Publications
Modeling Neural Circuits Made Simple with Python (textbook).
Rosenbaum, R. MIT Press 2023.
Rapid compensatory plasticity revealed by dynamic correlated activity in monkeys in vivo .
Andrei, A., Akil, A.E., Kharas, N., Rosenbaum, R., Josić, K., & Dragoi, V. Nature Neuroscience, 2023.
Meta-learning biologically plausible plasticity rules with random feedback pathways.
Shervani-Tabar, N. & Rosenbaum, R. Nature Communications, 2023.
Learning fixed points of recurrent neural networks by reparameterizing the network model .
Zhu, V. & Rosenbaum, R. Neural Computation (In Press), 2023.
On the relationship between predictive coding and backpropagation.
Rosenbaum, R. PLoS One, 2022.
Evaluating the extent to which homeostatic plasticity learns to compute prediction errors in unstructured neuronal networks.
Zhu, V. & Rosenbaum, R. Journal of Computational Neuroscience, 2022.
Balanced networks under spike-time dependent plasticity.
Akil, A.E., Rosenbaum, R., & Josić, K. PLoS Computational Biology, 2021.
Neural activity during a simple reaching task in macaques is counter to theories of basal ganglia-thalamic communication.
Schwab, B. C., Kase, D., Zimnik, A., Rosenbaum, R., Codianni, M. G., Rubin, J. E., & Turner, R. S. PLoS Biology, 2021.
Nonlinear stimulus representations in neural circuits with approximate excitatory-inhibitory balance.
Baker, C. Zhu, V., & Rosenbaum, R. PLoS Computational Biology, 2020.
Spatially extended balanced networks without translationally invariant connectivity.
Ebsch, C. & Rosenbaum, R. Journal of Mathematical Neuroscience, 2020.
Inference of Synaptic Connectivity and External Variability in Neural Microcircuits.
Baker, C., Froudarakis, E., Yatsenko, D., Tolias, A.S., & Rosenbaum, R. Journal of Computational Neuroscience, 2020.
The correlated state in balanced neuronal networks.
Baker, C. Ebsch, C., & Lampl, I., & Rosenbaum, R. Physical Review E, 2019.
A model of reward-modulated motor learning with parallel cortical and basal ganglia pathways.
Pyle, R. & Rosenbaum, R. Neural Computation, 2019.
Circuit-based models of shared variability in cortical networks.
Huang, C., Ruff, D., Pyle, R., Rosenbaum, R., Cohen, M., & Doiron, B. Neuron, 2019.
Special Issue from the 2017 International Conference on Mathematical Neuroscience.
Kilpatrick, Z., Gjorgjieva, J., & Rosenbaum, R. The Journal of Mathematical Neuroscience, 2019.
Imbalanced amplification: A mechanism of amplification and suppression from local imbalance of excitation and inhibition in cortical circuits.
Ebsch, C. & Rosenbaum, R. PLoS Computational Biology, 2018.
The spatial structure of correlated neuronal variability.
R. Rosenbaum, M.A. Smith, A. Kohn, J.E. Rubin and B. Doiron. Nature Neuroscience, 2017.
Spatiotemporal dynamics and reliable computations in recurrent spiking neural networks.
R. Pyle and R. Rosenbaum. Physical Review Letters, 2017.
From the statistics of connectivity to the statistics of spike times in neuronal networks.
Ocker, G.K., Hu, Y., Buice, M.A., Doiron, B., Josić, K., Rosenbaum, R., & Shea-Brown, E. Current Opinion in Neurobiology, 2017.
The mechanics of state-dependent neural correlations.
B. Doiron, A. Litwin-Kumar, R. Rosenbaum, G. Ocker and K Josić. Nature Neuroscience, 2016.
Highly connected neurons spike less frequently in balanced networks.
R. Pyle and R. Rosenbaum. Physical Review E, 2016.
A diffusion approximation and numerical methods for adaptive neuron models with stochastic inputs.
R. Rosenbaum. Frontiers in Computational Neuroscience, 2016.
Inhibitory stabilization and visual coding in cortical circuits with multiple interneuron subtypes.
A. Litwin-Kumar, R. Rosenbaum and B. Doiron. Journal of Neurophysiology, 2016.
Short-term Plasticity, Biophysical Models.
R. Rosenbaum. in Encyclopaedia of Computational Neuroscience, 2015.
Balanced networks of spiking neurons with spatially dependent recurrent connections.
R. Rosenbaum and B. Doiron. Physical Review X, 2014.
Axonal and synaptic failure suppress the transfer of firing rate oscillations, synchrony and information during high frequency deep brain stimulation.
