Lab Publications | Neurosim lab

Found 267 results

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L

Lytton, WW., Neymotin S., Lee HY., Uhlrich DJ., & Fenton AA. (2008). Circuit changes augment disinhibited shock responses in computer models of neocortex. American Epilepsy Society Annual Meeting. 3, 284.

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

Lytton, WW., & Stewart M. (2006). Rule-based firing for network simulations. Neurocomputing. 69, 1160-1164.

Lytton, WW., Orman R., & Stewart M. (2008). Broadening of activity with flow across neural structures. Perception. 37, 401-407.

Lytton, WW., & Wathey J. C. (1992). Realistic single-neuron modeling. Seminars in Neuroscience. 4, 15-25.

Lytton, WW. (1991). Simulations of a phase comparing neuron of the electric fish Eigenmannia. 169, 117-125.

Lytton, WW. (1997). Brain organization: from molecules to parallel processing. (Trimble, MR., & Cummings JL., Ed.).Contemporary Behavioral Neurology. 5-28.

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.

Lytton, WW., & Sejnowski TJ. (1992). Computational Neuroscience. (Asbury, AK., McKhann GM., & McDonald WI., Ed.).Diseases of the Nervous System: Clinical Neurobiology.

Lytton, WW. (1997). A computer model of clonazepam's effect in a thalamic slice model of absence epilepsy. Neuroreport. 8, 3339-3343.

Lytton, WW. (2006). Neural query system: data-mining from within the NEURON simulator. Neuroinformatics. 4, 163-176.

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

Lytton, WW., & Sejnowski TJ. (1991). Simulations of cortical pyramidal neurons synchronized by inhibitory interneurons. 66, 1059-1079.

Lytton, WW., & Lipton P. (1999). Can the hippocampus tell time?: The temporo-septal engram shift model. Neuroreport. 10, 2301-2306.

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

Lin, Z., Tropper C., Patoary M. Nazrul Ish, McDougal R. A., Lytton WW., & Hines M. L. (2015). Ntw-mt: A multi-threaded simulator for reaction diffusion simulations in neuron. Proceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation. 157–167.

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.

Lin, Z., Tropper C., Yao Y., McDougal R. A., Patoary MN., Lytton WW., et al. (2017). Load balancing for multi-threaded PDES of stochastic reaction-diffusion in neurons. J Simulation. 11, 267.

Li, K., Dura-Bernal S., Francis J., Lytton WW., & Principe J. (2015). Repairing Lesions via Kernel Adaptive Inverse Control in a Biomimetic Model of Sensorimotor Cortex. Neural Engineering (NER), 2015 7th International IEEE/EMBS Conference. 1-4.

Lee, GJ., Matsunaga A., Dura-Bernal S., Zhang W., Lytton WW., Francis JT., et al. (2014). Towards real-time communication between in vivo neurophysiological data sources and simulator-based brain biomimetic models. Journal of Computational Surgery. 3, 12.

Lazarewicz, M. T., Contreras D., Finkel L. H., & Lytton WW. (2009). Computer model of a theta-gamma dissociation in hippocampus. Society for Neuroscience 2009 (SFN '09).

Lakatos, P., Barczak A., Neymotin S., Lytton WW., McGinnis T., Javitt D., et al. (2014). Thalamocortical dynamics of rhythmic selective and tonic suppressive modes in the auditory system. Society for Neuroscience Abstracts. 44,

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.

Lakatos, P., Barczak A., Neymotin S. A., Lytton WW., Mcginnis T., Javitt D. C., et al. (2014). Thalamocortical dynamics of rhythmic selective and tonic suppressive modes in the auditory system. Society for Neuroscience 2014 (SFN '14).

K

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).

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.

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.

Kerr, C. C., Dura-Bernal S., Smolinski T. G., Chadderdon G. L., & Wilson D. P. (2018). Optimization by Adaptive Stochastic Descent. PLOS ONE. 13, 1-16.

Kerr, C., Von Kraus L., Iordanou J., Neymotin S. A., Francis J., & Lytton WW. (2013). Receptive field formation and erasure in somatosensory cortex. Society for Neuroscience 2013 (SFN '13).

Kerr, C., Choi J. S., Dura-Bernal S., Francis J. T., & Lytton WW. (2014). One size does not fit all: Calibrating microstimulation to individual subjects using spiking network models. Society for Neuroscience 2014 (SFN '14).

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, 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').

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.

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.

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.

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).

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, 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.

Kerr, C., Dura-Bernal S., Menzies R. J., Mclauchlan C., Van Albada S. J., Kedziora D. J., et al. (2016). Computational capacity as a function of network size. Society for Neuroscience 2016 (SFN '16).

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.

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).

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).

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.

J

Johnson, D. H., Jung R., & Ernst U. (2009). Computational Neuroscience (CNS*2009). BMC Neuroscience. 10, I1.

H

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.

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.

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).

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').

G

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.

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).

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').

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').

F

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).

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.

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).

E

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.

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.

D

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., 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., 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., 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., 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..