27 citations · 63 across the 9 of their papers we have counts for
9 papers
Extracting Governing Equations from Latent Dynamics via Multi-View Contrastive Learning
Paolo Muratore, Mackenzie Weygandt Mathis
Identifying latent dynamical systems from noisy, high-dimensional measurements is a central problem at the intersection of representation learning, system identification, and scien…
Stretching Beyond the Obvious: A Gradient-Free Framework to Unveil the Hidden Landscape of Visual Invariance
Lorenzo Tausani, Paolo Muratore, Morgan B. Talbot +3
Uncovering which feature combinations are encoded by visual units is critical to understanding how images are transformed into representations that support recognition. While exist…
Learning fast changing slow in spiking neural networks
Cristiano Capone, Paolo Muratore
Reinforcement learning (RL) faces substantial challenges when applied to real-life problems, primarily stemming from the scarcity of available data due to limited interactions with…
Beyond spiking networks: the computational advantages of dendritic amplification and input segregation
Cristiano Capone, Cosimo Lupo, Paolo Muratore +1
The brain can efficiently learn a wide range of tasks, motivating the search for biologically inspired learning rules for improving current artificial intelligence technology. Most…
Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks
Paolo Muratore, Sina Tafazoli, Eugenio Piasini +2
Visual object recognition has been extensively studied in both neuroscience and computer vision. Recently, the most popular class of artificial systems for this task, deep convolut…
Burst-dependent plasticity and dendritic amplification support target-based learning and hierarchical imitation learning
Cristiano Capone, Cosimo Lupo, Paolo Muratore +1
The brain can learn to solve a wide range of tasks with high temporal and energetic efficiency. However, most biological models are composed of simple single compartment neurons an…
Error-based or target-based? A unifying framework for learning in recurrent spiking networks
Cristiano Capone, Paolo Muratore, Pier Stanislao Paolucci
Learning in biological or artificial networks means changing the laws governing the network dynamics in order to better behave in a specific situation. In the field of supervised l…
Target spiking patterns enable efficient and biologically plausible learning for complex temporal tasks
Paolo Muratore, Cristiano Capone, Pier Stanislao Paolucci
Recurrent spiking neural networks (RSNN) in the human brain learn to perform a wide range of perceptual, cognitive and motor tasks very efficiently in terms of energy consumption a…
Analysis and Model of Cortical Slow Waves Acquired with Optical Techniques
Marco Celotto, Chiara De Luca, Paolo Muratore +5
Slow waves (SWs) are spatio-temporal patterns of cortical activity that occur both during natural sleep and anesthesia and are preserved across species. Even though electrophysiolo…