Lab Publications

Found 230 results
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Kerr, CC., Neymotin S., Chadderdon GL., Fietkiewicz CT., Francis JT., & Lytton WW. (2012).  Electrostimulation as a prosthesis for repair of information flow in a computer model of neocortex. IEEE Trans Neural Syst Rehab Eng. 20, 153–60.
Rowan, MS., Neymotin S., & Lytton WW. (2014).  Electrostimulation to reduce synaptic scaling driven progression of Alzheimer's disease. Front Comput Neurosci. 8, 39.
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).
Antic, S. D., Hines M., & Lytton WW. (2018).  Embedded ensemble encoding hypothesis: The role of the ``Prepared'' cell. J. Neurosci. Res..
Neymotin, S., Lee HY., Park EH., Fenton AA., & Lytton WW. (2011).  Emergence of physiological oscillation frequencies in a computer model of neocortex. Front Comput Neurosci. 5, 19.
Neymotin, SA., H L., Park EH., AA F., & Lytton WW. (2010).  Emergent oscillations in neocortex: a simulation study. Dynamical Neuroscience XVIII: The Resting Brain: Not at Rest! Satellite meeting for Society for Neuroscience Meeting.
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., 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.
Mcdougal, R. A., Tropper C., Hines M. L., & Lytton WW. (2016).  Expanding NEURON support for reaction-diffusion models. Society for Neuroscience 2016 (SFN '16).
Newton, A. J. H., Seidenstein A. H., McDougal R. A., Hines M., & Lytton W. W. (2018).  Extracellular reaction–diffusion in the NEURON simulator: modeling ischemic stroke. Computational Neuroscience Meeting (CNS 18').
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Mcdougal, R. A., Bulanova A. S., Hines M. L., & Lytton WW. (2015).  Hybrid 1d/3d reaction-diffusion in the neuron simulator. Society for Neuroscience 2015 (SFN '15).
Lytton, WW., & Hines M. (2004).  Hybrid neural networks - combining abstract and realistic neural units. IEEE Engineering in Medicine and Biology Society Proceedings. 6, 3996-3998.
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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).
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').
Neymotin, S., Hilscher MM., Moulin TC., Skolnick Y., Lazarewicz MT., & Lytton WW. (2013).  Ih Tunes theta/gamma oscillations and cross-frequency coupling in an in silico CA3 model. PLoS One. 8, e76285.
Tepper, Á., Sugi A., Lytton W. W., & Dura-Bernal S. (2018).  Implementation of Cmicrocircuits model in NetPyNE and exploration of the effect of neuronal/synaptic loss on memory recall. Computational Neuroscience Meeting (CNS 18').
Briska, AM., Uhlrich DJ., & Lytton WW. (2000).  Independent dendritic domains in the thalamic circuit. Neurocomputing. 32, 299–305.
Lytton, WW., & Hines M. (2005).  Independent variable timestep integration of individual neurons for network simulations. 17, 903-921.
Kerr, C. C., O'Shea D. J., Goo W., Dura-Bernal S., Diester I., Kalanithi P., et al. (2014).  Information flow in optogenetically stimulated macaque motor cortex: simulation and experiment. Neural Control of Movement (NCM) meeting.
Neymotin, S. A., Jacobs K. M., & Lytton WW. (2009).  Information transmission vs processing in computer models of neocortical columns. Society for Neuroscience 2009 (SFN '09).
Deyo, S., & Lytton WW. (1997).  Inhibition Can Disrupt Hypersynchrony In Model Neuronal Networks. Progress in neuro-psychopharmacology & biological psychiatry.
Olypher, AV., Lytton WW., & Prinz AA. (2012).  Input-to-output transformation in a model of the rat hippocampal CA1 network. Front Comput Neurosci. 6, 57.
Bulanova, AS., McDougal RA., Neymotin SA., Mutai V., Lytton WW., & Hines M. (2014).  Integrating Systems Biology Markup Language (SBML) with NEURON. Computational Neuroscience.
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).
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).
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).
Neymotin, S., Lee HY., Fenton AA., & Lytton WW. (2010).  Interictal EEG Discoordination in a Rat Seizure Model. J Clin Neurophysiol. 27, 438–444.
Neymotin, SA., H L., AA F., & Lytton WW. (2010).  Interictal EEG discoordination in a rat seizure model. Statistical Analysis of Neuronal Data.
Kerr, CC., Neymotin SA., Mo J., Schroeder CE., M D., & Lytton WW. (2011).  Interlaminar feedback connections dominate in macaque inferotemporal cortex: in vivo and in silico studies. Society for Neuroscience.
Kerr, CC., Mo J., Neymotin SA., M D., & Lytton WW. (2011).  Interlaminar Granger causality and alpha oscillations in a model of macaque cortex. Computational Neuroscience.
Zhu, JJ., Lytton WW., Xue JT., & Uhlrich D. (1999).  An intrinsic oscillation in interneurons of the rat lateral geniculate nucleus. jnphys. 81, 702-711.
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Womack, KB., Paliotta C., Strain JF., Ho JS., Skolnick Y., Lytton WW., et al. (2017).  Measurement of peripheral vision reaction time identifies white matter disruption in patients with mild traumatic brain injury. J Neurotrauma.
