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Found 22 results
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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. L. (2012).  Top-Down Feedback in an HMAX-Like Cortical Model of Object Perception Based on Hierarchical Bayesian Networks and Belief Propagation. PLoS One. 7, e48216.
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., 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., Li K., Brockmeier A., Kerr CC., Neymotin S., Principe J., et al. (2014).  Repairing lesions via microstimulation in a spiking network model driving a virtual arm.. 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.
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., 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).
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., 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., 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., 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., Garreau G., Georgiou J., Andreou A. G., Denham S. L., & Wennekers T. (2013).  Multimodal integration of micro-Doppler sonar and auditory signals for behavior classification with convolutional networks. International Journal of Neural Systems. 23, 1350021.
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..
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., 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., 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., Suter B. A., Neymotin S. A., Kerr C. C., Quintana A., Gleeson P., et al. (2016).  NetPyNE: a Python package for NEURON to facilitate development and parallel simulation of biological neuronal networks. Computational Neuroscience Meeting (CNS 16').
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').
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., 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).