Lab Publications

Found 267 results
[ Author(Desc)] Title Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
A
Angulo, S. L., Henzi T., Neymotin S. A., Suarez M. D., Lytton W. W., Schwaller B., et al. (2019).  Amyloid pathology–produced unexpected modifications of calcium homeostasis in hippocampal subicular dendrites. Alzheimers. Dement..
Angulo, S., Graham J. W., Gao P., Dura-Bernal S., Neymotin S. A., Antic S. D., et al. (2017).  Cortical ensembles based on dendritic plateau generation in the prefrontal cortex. Society for Neuroscience 2017 (SFN '17).
Antic, S. D., Hines M., & Lytton WW. (2018).  Embedded ensemble encoding hypothesis: The role of the ``Prepared'' cell. J. Neurosci. Res..
B
Bernard, C. (2018).  Understanding epilepsy across spatial and time scales. Society for Neuroscience 2018 (SFN '18).
Briska, AM., Uhlrich DJ., & Lytton WW. (2000).  Independent dendritic domains in the thalamic circuit. Neurocomputing. 32, 299–305.
Briska, AM., Uhlrich DJ., & Lytton WW. (1999).  Passive properties & signal synergy in thalamic cells. 25,
Briska, AM., Uhlrich DJ., & Lytton WW. (2003).  Computer model of passive signal integration based on whole-cell ınvit\ studies of rat lateral geniculate nucleus. European Journal of Neuroscience. 17, 1531-1541.
Bulanova, A. S., Mcdougal R. A., Neymotin S. A., Mutai V. K., Lytton WW., & Hines M. L. (2014).  Integrating Systems Biology Markup Language (SBML) with NEURON. Society for Neuroscience 2014 (SFN '14).
Bulanova, AS., McDougal RA., Neymotin SA., Mutai V., Lytton WW., & Hines M. (2014).  Integrating Systems Biology Markup Language (SBML) with NEURON. Computational Neuroscience.
C
Cantarelli, M., Marin B., Quintana A., Earnshaw M., Gleeson P., Dura-Bernal S., et al. (2018).  Geppetto: a reusable modular open platform for exploring neuroscience data and models. Phil. Trans. R. Soc. B. 373, 20170380.
Cantarelli, M., Quintana A., Marin B., Earnshaw M., Gleeson P., Court R., et al. (2017).  Geppetto: an open source visualisation and simulation platform for neuroscience. Computational Neuroscience Meeting (CNS 17').
Chadderdon, GL., Neymotin SA., Kerr CC., Francis JT., & Lytton WW. (2012).  Dopamine-based reinforcement learning of virtual arm reaching task in a spiking model of motor cortex. Society for Neuroscience.
Chadderdon, GL., Neymotin SA., Kerr CC., Francis JT., & Lytton WW. (2012).  Dopamine-based reinforcement learning of virtual arm reaching task in a spiking model of motor cortex. International Conference on Cognititve and Neural Systems 16.
Chadderdon, GL., Mohan A., Suter BA., Neymotin S., Kerr CC., Francis JT., et al. (2014).  Motor cortex microcircuit simulation based on brain activity mapping. Neural Comput. 26, 1239–1262.
Chadderdon, GL., Neymotin S., Kerr CC., & Lytton WW. (2012).  Reinforcement Learning of Targeted Movement in a Spiking Neuronal Model of Motor Cortex. PLoS One. 7, e47251.
Choi, J. S., Menzies R. J., Dura-Bernal S., Francis J. T., Lytton WW., & Kerr C. C. (2015).  Spiking network modeling of neuronal dynamics in individual rats. BMC Neuroscience.
Chover, J., Haberly L., & Lytton WW. (2001).  Alternating dominance of NMDA and AMPA for learning and recall: a computer model. Neuroreport. 12, 2503-2507.
D
Denham, S., Poirazi P., De Schutter E., Friston K., Chan H. Ka, Nowotny T., et al. (2017).  26th Annual Computational Neuroscience Meeting (CNS*2017): Part 1. BMC Neuroscience. 18, 58.
Deyo, S., & Lytton WW. (1997).  Inhibition Can Disrupt Hypersynchrony In Model Neuronal Networks. Progress in neuro-psychopharmacology & biological psychiatry.
Doherty, D., Sivagnanam S., Dura-Bernal S., & Lytton W. W. (2018).  Simulation of avalanches in mouse primary motor cortex (M1). Computational Neuroscience Meeting (CNS 18').
Doherty, D.. W., Dura-Bernal S.., & Lytton W.. W. (2019).  Computer models of mouse area M1 show avalanches for full model and subcircuits defined by layer or cell type. Society for Neuroscience 2019 (SFN '19).