Neymotin, S., Lytton WW., Olypher AO., & AA F. (2011).  Measuring the quality of neuronal identification in ensemble recordings. jnsci. 31, 16398–16409.
McDougal, RA., Hines ML., & Lytton WW. (2014).  A method for multi-simulator reaction-diffusion with NEURON. Computational Neuroscience.
Neymotin, SA., Salvador D-B., Suter BA., Migliore M., Shepherd GMG., & Lytton WW. (2015).  Microconnectomics of primary motor cortex: a multiscale computer model. Multiscale Modeling. National Institutes of Health.
Newton, A., Mcdougal R. A., Hines M. L., Miyazaki K., Ross W. N., & Lytton WW. (2017).  Modeling electrodiffusion with the NEURON reaction-diffusion module. Society for Neuroscience 2017 (SFN '17).
Taxin, ZH., Neymotin S., Mohan A., Lipton P., & Lytton WW. (2014).  Modeling molecular pathways of neuronal ischemia. Prog Mol Biol Transl Sci. 123, 249–275.
Knox, A. T., Glauser T., Tenney J., Lytton WW., & Holland K. (2018).  Modeling pathogenesis and treatment response in childhood absence epilepsy. Epilepsia. 59, 135–145.
Lytton, WW., & Thomas E. (1999).  Modeling thalamocortical oscillations. (Ulinski, P., Jones EG., & Peters A., Ed.).Cerebral Cortex. 13, 479-509.
Kerr, CC., Neymotin SA., Song W., Francis JT., & Lytton WW. (2011).  Modulation of stimulus fields in a computer model of the thalamocortical system. Society for Neuroscience.
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.
Seidenstein, A., Mcdougal R. A., Hines M. L., & Lytton WW. (2016).  Mosaic multiscale computer modeling of ischemic stroke. Society for Neuroscience 2016 (SFN '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.
Neymotin, SA., BA S., Migliore M., Salvador D-B., Shepherd GMG., & Lytton WW. (2015).  Motor cortex neurons: from experiment to model via evolutionary algorithms. Computational Neuroscience.
Lytton, WW., Vadigepalli R., & Kramer MA. (2015).  Multiscale Computational Modeling for the US BRAIN initiative.
Seidenstein, A., Newton A., Macdougal R. A., & Lytton WW. (2017).  Multiscale computer modeling of penumbral zones in brain ischemia. Society for Neuroscience 2017 (SFN '17).
Lytton, WW., Neymotin S., & Kerr CC. (2014).  Multiscale modeling for clinical translation in neuropsychiatric disease. J Comput Surgery. 1, 7.
Lytton, WW., Arle J., Bobashev G., Ji S., Klassen TL., Marmarelis VZ., et al. (2017).  Multiscale modeling in the clinic: diseases of the brain and nervous system. Brain Inform. 4, 219-230.
Lytton, WW. (2018).  Multiscale modeling of brain disease. Society for Neuroscience 2018 (SFN '18).
Kerr, C. C., Van Albada S. J., Neymotin S. A., Chadderdon, Iii G. L., Robinson P. A., & Lytton W. W. (2013).  Multiscale modeling of cortical information flow in Parkinson's disease. Computational Neuroscience Meeting (CNS '13).
Newton, A., Seidenstein A. H., Hines M. L., Mcdougal R. A., & Lytton WW. (2018).  Multiscale simulation of spreading depolarization in ischemic stroke. Society for Neuroscience 2018 (SFN '18).
Neymotin, S., Dura-Bernal S., Lakatos P., Sanger TD., & Lytton WW. (2016).  Multitarget multiscale simulation for pharmacological treatment of dystonia in motor cortex. Front Pharmacol. 7, 157.
Lin, Z., Tropper C., McDougal R. A., Patoary M. Nazrul Ish, Lytton WW., Yao Y., et al. (2017).  Multithreaded Stochastic PDES for Reactions and Diffusions in Neurons. ACM Transactions on Modeling and Computer Simulation (TOMACS). 27, 7.
Zhu, JJ., Uhlrich D., & Lytton WW. (1996).  Muscarinic receptor mediated responses in thalamic local interneurons. snabs. 22, 574.8.
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Lytton, WW., Neymotin S., Wester JC., & Contreras D. (2014).  Neocortical simulation for epilepsy surgery guidance: localization and intervention. (Bass, B., & Garbey M., Ed.).Computational Surgery and Dual Training. 339–349.
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., 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., 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.
Mohan, A., Chadderdon G. L., Suter B. A., Shepherd G. M. G., & Lytton WW. (2013).  Network-level coincidence detection in a computer simulation of primary motor cortex. Society for Neuroscience 2013 (SFN '13).
Kerr, CC., O'Shea DJ., Goo W., Dura-Bernal S., Francis JT., Diester I., et al. (2014).  Network-level effects of optogenetic stimulation in a computer model of macaque primary motor cortex. BMC Neuroscience. 15, P107.
Kerr, C. C., O'Shea D. J., Goo W., Dura-Bernal S., Francis J. T., Diester I., et al. (2014).  Network-level effects of optogenetic stimulation in a computer model of macaque primary motor cortex. Computational Neuroscience Meeting (CNS 14').
Lytton, WW. (2006).  Neural query system: data-mining from within the NEURON simulator. Neuroinformatics. 4, 163-176.