Biblio

Found 179 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
Antic, S. D., Hines M., & Lytton WW. (2018).  Embedded ensemble encoding hypothesis: The role of the ``Prepared'' cell. J. Neurosci. Res..
B
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.
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,
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.
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.
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.
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.
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., 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., 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., 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., 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., 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).