454 citations · 809 across the 9 of their papers we have counts for
9 papers
Shaping manifolds in equivariant recurrent neural networks
Arianna Di Bernardo, Adrian Valente, Francesca Mastrogiuseppe +1
Recordings of increasingly large neural populations have revealed that the firing of individual neurons is highly coordinated. When viewed in the space of all possible patterns, th…
Aligned and oblique dynamics in recurrent neural networks
Friedrich Schuessler, Francesca Mastrogiuseppe, Srdjan Ostojic +1
The relation between neural activity and behaviorally relevant variables is at the heart of neuroscience research. When strong, this relation is termed a neural representation. The…
Quality of internal representation shapes learning performance in feedback neural networks
Lee Susman, Francesca Mastrogiuseppe, Naama Brenner +1
A fundamental feature of complex biological systems is the ability to form feedback interactions with their environment. A prominent model for studying such interactions is reservo…
Shaping dynamics with multiple populations in low-rank recurrent networks
Manuel Beiran, Alexis Dubreuil, Adrian Valente +2
An emerging paradigm proposes that neural computations can be understood at the level of dynamical systems that govern low-dimensional trajectories of collective neural activity. H…
The interplay between randomness and structure during learning in RNNs
Friedrich Schuessler, Francesca Mastrogiuseppe, Alexis Dubreuil +2
Recurrent neural networks (RNNs) trained on low-dimensional tasks have been widely used to model functional biological networks. However, the solutions found by learning and the ef…
Dynamics of random recurrent networks with correlated low-rank structure
Friedrich Schuessler, Alexis Dubreuil, Francesca Mastrogiuseppe +2
A given neural network in the brain is involved in many different tasks. This implies that, when considering a specific task, the network's connectivity contains a component which…
A geometrical analysis of global stability in trained feedback networks
Francesca Mastrogiuseppe, Srdjan Ostojic
Recurrent neural networks have been extensively studied in the context of neuroscience and machine learning due to their ability to implement complex computations. While substantia…
Linking connectivity, dynamics and computations in low-rank recurrent neural networks
Francesca Mastrogiuseppe, Srdjan Ostojic
Large scale neural recordings have established that the transformation of sensory stimuli into motor outputs relies on low-dimensional dynamics at the population level, while indiv…
Intrinsically-generated fluctuating activity in excitatory-inhibitory networks
Francesca Mastrogiuseppe, Srdjan Ostojic
Recurrent networks of non-linear units display a variety of dynamical regimes depending on the structure of their synaptic connectivity. A particularly remarkable phenomenon is the…