Doherty, D. W., Dura-Bernal S., Neymotin S. A., & Lytton WW. (2018).  Identifying avalanches in simulated mouse primary motor cortex (M1). Society for Neuroscience 2018 (SFN '18).
Dura-Bernal, S., Neymotin S., Suter BA., Shepherd GMG., & Lytton WW. (2018).  Long-range inputs and H-current regulate different modes of operation in a multiscale model of mouse M1 microcircuits. bioRxiv.
Dura-Bernal, S., Griffith E. Y., Marczak A., O'Connell N., McGinnis T., Lytton W. W., et al. (2019).  Data-driven model of auditory thalamocortical system rhythms. Society for Neuroscience (SFN '19).
Dura-Bernal, S., Zhou X., Neymotin S., Przekwas A., Francis J. T., & Lytton WW. (2015).  Cortical spiking network interfaced with virtual musculoskeletal arm and robotic arm. Frontiers in Neurorobotics. 9,
Dura-Bernal, S., Li K., Brockmeier A., Kerr C., Neymotin S., Principe J., et al. (2014).  Modulation of virtual arm trajectories via microstimulation in a spiking model of sensorimotor cortex. BMC Neuroscience. 15, P106.
Dura-Bernal, S., Suter B. A., Quintana A., Cantarelli M., Gleeson P., Rodriguez F., et al. (2018).  NetPyNE: A GUI-based tool to build, simulate and analyze large-scale, data-driven network models in parallel NEURON. Society for Neuroscience 2018 (SFN '18).
Dura-Bernal, S., Neymotin S. A., Suter B. A., Kelley C., Tekin R., Shepherd G. M. G., et al. (2019).  Cross-frequency coupling and information flow in a multiscale model of M1 microcircuits. Society for Neuroscience (SFN'19).
Dura-Bernal, S., Majumdar A., Neymotin S., Sivagnanam S., Francis J. T., & Lytton WW. (2015).  A dynamic data-driven approach to closed-loop neuroprosthetics based on multiscale biomimetic brain models. IEEE Interanationl Conference on High Performance Computing 2015 Workshop: InfoSymbiotics/Dynamic Data Driven Applications Systems (DDDAS) for Smarter Systems, Bangalore, India.
Dura-Bernal, S., Garreau G., Andreou C. M., Andreou A. G., Georgiou J., Wennekers T., et al. (2011).  Human Action Categorization Using Ultrasound Micro-Doppler Signatures. HBU.
Dura-Bernal, S., Li K., Brockmeier A., Kerr C., Neymotin S., Principe J., et al. (2014).  Modulation of virtual arm trajectories via microstimulation in a spiking model of sensorimotor cortex. Computational Neuroscience Meeting (CNS '14). 15, P106.
Dura-Bernal, S., Chadderdon GL., Neymotin S., Francis JT., & Lytton WW. (2014).  Towards a real-time interface between a biomimetic model of sensorimotor cortex and a robotic arm. Pattern Recognit Lett. 36, 204–212.
Dura-Bernal, S., Chadderdon G.L.., Neymotin S., Zhou X., Przekwas A.., Francis J.T.., et al. (2013).  Virtual musculoskeletal arm and robotic arm driven by a biomimetic model of sensorimotor cortex with reinforcement learning. Signal Processing in Medicine and Biology Symposium (SPMB), 2013 IEEE. 1-5.
Dura-Bernal, S., Kerr C., Neymotin S., Suter B., Shepherd G., Francis J., et al. (2015).  Large-scale M1 microcircuit model with plastic input connections from biological PMd neurons used for prosthetic arm control. 24th Annual Computational Neuroscience Meeting (CNS15).
Dura-Bernal, S., Wennekers T.., & Denham S.. (2010).  The role of feedback in a hierarchical model of object perception. Proceedings of BICS 2010 - Brain Inspired Cognitive Systems 14-16 July 2010, Madrid, Spain..
Dura-Bernal, S., Neymotin S. A., Suter B. A., Shepherd G. M., & Lytton WW. (2017).  Long-range inputs and H-current regulate different modes of operation in a multiscale model of mouse M1 microcircuits. Society for Neuroscience 2017 (SFN '17).
Dura-Bernal, S., Li K., Brockmeier A., Kerr CC., Neymotin S., Principe J., et al. (2014).  Repairing lesions via microstimulation in a spiking network model driving a virtual arm.. Society for Neuroscience.
