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Found 52 results
Author [ Title(Desc)] Type Year
Filters: Author is Samuel Neymotin  [Clear All Filters]
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B
Neymotin, S., Taxin ZH., Mohan A., & Lipton P. (2013).  Brain Ischemia and Stroke. (Jaeger, D., & Jang R., Ed.).Encyclopedia of Computational Neuroscience.
C
Neymotin, S., McDougal R. A., Bulanova AS., Zeki M., Lakatos P., Terman D., et al. (2016).  Calcium regulation of HCN channels supports persistent activity in a multiscale model of neocortex. Neurosci. 316, 344-366.
Neymotin, S., McDougal R. A., Hines ML., & Lytton WW. (2014).  Calcium regulation of HCN supports persistent activity associated with working memory: a multiscale model of prefrontal cortex. BMC Neuroscience. 15, P108.
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.
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.
Neymotin, S., Mathew A.M.., Kerr C.., & Lytton WW. (2013).  Computational Neuroscience of Neuronal Networks. (Pfaff, D., Ed.).
Neymotin, S., Dura-Bernal S., Moreno H., & Lytton WW. (2017).  Computer modeling for pharmacological treatments for dystonia. Drug Discov Today: Dis Model. In Press.
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.
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,
D
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., 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.
Neymotin, S., Lytton WW., O'Connell MN., & Lakatos P. (2013).  Dynamical microstates in primary auditory cortex. Society for Neuroscience Abstracts. 43,