454 citations · 1.1k across the 23 of their papers we have counts for
24 papers
Balancing structure and randomness: maximum entropy networks for context-dependent computations
Ludwig Hruza, Srdjan Ostojic
Understanding how network function constrains neural connectivity is a central challenge in neuroscience. An influential approach is to train neural networks with gradient descent…
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…
Identifying the impact of local connectivity patterns on dynamics in excitatory-inhibitory networks
Yuxiu Shao, David Dahmen, Stefano Recanatesi +2
Networks of excitatory and inhibitory (EI) neurons form a canonical circuit in the brain. Seminal theoretical results on dynamics of such networks are based on the assumption that…
Brief technical note on linearizing recurrent neural networks (RNNs) before vs after the pointwise nonlinearity
Marino Pagan, Adrian Valente, Srdjan Ostojic +1
Linearization of the dynamics of recurrent neural networks (RNNs) is often used to study their properties. The same RNN dynamics can be written in terms of the ``activations" (the…
The computational role of structure in neural activity and connectivity
Srdjan Ostojic, Stefano Fusi
One major challenge of neuroscience is finding interesting structures in a seemingly disorganized neural activity. Often these structures have computational implications that help…
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…