From Neurons to Computation: Biological Reservoir Computing for Pattern Recognition

TL;DR

This paper demonstrates a novel biological reservoir computing system using cultured neurons and multi-electrode arrays to perform pattern recognition, showing potential for bio-hybrid computing applications.

Contribution

Introduces a biological reservoir computing paradigm leveraging cultured neurons and MEA recordings for pattern recognition tasks, bridging biological neural networks and computational models.

Findings

Biological reservoir computing can perform pattern recognition effectively.

Neural activity from cultured neurons maps inputs to high-dimensional features.

The system achieves accurate digit recognition using simple linear classifiers.

Abstract

In this paper, we introduce a paradigm for reservoir computing (RC) that leverages a pool of cultured biological neurons as the reservoir substrate, creating a biological reservoir computing (BRC). This system operates similarly to an echo state network (ESN), with the key distinction that the neural activity is generated by a network of cultured neurons, rather than being modeled by traditional artificial computational units. The neuronal activity is recorded using a multi-electrode array (MEA), which enables high-throughput recording of neural signals. In our approach, inputs are introduced into the network through a subset of the MEA electrodes, while the remaining electrodes capture the resulting neural activity. This generates a nonlinear mapping of the input data to a high-dimensional biological feature space, where distinguishing between data becomes more efficient and straightforward, allowing a simple linear classifier to perform pattern recognition tasks effectively. To evaluate the performance of our proposed system, we present an experimental study that includes various input patterns, such as positional codes, bars with different orientations, and a digit recognition task. The results demonstrate the feasibility of using biological neural networks to perform tasks traditionally handled by artificial neural networks, paving the way for further exploration of biologically-inspired computing systems, with potential applications in neuromorphic engineering and bio-hybrid computing.