RT Journal Article
SR Electronic
T1 Real-time detection of bursts in neuronal cultures using a Neuromorphic Auditory Sensor and Spiking Neural Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.05.20.105593
DO 10.1101/2020.05.20.105593
A1 Juan P. Dominguez-Morales
A1 Stefano Buccelli
A1 Daniel Gutierrez-Galan
A1 Ilaria Colombi
A1 Angel Jimenez-Fernandez
A1 Michela Chiappalone
YR 2020
UL http://biorxiv.org/content/early/2020/05/22/2020.05.20.105593.abstract
AB The correct identification of burst events is crucial in many scenarios, ranging from basic neuroscience to biomedical applications. However, none of the burst detection methods that can be found in the literature have been widely adopted for this task. As an alternative to conventional techniques, a novel neuromorphic approach for real-time burst detection is proposed and tested on acquisitions from in vitro cultures. The system consists of a Neuromorphic Auditory Sensor, which converts the input signal obtained from electrophysiological recordings into spikes and decomposes them into different frequency bands. The output of the sensor is sent to a trained spiking neural network implemented on a SpiNNaker board that discerns between bursting and non-bursting activity. This data-driven approach was compared with 8 different conventional spike-based methods, addressing some of their drawbacks, such as being able to detect both high and low frequency events and working in an online manner. Similar results in terms of number of detected events, mean burst duration and correlation as current state-of-the-art approaches were obtained with the proposed system, also benefiting from its lower power consumption and computational latency. Therefore, our neuromorphic-based burst detection paves the road to future implementations for neuroprosthetic applications.Competing Interest StatementThe authors have declared no competing interest.