Tambaro M., Bisio M., Maschietto M., Leparulo A., Vassanelli S.
FPGA Design Integration of a 32-Microelectrodes Low-Latency Spike Detector in a Commercial System for Intracortical Recordings
Digital 1, 34-53 (2021)


G. Bellec, F. Scherr, A. Subramoney, E. Hajek, D. Salaj, R. Legenstein, and W. Maass
A solution to the learning dilemma for recurrent networks of spiking neurons.
Nature Communications, 11:3625, 2020.


D. Salaj, A. Subramoney, C. Kraisnikovic, G. Bellec, R. Legenstein, and W. Maass
Spike-frequency adaptation supports network computations on temporally dispersed information.
bioRxiv, 2020. (under revision)


Bauer, F. C., Muir, D. R. & Indiveri, G.
Real-Time Ultra-Low Power ECG Anomaly Detection Using an Event-Driven Neuromorphic Processor.
EEE Transactions on Biomedical Circuits and Systems 13, 1575–1582 (2019)


George, R. et al.
Plasticity and Adaptation in Neuromorphic Biohybrid Systems.
iScience 23, 101589 (2020)


Tambaro, M. et al.
Evaluation of In Vivo Spike Detection Algorithms for Implantable MTA Brain—Silicon Interfaces.
Journal of Low Power Electronics and Applications 10, 26 (2020)


MarkovichÔÇÉMolochnikov, I. & Cohen, D.
Bilateral responses of rat ventral striatum tonically active neurons to unilateral medial forebrain bundle stimulation.
European Journal of Neuroscience 52, 4499–4516 (2020)


M. Tambaro, E. A. Vallicelli, D. Tomasella, A. Baschirotto, S. Vassanelli, M. Maschietto, M. De Matteis
Real-Time Neural Spikes Imaging by a 9375 sample / (secÍ╝ pixel) 32x32 pixels Electrolyte-Oxide-Semiconductor Biosensor
(2019) PRIME 2019 - 15th Conference on Ph.D. Research in Microelectronics and Electronics, Proceedings, art. no. 8787817, pp. 233-236


FC. Bauer, D.r.Muir, and G. Indiveri
2019 “Real-time ultra-low power ECG anomaly detection using an event-driven neuromorphic processor”
IEEE Transactions on Biomedical Circuits and Systems, 11 Nov 2019, DOI: 10.1109/tbcas.2019.2953001 PMID: 31715572 - AICTX