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 different conventional spike-based and raw-based burst detection 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 real-time neuroprosthetic applications.

Real-time detection of bursts in neuronal cultures using a neuromorphic auditory sensor and spiking neural networks

Buccelli S.;Colombi I.;Chiappalone M.
2021

Abstract

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 different conventional spike-based and raw-based burst detection 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 real-time neuroprosthetic applications.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/1077488
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