R. Rosenbaum, A. Zimnik, F. Zheng, R. Turner, C. Alzheimer, B. Doiron, and J.E. Rubin. Neurobiology of Disease, 2014.
Correlated neuronal activity and its relationship to coding, dynamics and network architecture.
Rosenbaum, R., Tchumatchenko, T., & Moreno-Bote, R. Frontiers in Computational Neuroscience, 2014.
The impact of short term synaptic depression and stochastic vesicle dynamics on neuronal variability.
S. Reich and R. Rosenbaum. Journal of Computational Neuroscience, 2013.
Short term synaptic depression and stochastic vesicle dynamics reduce and shape neuronal correlations.
R. Rosenbaum, J.E. Rubin, B. Doiron. Journal of Neurophysiology, 2013.
Short term synaptic depression imposes a frequency dependent filter on synaptic information transfer.
R. Rosenbaum, J.E. Rubin, B. Doiron. PLoS Computational Biology, 2012.
Beta adrenergic modulation of spontaneous spatiotemporal activity patterns and synchrony in hyper-excitable hippocampal circuits.
A. Hazra, R. Rosenbaum, B. Bodmann, S. Cao, K. Josić and J. Ziburkus. Journal of Neurophysiology, 2012.
Membrane potential and spike train statistics depend distinctly on input statistics.
R. Rosenbaum and K. Josić. Physical Review E, 2011.
The effects of pooling on correlated neural variability.
R. Rosenbaum, J. Trousdale and K. Josić. Frontiers in Neuroscience, 2011.
Mechanisms that modulate the transfer of spiking correlations.
R. Rosenbaum and K. Josić. Neural Computation, 2011.
Finite volume and asymptotic methods for analyzing stochastic neuron models with correlated inputs.
R. Rosenbaum, F. Marpeau, J. Ma, A. Barua and K. Josić. Journal of Mathematical Biology, 2011.
Pooling and correlated neural activity.
R. Rosenbaum, J. Trousdale and K. Josić. Frontiers in Computational Neuroscience, 2010.
Unstable solutions of nonautonomous linear differential equations.
K. Josić and R. Rosenbaum. SIAM Review, 2008.
Rosenbaum, R. MIT Press 2023.
Rapid compensatory plasticity revealed by dynamic correlated activity in monkeys in vivo .
Andrei, A., Akil, A.E., Kharas, N., Rosenbaum, R., Josić, K., & Dragoi, V. Nature Neuroscience, 2023.
Meta-learning biologically plausible plasticity rules with random feedback pathways.
Shervani-Tabar, N. & Rosenbaum, R. Nature Communications, 2023.
Learning fixed points of recurrent neural networks by reparameterizing the network model .
Zhu, V. & Rosenbaum, R. Neural Computation (In Press), 2023.
On the relationship between predictive coding and backpropagation.
Rosenbaum, R. PLoS One, 2022.
Evaluating the extent to which homeostatic plasticity learns to compute prediction errors in unstructured neuronal networks.
Zhu, V. & Rosenbaum, R. Journal of Computational Neuroscience, 2022.
Balanced networks under spike-time dependent plasticity.
Akil, A.E., Rosenbaum, R., & Josić, K. PLoS Computational Biology, 2021.
Neural activity during a simple reaching task in macaques is counter to theories of basal ganglia-thalamic communication.
Schwab, B. C., Kase, D., Zimnik, A., Rosenbaum, R., Codianni, M. G., Rubin, J. E., & Turner, R. S. PLoS Biology, 2021.
Nonlinear stimulus representations in neural circuits with approximate excitatory-inhibitory balance.
Baker, C. Zhu, V., & Rosenbaum, R. PLoS Computational Biology, 2020.
Spatially extended balanced networks without translationally invariant connectivity.
Ebsch, C. & Rosenbaum, R. Journal of Mathematical Neuroscience, 2020.
Inference of Synaptic Connectivity and External Variability in Neural Microcircuits.
Baker, C., Froudarakis, E., Yatsenko, D., Tolias, A.S., & Rosenbaum, R. Journal of Computational Neuroscience, 2020.
The correlated state in balanced neuronal networks.
Baker, C. Ebsch, C., & Lampl, I., & Rosenbaum, R. Physical Review E, 2019.
A model of reward-modulated motor learning with parallel cortical and basal ganglia pathways.
Pyle, R. & Rosenbaum, R. Neural Computation, 2019.
Circuit-based models of shared variability in cortical networks.
