Information encoding and decoding in in-vitro neural networks on micro electrode arrays through stimulation timing

TL;DR

This study investigates how stimulation timing can be used to encode information in in-vitro neural networks on microelectrode arrays, analyzing optimal parameters for encoding and decoding spike responses.

Contribution

It introduces stimulation timing as an encoding method and identifies optimal timing bounds and readout parameters for neural network data decoding.

Findings

Stimulation timings between 36 and 436ms are optimal for encoding.

Different readout parameters are optimal at different response phases.

Stimulation timing can produce linearly separable spike responses.

Abstract

A primary challenge in utilizing in-vitro biological neural networks for computations is finding good encoding and decoding schemes for inputting and decoding data to and from the networks. Furthermore, identifying the optimal parameter settings for a given combination of encoding and decoding schemes adds additional complexity to this challenge. In this study we explore stimulation timing as an encoding method, i.e. we encode information as the delay between stimulation pulses and identify the bounds and acuity of stimulation timings which produce linearly separable spike responses. We also examine the optimal readout parameters for a linear decoder in the form of epoch length, time bin size and epoch offset. Our results suggest that stimulation timings between 36 and 436ms may be optimal for encoding and that different combinations of readout parameters may be optimal at different parts of the evoked spike response.