Lab Publications

Olypher, A. V., Fenton A. A., Lytton W. W., & Prinz A. A. (2009).  Information processing in homeostatically regulated hippocampal neurons. Society for Neuroscience 2009 (SFN '09).

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.

Lytton, WW., & Hines M. (2005).  Independent variable timestep integration of individual neurons for network simulations. 17, 903-921.

Briska, AM., Uhlrich DJ., & Lytton WW. (2000).  Independent dendritic domains in the thalamic circuit. Neurocomputing. 32, 299–305.

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').

Sanjay, M., Neymotin S., & Babu KS. (2015).  Impaired dendritic inhibition leads to epileptic activity in a computer model of CA3. Hippocampus. in press.

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.

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').

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).

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.

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).

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.

Newton, A. J. H., Hines M. L., Lytton W. W., & McDougal R. A. (2019).  Homeostasis and spreading depolarization in multiscale simulation of ischemic stroke.. Society for Neuroscience 2018 (SFN '19).

Kubie, J. L., Fenton A. A., Lytton WW., & Burgess N. (2009).  Grid-cell models for navigation and context discrimination. Society for Neuroscience 2009 (SFN '09).

Lakatos, P., Barczak A., Neymotin S., McGinnis T., Ross D., Javitt DC., et al. (2016).  Global dynamics of selective attention and its lapses in primary auditory cortex. Nat Neurosci. In press.

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').

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.

Neymotin, S., Sherif M. A., Jung J. Q., Kabariti J. J., & Lytton WW. (2018).  Genome-wide associations of schizophrenia studied with computer simulation. (Cutsuridis, V., Graham BP., Cobb S., & Vida I., Ed.).Hippocampal Microcircuits: A Computational Modeler's Resource Book. 2,

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.

Lytton, WW. (2002).  From Computer to Brain.

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').

Mcdougal, R. A., Tropper C., Hines M. L., & Lytton WW. (2016).  Expanding NEURON support for reaction-diffusion models. Society for Neuroscience 2016 (SFN '16).

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., 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.

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.

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.

Antic, S. D., Hines M., & Lytton WW. (2018).  Embedded ensemble encoding hypothesis: The role of the ``Prepared'' cell. J. Neurosci. Res..

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).

Rowan, MS., Neymotin S., & Lytton WW. (2014).  Electrostimulation to reduce synaptic scaling driven progression of Alzheimer's disease. Front Comput Neurosci. 8, 39.

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.

Newton, A.. J. H., Conte C.., Eggleston L.., Blasy E.., Hines M.. L., Lytton W.. W., et al. (2019).  Efficient in silico 3D intracellular neuron simulation. Society for Neuroscience 2019 (SFN '19).

Kerr, CC., van Albada SJ., Chadderdon GL., Neymotin SA., Robinson PA., & Lytton WW. (2012).  Effects of basal ganglia on cortical computation: a hybrid network/neural field model. Society for Neuroscience.

Kerr, C., Van Albada S. J., Neymotin S. A., Chadderdon G. L., Robinson P. A., & Lytton WW. (2012).  Effects of basal ganglia on cortical computation: A hybrid network/neural field model. Society for Neuroscience 2012 (SFN '12).

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').

Sanchez, J., Lytton WW., Carmena J., Principe J., Fortes J., Barbour R., et al. (2012).  Dynamically repairing and replacing neural networks: using hybrid computational and biological tools. {IEEE} Pulse. 3, 57-59.

Neymotin, S., Lytton WW., O'Connell MN., & Lakatos P. (2013).  Dynamical microstates in primary auditory cortex. Society for Neuroscience Abstracts. 43,

Neymotin, S. A., Lytton WW., Oconnell M. N., & Lakatos P. (2013).  Dynamical microstates in primary auditory cortex. Society for Neuroscience 2013 (SFN '13).

Lytton, WW., Contreras D., Destexhe A., & Steriade M. (1997).  Dynamic interactions determine partial thalamic quiescence in a computer network model of spike-and-wave seizures. jnphys. 77, 1679-1696.

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.

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., 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.

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).

Lytton, WW., & Brust JC. (1989).  Direct dyslexia: Preserved oral reading of real words in Wernicke's aphasia. Brain. 112, 583-594.

Kerr, CC., Fietkiewicz CT., Chadderdon GL., Neymotin SA., & Lytton WW. (2010).  Development of In Silico Brain for DARPA REPAIR project. DARPA Neural Engineering, Science, and Technology Meeting.

