3 papers
q-bio.NC2020
Optimal Learning with Excitatory and Inhibitory synapses
Alessandro Ingrosso
Characterizing the relation between weight structure and input/output statistics is fundamental for understanding the computational capabilities of neural circuits. In this work, I…
cond-mat.dis-nn2018
Training dynamically balanced excitatory-inhibitory networks
Alessandro Ingrosso, L. F. Abbott
The construction of biologically plausible models of neural circuits is crucial for understanding the computational properties of the nervous system. Constructing functional networ…
cond-mat.dis-nn2018
From statistical inference to a differential learning rule for stochastic neural networks
Luca Saglietti, Federica Gerace, Alessandro Ingrosso +2
Stochastic neural networks are a prototypical computational device able to build a probabilistic representation of an ensemble of external stimuli. Building on the relationship bet…