Dura-Bernal, S., Wennekers T., & Denham S. L. (2012).  Top-Down Feedback in an HMAX-Like Cortical Model of Object Perception Based on Hierarchical Bayesian Networks and Belief Propagation. PLoS One. 7, e48216.
Dura-Bernal, S., Zhou X., Chadderdon G. L., Przekwas A., & Lytton WW. (2013).  Interfacing a biomimetic model of sensorimotor cortex with a musculoskeletal model and a robotic arm. Society for Neuroscience 2013 (SFN '13).
Dura-Bernal, S., Neymotin S., Kerr CC., Sivagnanam S., Majumdar A., Francis JT., et al. (2017).  Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis. IBM Journal of Research and Development. 61, 6–1.
Dura-Bernal, S., Gleeson P., Neymotin S., Suter B. A., Quintana A., Cantarelli M., et al. (2018).  NetPyNE: a high-level interface to NEURON to facilitate the development, parallel simulation and analysis of data-driven multiscale network models. Computational Neuroscience Meeting (CNS 18').
Dura-Bernal, S., Suter B. A., Gleeson P., Cantarelli M., Quintana A., Rodriguez F., et al. (2019).  NetPyNE, a tool for data-driven multiscale modeling of brain circuits. eLife. 8, e44494.
Dura-Bernal, S., Prins N., Neymotin S., Prasad A., Sanchez J., Francis JT., et al. (2014).  Evaluating Hebbian reinforcement learning BMI using an in silico brain model and a virtual musculoskeletal arm.. Neural Control of Movement.
Dura-Bernal, S., Garreau G., Georgiou J., Andreou A. G., Denham S. L., & Wennekers T. (2013).  Multimodal integration of micro-Doppler sonar and auditory signals for behavior classification with convolutional networks. International Journal of Neural Systems. 23, 1350021.
Dura-Bernal, S., Suter B. A., Neymotin S. A., Quintana A., Gleeson P., Sheperd G. M. G., et al. (2015).  Normalized cortical depth (NCD) as a primary coordinate system for cell connectivity in cortex: experiment and model. Society for Neuroscience 2015 (SFN '15).
Dura-Bernal, S., Menzies R. J., McLauchlan C., van Albada S. J., Kedziora D. J., Neymotin S., et al. (2016).  Effect of network size on computational capacity. Computational Neuroscience Meeting (CNS 16').
Dura-Bernal, S., Li K., Neymotin S., Francis JT., Principe JC., & Lytton WW. (2016).  Restoring behavior via inverse neurocontroller in a lesioned cortical spiking model driving a virtual arm. Front Neurosci. 10, 28.
Dura-Bernal, S.., Neymotin S.. A., Suter B.. A., Kelley C.., Tekin R.., Shepherd G.. M., et al. (2019).  Response to simultaneous long-range inputs and oscillatory inputs in a multiscale model of M1 microcircuits. Society for Neuroscience 2019 (SFN '19).
Dura-Bernal, S., Gleeson P., Kerr C., Graham J., Quintana A., & Lytton WW. (2018).  Neurosim-lab/netpyne: v0.7.7.
Dura-Bernal, S., BA S., Kerr C. C., Quintana A., Gleeson P., Shepherd GMG., et al. (2016).  NetPyNE: a Python package for NEURON to facilitate development and parallel simulation of biological neuronal networks. Computational Neuroscience.
Dura-Bernal, S., Suter B. A., Neymotin S. A., Kerr C. C., Quintana A., Gleeson P., et al. (2016).  NetPyNE: a Python package for NEURON to facilitate development and parallel simulation of biological neuronal networks. Computational Neuroscience Meeting (CNS 16').
E
Eguchi, A., Neymotin S., & Stringer SM. (2014).  Color opponent receptive fields self-organize in a biophysical model of visual cortex via spike-timing dependent plasticity. Front Neural Circuits. 8, 16.
Erdemir, A., Mulugeta L., & Lytton WW. (2015).  Ten "not so simple" rules for credible practice of modeling & simulation in healthcare. Frontiers in Medical Devices Meeting.
F
Fenton, AA., Kao HY., Neymotin S., Olypher A., Vayntrub Y., Lytton WW., et al. (2008).  Unmasking the CA1 ensemble place code by exposures to small and large environments: more place cells and multiple, irregularly arranged, and expanded place fields in the larger space. jnsci. 28, 11250-11262.
Fenton, AA., Lytton WW., Barry JM., Lenck-Santini PP., Zinyuk LE., Kubík S., et al. (2010).  Attention-like modulation of hippocampus place cell discharge. J Neurosci. 30, 4613-4625.
