JitCon: Just in time connectivity for large spiking networks (Lytton et al. 2008)

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• Spatial gridding and temporal accuracy in NEURON (Hines and Carnevale 2001) [Model]

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• Local variable time step method (Lytton, Hines 2005) [Model]

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• Tonic-clonic transitions in a seizure simulation (Lytton and Omurtag 2007) [Model]

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• Parallel network simulations with NEURON (Migliore et al 2006) [Model]

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• Motor cortex microcircuit simulation based on brain activity mapping (Chadderdon et al. 2014) [Model]

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• Reinforcement learning of targeted movement (Chadderdon et al. 2012) [Model]

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• Connection-set Algebra (CSA) for the representation of connectivity in NN models (Djurfeldt 2012) [Model]

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• Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015) [Model]

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• Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015) [Model]

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• Prosthetic electrostimulation for information flow repair in a neocortical simulation (Kerr 2012) [Model]

Kerr CC et al. (2013). Cortical information flow in Parkinson's disease: a composite network/field model. Frontiers in computational neuroscience. 7 [PubMed]

• Composite spiking network/neural field model of Parkinsons (Kerr et al 2013) [Model]

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• The virtual slice setup (Lytton et al. 2008) [Model]

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• Sensorimotor cortex reinforcement learning of 2-joint virtual arm reaching (Neymotin et al. 2013) [Model]

Neymotin SA, Lee H, Park E, Fenton AA, Lytton WW. (2011). Emergence of physiological oscillation frequencies in a computer model of neocortex. Frontiers in computational neuroscience. 5 [PubMed]

• Emergence of physiological oscillation frequencies in neocortex simulations (Neymotin et al. 2011) [Model]

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• Synaptic scaling balances learning in a spiking model of neocortex (Rowan & Neymotin 2013) [Model]

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• Electrostimulation to reduce synaptic scaling driven progression of Alzheimers (Rowan et al. 2014) [Model]