Lytton, WW., & Stewart M. (2002).  Dendritic resonance in in subicular dendrites, a computer model. 28,

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).

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).

Suter, B. A., Neymotin S. A., Shepherd G. M. G., & Lytton WW. (2016).  Dendritic morphology of corticospinal and crossed-corticostriatal neurons in mouse primary motor cortex. Society for Neuroscience 2016 (SFN '16).

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).

Lytton, WW., & Stewart M. (2007).  Data mining through simulation. Methods Mol Biol. 401, 155-166.

Neymotin, S., Uhlrich DJ., Manning KA., & Lytton WW. (2008).  Data mining of time-domain features from neural extracellular field data. Studies in Computational Intelligence. 151, 119-140.

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).

Mulugeta, L., Drach A., Erdemir A., Hunt C. A., Horner M., Ku J. P., et al. (2018).  Credibility, Replicability, and Reproducibility in Simulation for Biomedicine and Clinical Applications in Neuroscience. Front. Neuroinform.. 12,

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).

Sherif, M. A., Barry J. M., Neymotin S. A., & Lytton W. W. (2012).  CPP alters theta/gamma oscillations in rat hippocampus: simulation and experiment. Computational Neuroscience Meeting (CNS 12').

Sherif, MA., Barry JM., Neymotin SA., & Lytton WW. (2012).  CPP alters hippocampal CA1 oscillations in rat: simulation and experiment. Computational Neuroscience.

Sherif, M. A., Barry J. M., Neymotin S. A., & Lytton WW. (2012).  CPP alters cross-frequency coupling between theta and gamma in CA1 in rats: Simulation and experiment. Society for Neuroscience 2012 (SFN '12).

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,

Song, W., Kerr CC., Lytton WW., & Francis JT. (2013).  Cortical plasticity induced by spike-triggered microstimulation in primate somatosensory cortex. PLoS One. 8, e57453.

Kerr, CC., van Albada SJ., Neymotin S., Chadderdon GL., Robinson PA., & Lytton WW. (2013).  Cortical information flow in Parkinson's disease: a composite network/field model. Front Comput Neurosci. 7, 39.

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).

Lytton, WW., Destexhe A., & Sejnowski TJ. (1996).  Control of slow oscillations in the thalamocortical neuron: A computer model. Neuroscience. 70, 673-684.

Lytton, WW. (2017).  Computers, causality and cure in epilepsy. Brain. 140, 516-519.

Wathey, JC., Lytton WW., Jester JM., & Sejnowski TJ. (1992).  Computer simulations of EPSP-to-spike (E-S) potentiation in hippocampal CA1 pyramidal cells. 12, 607-618.

Lytton, WW., Orman R., & Stewart M. (2005).  Computer simulation of epilepsy: implications for seizure spread and behavioral dysfunction. Epilepsy & Behavior. 7, 336-344.

Lytton, WW., & Shepherd G. M. G. (2010).  Computer network model predicts dependence of neocortical laminar activation patterns on form of stimulation. Society for Neuroscience 2010 (SFN '10).

Lytton, WW., Hellman KM., & Sutula TP. (1996).  Computer network model of mossy fiber sprouting in dentate gyrus. Epilepsia – AES Proceedings. 37 S. 5, 117.

Lytton, WW., Stark JM., Yamasaki DS., & Sober SJ. (1999).  Computer models of stroke recovery: Implications for neurorehabilitation. The Neuroscientist. 5, 100-111.

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).

Lytton, WW., Hellman KM., & Sutula TP. (1998).  Computer Models of Hippocampal Circuit Changes of the Kindling Model of Epilepsy. Artificial Intelligence in Medicine. 13, 81-98.

Lytton, WW. (2008).  Computer modelling of epilepsy. Nat Rev Neurosci. 9, 626-637.

Newton, AJH., & Lytton WW. (2017).  Computer modeling of ischemic stroke. Drug Discov Today: Dis Model. 30, In press.

Seidenstein, AH., Barone FC., & Lytton WW. (2015).  Computer modeling of ischemic stroke. Scholarpedia. 10, 32015; revision \#148671; Accessed Oct 12, 2015.

Lytton, WW. (2017).  Computer modeling of epilepsy: opportunities for drug discovery. Drug Discov Today: Dis Model. In press.

Neymotin, S., Dura-Bernal S., Moreno H., & Lytton WW. (2017).  Computer modeling for pharmacological treatments for dystonia. Drug Discov Today: Dis Model. In Press.