# A Memristor-Inspired Computation for Epileptiform Signals in Spheroids

arXiv: https://arxiv.org/abs/2307.04607 · HTML: https://mlsanity.com/paper/2307.04607/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 10 Jul 2023.

## Builds on (most cited first, 3 of 3)

- [An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach](https://mlsanity.com/paper/1801.05412/) (2018)
- [An End-to-End Deep Learning Approach for Epileptic Seizure Prediction](https://mlsanity.com/paper/2108.07453/) (2021)
- [A Novel Multi-scale Dilated 3D CNN for Epileptic Seizure Prediction](https://mlsanity.com/paper/2105.02823/) (2021)

## Abstract (arXiv)

In this paper we present a memristor-inspired computational method for obtaining a type of running spectrogram or fingerprint of epileptiform activity generated by rodent hippocampal spheroids. It can be used to compute on the fly and with low computational cost an alert-level signal for epileptiform events onset. Here, we describe the computational method behind this fingerprint technique and illustrate it using epileptiform events recorded from hippocampal spheroids using a microelectrode array system.