Fenton, A. A., Lee H., & Lytton WW. (2009).  Disinhibition can account for the neural discoordination associated with impaired cognitive control. Society for Neuroscience 2009 (SFN '09).
Forgacs, PB., Gizycki H., Selesnick I., Syed NA., Ebrahim K., Avitable M., et al. (2008).  Perisaccadic parietal and occipital gamma power in light and in complete darkness. Perception. 37, 419-432.
Francis, J. T., Chapin J., Lytton WW., Barbour R., Carmena J., Principe J., et al. (2010).  Creating the synthetic brain through hybrid computational and biological systems repairing and replacing neural networks. Society for Neuroscience 2010 (SFN '10).
G
Gao, P., Graham J. W., Angulo S., Dura-Bernal S., Lytton WW., & Antic S. D. (2017).  Dendritic plateau generation model in cortical pyramidal neurons: A link to cortical ensembles. Society for Neuroscience 2017 (SFN '17).
Gao, P. P., Graham J. W., Angulo S. L., Dura-Bernal S., Hines M. L., Lytton WW., et al. (2018).  Recruitment of neurons into neural ensembles based on dendritic plateau potentials. Society for Neuroscience 2018 (SFN '18).
Gao, P., Graham J., Angulo S., Dura-Bernal S., Hines M., Lytton W. W., et al. (2018).  Recruitment of neurons into neural ensembles based on dendritic plateau potentials. Computational Neuroscience Meeting (CNS 18').
Gleeson, P., Cantarelli M., Marin B., Quintana A., Earnshaw M., Piasini E., et al. (2018).  Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits. bioRxiv. 229484.
Gleeson, P., Marin B., Sadeh S., Quintana A., Cantarelli M., Dura-Bernal S., et al. (2016).  A set of curated cortical models at multiple scales on Open Source Brain. Computational Neuroscience Meeting (CNS 16').
Graham, J. W., Angulo S., Gao P. P., Dura-Bernal S., Sivagnanam S., Hines M.., et al. (2018).  Embedded ensemble encoding: A hypothesis for reconciling cortical coding strategies. Society for Neuroscience 2018 (SFN '18).
Graham, J.. W., Gao P.. P., Dura-Bernal S.., Sivagnanam S.., Hines M.. L., Antic S.. D., et al. (2019).  Modeling network effects of dendritic plateau potentials in cortical pyramidal neurons. Society for Neuroscience 2019 (SFN '19).
Günay, C., Smolinski TG., Lytton WW., Morse TM., Gleeson P., Crook S., et al. (2008).  Computational Intelligence in Electrophysiology. Studies in Computational Intelligence. 122, 325-359.
H
Hilscher, M. M., Moulin T., Skolnick Y., Lytton W. W., & Neymotin S. A. (2012).  Ih modulates theta rhythm and synchrony in computer model of CA3. Computational Neuroscience Meeting (CNS 12').
Hines, M. L., Mcdougal R., Neymotin S. A., Tropper C., & Lytton WW. (2013).  Interfaces in multiscale reaction-diffusion models in the NEURON simulator. Society for Neuroscience 2013 (SFN '13).
Holmes, W. R., Jung R., & Skinner F. (2007).  Computational Neuroscience (CNS*2007). BMC Neuroscience. 8, I1.
Holmes, W. R., Jung R., & Roberts P. (2008).  Computational Neuroscience (CNS*2008). BMC Neuroscience. 9, I1.
C Hunt, A., Erdemir A., Gabhann F. Mac, Lytton WW., Sander E. A., Transtrum M. K., et al. (2018).  The Spectrum of Mechanism-oriented Models for Explanations of Biological Phenomena.
J
Johnson, D. H., Jung R., & Ernst U. (2009).  Computational Neuroscience (CNS*2009). BMC Neuroscience. 10, I1.
K
Kapur, A., Lytton WW., Ketchum K., & Haberly L. (1997).  Regulation of the NMDA component of EPSPs by different components of postsynaptic GABAergic inhibition: A computer simulation analysis in piriform cortex. jnphys. 78, 2546-2559.
Kapur, A., Pearce R., Lytton WW., & L H. (1997).  \gabaa-mediated IPSCs in piriform cortex have fast and slow components with different properties and locations on pyramidal cells: Study with physiological and modeling methods. jnphys. 78, 2531-2545.
Kelley, C.., Dura-Bernal S.., Neymotin S.., & Lytton W.. W. (2019).  Dendritic resonance in a detailed model of pyramidal tract neuron of mouse primary motor cortex. Society for Neuroscience 2019 (SFN '19).