Huang, C., Ruff, D., Pyle, R., Rosenbaum, R., Cohen, M., & Doiron, B. Neuron, 2019.
Special Issue from the 2017 International Conference on Mathematical Neuroscience.
Kilpatrick, Z., Gjorgjieva, J., & Rosenbaum, R. The Journal of Mathematical Neuroscience, 2019.
Imbalanced amplification: A mechanism of amplification and suppression from local imbalance of excitation and inhibition in cortical circuits.
Ebsch, C. & Rosenbaum, R. PLoS Computational Biology, 2018.
The spatial structure of correlated neuronal variability.
R. Rosenbaum, M.A. Smith, A. Kohn, J.E. Rubin and B. Doiron. Nature Neuroscience, 2017.
Spatiotemporal dynamics and reliable computations in recurrent spiking neural networks.
R. Pyle and R. Rosenbaum. Physical Review Letters, 2017.
From the statistics of connectivity to the statistics of spike times in neuronal networks.
Ocker, G.K., Hu, Y., Buice, M.A., Doiron, B., Josić, K., Rosenbaum, R., & Shea-Brown, E. Current Opinion in Neurobiology, 2017.
The mechanics of state-dependent neural correlations.
B. Doiron, A. Litwin-Kumar, R. Rosenbaum, G. Ocker and K Josić. Nature Neuroscience, 2016.
Highly connected neurons spike less frequently in balanced networks.
R. Pyle and R. Rosenbaum. Physical Review E, 2016.
A diffusion approximation and numerical methods for adaptive neuron models with stochastic inputs.
R. Rosenbaum. Frontiers in Computational Neuroscience, 2016.
Inhibitory stabilization and visual coding in cortical circuits with multiple interneuron subtypes.
A. Litwin-Kumar, R. Rosenbaum and B. Doiron. Journal of Neurophysiology, 2016.
Short-term Plasticity, Biophysical Models.
R. Rosenbaum. in Encyclopaedia of Computational Neuroscience, 2015.
Balanced networks of spiking neurons with spatially dependent recurrent connections.
R. Rosenbaum and B. Doiron. Physical Review X, 2014.
Axonal and synaptic failure suppress the transfer of firing rate oscillations, synchrony and information during high frequency deep brain stimulation.
R. Rosenbaum, A. Zimnik, F. Zheng, R. Turner, C. Alzheimer, B. Doiron, and J.E. Rubin. Neurobiology of Disease, 2014.
Correlated neuronal activity and its relationship to coding, dynamics and network architecture.
Rosenbaum, R., Tchumatchenko, T., & Moreno-Bote, R. Frontiers in Computational Neuroscience, 2014.
The impact of short term synaptic depression and stochastic vesicle dynamics on neuronal variability.
S. Reich and R. Rosenbaum. Journal of Computational Neuroscience, 2013.
Short term synaptic depression and stochastic vesicle dynamics reduce and shape neuronal correlations.
R. Rosenbaum, J.E. Rubin, B. Doiron. Journal of Neurophysiology, 2013.
Short term synaptic depression imposes a frequency dependent filter on synaptic information transfer.
R. Rosenbaum, J.E. Rubin, B. Doiron. PLoS Computational Biology, 2012.
Beta adrenergic modulation of spontaneous spatiotemporal activity patterns and synchrony in hyper-excitable hippocampal circuits.
A. Hazra, R. Rosenbaum, B. Bodmann, S. Cao, K. Josić and J. Ziburkus. Journal of Neurophysiology, 2012.
Membrane potential and spike train statistics depend distinctly on input statistics.
R. Rosenbaum and K. Josić. Physical Review E, 2011.
The effects of pooling on correlated neural variability.
R. Rosenbaum, J. Trousdale and K. Josić. Frontiers in Neuroscience, 2011.
Mechanisms that modulate the transfer of spiking correlations.
R. Rosenbaum and K. Josić. Neural Computation, 2011.
Finite volume and asymptotic methods for analyzing stochastic neuron models with correlated inputs.
R. Rosenbaum, F. Marpeau, J. Ma, A. Barua and K. Josić. Journal of Mathematical Biology, 2011.
Pooling and correlated neural activity.
R. Rosenbaum, J. Trousdale and K. Josić. Frontiers in Computational Neuroscience, 2010.
Unstable solutions of nonautonomous linear differential equations.
K. Josić and R. Rosenbaum. SIAM Review, 2008.
PhD Dissertation
The transfer and propagation of correlated neuronal activity.
R. Rosenbaum. University of Houston, 2011.
R. Rosenbaum. University of Houston, 2011.