modular architecture, in the cortex integrates with the biophysics of living neurons to characterize the function of biological neuronal networks (BNNs). Here, we used optogenetics and calcium imaging to record the multicellular responses of cultured BNNs and employed the reservoir computing framework to decode their computational capabilities. Micropatterned substrates were used to embed the modular architecture in the BNNs. We first show that the dynamics of modular BNNs in response to static inputs can be classified with a linear decoder and that the modularity of the BNNs positively correlates with the classification accuracy. We then used a timer task to verify that BNNs possess a short-term memory of several 100 ms and finally show that this property can be exploited for spoken digit classification. Interestingly, BNN-based reservoirs allow categorical learning, wherein a network trained on one dataset can be used to classify separate datasets of the same category. Such classification was not possible when the inputs were directly decoded by a linear decoder, suggesting that BNNs act as a generalization filter to improve reservoir computing performance. Our findings pave the way toward a mechanistic understanding of information representation within BNNs and build future expectations toward the realization of physical reservoir computing systems based on BNNs.
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