# Biological neurons act as generalization filters in reservoir computing

arXiv: https://arxiv.org/abs/2210.02913 · HTML: https://mlsanity.com/paper/2210.02913/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 75 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks](https://mlsanity.com/paper/2007.15951/) (2020)
- [Physical reservoir computing -- An introductory perspective](https://mlsanity.com/paper/2005.00992/) (2020)
- [The physics of brain network structure, function, and control](https://mlsanity.com/paper/1809.06441/) (2018)
- [Exploiting short-term memory in soft body dynamics as a computational resource](https://mlsanity.com/paper/1406.4929/) (2014)
- [Optimal modularity and memory capacity of neural reservoirs](https://mlsanity.com/paper/1706.06511/) (2017)

## Built on by (most cited first, 12 of 12)

- [An introduction to reservoir computing](https://mlsanity.com/paper/2412.13212/) (2024)
- [From Neurons to Computation: Biological Reservoir Computing for Pattern Recognition](https://mlsanity.com/paper/2505.03510/) (2025)
- [Growing Reservoirs with Developmental Graph Cellular Automata](https://mlsanity.com/paper/2508.08091/) (2025)
- [Modularity is the Bedrock of Natural and Artificial Intelligence](https://mlsanity.com/paper/2602.18960/) (2026)
- [A Quantifiable Information-Processing Hierarchy Provides a Necessary Condition for Detecting Agency](https://mlsanity.com/paper/2601.03498/) (2026)
- [Neuro-Inspired Visual Pattern Recognition via Biological Reservoir Computing](https://mlsanity.com/paper/2602.05737/) (2026)
- [Information processing via human soft tissue](https://mlsanity.com/paper/2305.14366/) (2023)
- [Boosting Reservoir Computing with Brain-inspired Adaptive Dynamics](https://mlsanity.com/paper/2504.12480/) (2025)
- [Multi-Model Ensemble and Reservoir Computing for River Discharge Prediction in Ungauged Basins](https://mlsanity.com/paper/2507.18423/) (2025)
- [Insect-Wing Structured Microfluidic System for Reservoir Computing](https://mlsanity.com/paper/2508.10915/) (2025)
- [Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation](https://mlsanity.com/paper/2605.13315/) (2026)
- [Connectome-to-Function: Conditional Generative Latent Representations for Reservoir Computing](https://mlsanity.com/paper/2609.06093/) (2026)

## Abstract (arXiv)

Reservoir computing is a machine learning paradigm that transforms the transient dynamics of high-dimensional nonlinear systems for processing time-series data. Although reservoir computing was initially proposed to model information processing in the mammalian cortex, it remains unclear how the non-random 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 fluorescent 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 modular BNNs can be used to classify static input patterns 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 ~1 s and finally show that this property can be exploited for spoken digit classification. Interestingly, BNN-based reservoirs allow transfer 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 input patterns 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 processing within BNNs and, simultaneously, build future expectations toward the realization of physical reservoir computing systems based on BNNs.