74 citations · 90 across the 7 of their papers we have counts for
9 papers
Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization
Ravi Srinivasan, Francesca Mignacco, Martino Sorbaro +4
"Forward-only" algorithms, which train neural networks while avoiding a backward pass, have recently gained attention as a way of solving the biologically unrealistic aspects of ba…
Bio-Inspired, Task-Free Continual Learning through Activity Regularization
Francesco Lässig, Pau Vilimelis Aceituno, Martino Sorbaro +1
The ability to sequentially learn multiple tasks without forgetting is a key skill of biological brains, whereas it represents a major challenge to the field of deep learning. To a…
Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based Vision
Julian Büchel, Gregor Lenz, Yalun Hu +2
Event-based dynamic vision sensors provide very sparse output in the form of spikes, which makes them suitable for low-power applications. Convolutional spiking neural networks mod…
Optimizing the energy consumption of spiking neural networks for neuromorphic applications
Martino Sorbaro, Qian Liu, Massimo Bortone +1
In the last few years, spiking neural networks have been demonstrated to perform on par with regular convolutional neural networks. Several works have proposed methods to convert a…
Statistical models of neural activity, criticality, and Zipf's law
Martino Sorbaro, J. Michael Herrmann, Matthias H. Hennig
In this overview, we discuss the connections between the observations of critical dynamics in neuronal networks and the maximum entropy models that are often used as statistical mo…
Scaling Spike Detection and Sorting for Next Generation Electrophysiology
Matthias H. Hennig, Cole Hurwitz, Martino Sorbaro
Reliable spike detection and sorting, the process of assigning each detected spike to its originating neuron, is an essential step in the analysis of extracellular electrical recor…
Local learning rules to attenuate forgetting in neural networks
Michael Deistler, Martino Sorbaro, Michael E. Rule +1
Hebbian synaptic plasticity inevitably leads to interference and forgetting when different, overlapping memory patterns are sequentially stored in the same network. Recent work on…
Optimal encoding in stochastic latent-variable Models
M. E. Rule, M. Sorbaro, M. H. Hennig
In this work we explore encoding strategies learned by statistical models of sensory coding in noisy spiking networks. Early stages of sensory communication in neural systems can b…
Critical behavior of the relaxation rate, the susceptibility, and a pair correlation function in the Kuramoto model on scale-free networks
S. Yoon, M. Sorbaro Sindaci, A. V. Goltsev +1
We study the impact of network heterogeneity on relaxation dynamics of the Kuramoto model on uncorrelated complex networks with scale-free degree distributions. Using the Ott-Anton…