80 citations · 187 across the 13 of their papers we have counts for
13 papers
Empirical scaling laws in balanced networks with conductance-based synapses
Vicky Zhu, Gabriel Ocker, Robert Rosenbaum
Strongly coupled, recurrent, balanced network models have been successful in describing and predicting many phenomena observed in cortical neural recordings. However, most balanced…
Learning fixed points of recurrent neural networks by reparameterizing the network model
Vicky Zhu, Robert Rosenbaum
In computational neuroscience, fixed points of recurrent neural networks are commonly used to model neural responses to static or slowly changing stimuli. These applications raise…
Meta-Learning Biologically Plausible Plasticity Rules with Random Feedback Pathways
Navid Shervani-Tabar, Robert Rosenbaum
Backpropagation is widely used to train artificial neural networks, but its relationship to synaptic plasticity in the brain is unknown. Some biological models of backpropagation r…
Evaluating the extent to which homeostatic plasticity learns to compute prediction errors in unstructured neuronal networks
Vicky Zhu, Robert Rosenbaum
The brain is believed to operate in part by making predictions about sensory stimuli and encoding deviations from these predictions in the activity of "prediction error neurons." T…
On the relationship between predictive coding and backpropagation
Robert Rosenbaum
Artificial neural networks are often interpreted as abstract models of biological neuronal networks, but they are typically trained using the biologically unrealistic backpropagati…
Synaptic Plasticity in Correlated Balanced Networks
Alan Eric Akil, Robert Rosenbaum, Krešimir Josić
The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory--inhibitory balance is a plausible mechanism that generates such irregular activity, but i…