80 citations · 187 across the 13 of their papers we have counts for
13 papers · 1 filter
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…
A model of reward-modulated motor learning with parallelcortical and basal ganglia pathways
Ryan Pyle, Robert Rosenbaum
Many recent studies of the motor system are divided into two distinct approaches: Those that investigate how motor responses are encoded in cortical neurons' firing rate dynamics a…
From the statistics of connectivity to the statistics of spike times in neuronal networks
Gabriel Koch Ocker, Yu Hu, Michael A. Buice +4
An essential step toward understanding neural circuits is linking their structure and their dynamics. In general, this relationship can be almost arbitrarily complex. Recent theore…
Spatiotemporal dynamics and reliable computations in recurrent spiking neural networks
Ryan Pyle, Robert Rosenbaum
Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computati…
Highly connected neurons spike less frequently in balanced networks
Ryan Pyle, Robert Rosenbaum
Many biological neuronal networks exhibit highly variable spiking activity. Balanced networks offer a parsimonious model of this variability. In balanced networks, strong excitator…
Balanced networks of spiking neurons with spatially dependent recurrent connections
Robert Rosenbaum, Brent Doiron
Networks of model neurons with balanced recurrent excitation and inhibition produce irregular and asynchronous spiking activity. We extend the analysis of balanced networks to incl…
The impact of short term synaptic depression and stochastic vesicle dynamics on neural variability
Steven Reich, Robert Rosenbaum
Neural variability plays a central role in neural coding and neuronal network dynamics. Unreliability of synaptic transmission is a major source of neural variability: synaptic neu…
Finite volume and asymptotic methods for stochastic neuron models with correlated inputs
Robert Rosenbaum, Jianfu Ma, Fabien Marpeau +2
We consider a pair of stochastic integrate and fire neurons receiving correlated stochastic inputs. The evolution of this system can be described by the corresponding Fokker-Planck…