1.1k citations · 4.6k across the 68 of their papers we have counts for
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Feed-forward and recurrent inhibition for compressing and classifying high dynamic range biosignals in spiking neural network architectures
Rachel Sava, Elisa Donati, Giacomo Indiveri
Neuromorphic processors that implement Spiking Neural Networks (SNNs) using mixed-signal analog/digital circuits represent a promising technology for closed-loop real-time processi…
An Adaptive Event-based Data Converter for Always-on Biomedical Applications at the Edge
Mohammadali Sharifshazileh, Giacomo Indiveri
Typical bio-signal processing front-ends are designed to maximize the quality of the recorded data, to allow faithful reproduction of the signal for monitoring and off-line process…
Neuromorphic implementation of ECG anomaly detection using delay chains
Stefan Gerber, Marc Steiner, Maryada +2
Real-time analysis and classification of bio-signals measured using wearable devices is computationally costly and requires dedicated low-power hardware. One promising approach is…
Organic log-domain integrator synapse
Mohammad Javad Mirshojaeian Hosseini, Elisa Donati, Giacomo Indiveri +1
Synapses play a critical role in memory, learning, and cognition. Their main functions include converting pre-synaptic voltage spikes to post-synaptic currents, as well as scaling…
A Spiking Neural Network (SNN) for detecting High Frequency Oscillations (HFOs) in the intraoperative ECoG
Karla Burelo, Mohammadali Sharifshazileh, Niklaus Krayenbühl +3
To achieve seizure freedom, epilepsy surgery requires the complete resection of the epileptogenic brain tissue. In intraoperative ECoG recordings, high frequency oscillations (HFOs…
An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG
Mohammadali Sharifshazileh, Karla Burelo, Johannes Sarnthein +1
In this work, we present a neuromorphic system that combines for the first time a neural recording headstage with a signal-to-spike conversion circuit and a multi-core spiking neur…