Showing results (1-10 of 46) with videos related to
Sort By:
Pageof 5
Neural Computation|July 19, 2024
Learning Fixed Points of Recurrent Neural Networks by Reparameterizing the Network ModelVicky Zhu, Robert RosenbaumJournal of Computational Neuroscience|June 3, 2022
Evaluating the extent to which homeostatic plasticity learns to compute prediction errors in unstructured neuronal networksVicky Zhu, Robert RosenbaumPlos Computational Biology|September 18, 2020
Nonlinear stimulus representations in neural circuits with approximate excitatory-inhibitory balanceCody Baker, Vicky Zhu, Robert RosenbaumPlos One|March 31, 2022
On the relationship between predictive coding and backpropagationRobert RosenbaumPlos One|March 25, 2025
Correction: On the relationship between predictive coding and backpropagationRobert RosenbaumFrontiers in Computational Neuroscience|May 6, 2016
A Diffusion Approximation and Numerical Methods for Adaptive Neuron Models with Stochastic InputsRobert RosenbaumPhysical Review. E|May 14, 2016
Highly connected neurons spike less frequently in balanced networksRyan Pyle, Robert RosenbaumJournal of Computational Neuroscience|January 29, 2013
The impact of short term synaptic depression and stochastic vesicle dynamics on neuronal variabilitySteven Reich, Robert RosenbaumPhysical Review Letters|January 21, 2017
Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural NetworksRyan Pyle, Robert RosenbaumPhysical Review. E, Statistical, Nonlinear, and Soft Matter Physics|December 21, 2011
Membrane potential and spike train statistics depend distinctly on input statisticsRobert Rosenbaum, Krešimir JosićPageof 5