6 papers
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…
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…