# Neuro-Inspired Visual Pattern Recognition via Biological Reservoir Computing

arXiv: https://arxiv.org/abs/2602.05737 · HTML: https://mlsanity.com/paper/2602.05737/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 67 references that are in the map.
- First posted on arXiv 5 Feb 2026.

## Builds on (most cited first, 14 of 14)

- [Hebbian Deep Learning Without Feedback](https://mlsanity.com/paper/2209.11883/) (2022)
- [Reservoir computing approaches for representation and classification of multivariate time series](https://mlsanity.com/paper/1803.07870/) (2018)
- [Biologically plausible deep learning -- but how far can we go with shallow networks?](https://mlsanity.com/paper/1905.04101/) (2019)
- [Deep Echo State Network (DeepESN): A Brief Survey](https://mlsanity.com/paper/1712.04323/) (2017)
- [Hebbian Semi-Supervised Learning in a Sample Efficiency Setting](https://mlsanity.com/paper/2103.09002/) (2021)
- [Biological neurons act as generalization filters in reservoir computing](https://mlsanity.com/paper/2210.02913/) (2022)
- [Highlighting the Importance of Reducing Research Bias and Carbon Emissions in CNNs](https://mlsanity.com/paper/2106.03242/) (2021)
- [Continual Learning with Echo State Networks](https://mlsanity.com/paper/2105.07674/) (2021)
- [Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning: A Survey](https://mlsanity.com/paper/2307.16236/) (2023)
- [Cluster-based Input Weight Initialization for Echo State Networks](https://mlsanity.com/paper/2103.04710/) (2021)
- [Biologically-inspired Semi-supervised Semantic Segmentation for Biomedical Imaging](https://mlsanity.com/paper/2412.03192/) (2024)
- [From Neurons to Computation: Biological Reservoir Computing for Pattern Recognition](https://mlsanity.com/paper/2505.03510/) (2025)
- [Exploration of Input Patterns for Enhancing the Performance of Liquid State Machines](https://mlsanity.com/paper/2004.02540/) (2020)
- [From Neural Activity to Computation: Biological Reservoirs for Pattern Recognition in Digit Classification](https://mlsanity.com/paper/2510.05637/) (2025)

## Built on by (most cited first, 1 of 1)

- [Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation](https://mlsanity.com/paper/2605.13315/) (2026)

## Abstract (arXiv)

In this paper, we present a neuro-inspired approach to reservoir computing (RC) in which a network of in vitro cultured cortical neurons serves as the physical reservoir. Rather than relying on artificial recurrent models to approximate neural dynamics, our biological reservoir computing (BRC) system leverages the spontaneous and stimulus-evoked activity of living neural circuits as its computational substrate. A high-density multi-electrode array (HD-MEA) provides simultaneous stimulation and readout across hundreds of channels: input patterns are delivered through selected electrodes, while the remaining ones capture the resulting high-dimensional neural responses, yielding a biologically grounded feature representation. A linear readout layer (single-layer perceptron) is then trained to classify these reservoir states, enabling the living neural network to perform static visual pattern-recognition tasks within a computer-vision framework. We evaluate the system across a sequence of tasks of increasing difficulty, ranging from pointwise stimuli to oriented bars, clock-digit-like shapes, and handwritten digits from the MNIST dataset. Despite the inherent variability of biological neural responses-arising from noise, spontaneous activity, and inter-session differences-the system consistently generates high-dimensional representations that support accurate classification. These results demonstrate that in vitro cortical networks can function as effective reservoirs for static visual pattern recognition, opening new avenues for integrating living neural substrates into neuromorphic computing frameworks. More broadly, this work contributes to the effort to incorporate biological principles into machine learning and supports the goals of neuro-inspired vision by illustrating how living neural systems can inform the design of efficient and biologically grounded computational models.