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In Press
Crawford, E., Gingerich M., & Eliasmith C. (In Press).  Biologically Plausible, Human-scale Knowledge Representation. CogSci.
Hunsberger, E., Blouw P., Bergstra J., & Eliasmith C. (In Press).  A Neural Model of Human Image Categorization. CogSci.
Stewart, T. C., & Eliasmith C. (In Press).  Realistic neurons can compute the operations needed by quantum probability theory and other vector symbolic architectures. Behavioral and Brain Sciences Commentary.
2013
Orchard, J., Yang H., & Ji X. (2013).  Does the Entorhinal Cortex use the Fourier Transform?. OrchardTR2013.pdf (2.06 MB)
Rasmussen, D., & Eliasmith C. (2013).  God, the devil, and details: Fleshing out the predictive processing framework (commentary on Clark). Behavioral and Brain Sciences. 36, 223-224. Rasmussen, Eliasmith - 2013 - God, the devil, and details Fleshing out the predictive processing framework (commentary on Clark).pdf (1.19 MB)
Hunsberger, E., Scott M., & Eliasmith C. (2013).  Heterogeneity Increases Information Transmission in Neuronal Populations. Cognitive and Systems Neuroscience. Hunsberger.Cosyne2013.pdf (641.19 KB)
Eliasmith, C. (2013).  How to build a brain: A neural architecture for biological cognition.
Davies, S., Stewart T. C., Eliasmith C., & Furber S. (2013).  Spike-based learning of transfer functions with the SpiNNaker neuromimetic simulator. International Joint Conference on Neural Networks.
2012
Stewart, T. C., & Eliasmith C. (2012).  Compositionality and biologically plausible models. (Hinzen, W., Werning M., & Machery E., Ed.).Oxford Handbook of Compositionality. Stewart.Compositionality.pdf (1.42 MB)
Wu, Y. (2012).  In Search of Lost Time: A computational model of the representations and dynamics of episodic memory. Systems Design Engineering. Yan Wu Masters Thesis.pdf (1.56 MB)
Eliasmith, C., Stewart T. C., Choo X., Bekolay T., DeWolf T., Tang Y., et al. (2012).  A large-scale model of the functioning brain. Science. 338(6111), 1202-1205.
Stewart, T. C., Bekolay T., & Eliasmith C. (2012).  Learning to select actions with spiking neurons in the basal ganglia. Frontiers in Decision Neuroscience. 6(2), 
Hurzook, A. (2012).  A mechanistic model of motion processing in the early visual system. Systems Design Engineering. Master of Applied Science, 94. Hurzook_motion-processing-oscillator-interference.pdf (3.28 MB)
Eliasmith, C., & Stewart T. C. (2012).  Nengo and the Neural Engineering Framework: From Spikes to Cognition. Cognitive Science Society. 22-23. 2012-NengoTutorial.pdf (191.08 KB)
Stewart, T. C. (2012).  The Neural Engineering Framework. AISB Quarterly. 2-7.
Galluppi, F., Davies S., Stewart T. C., Eliasmith C., & Furber S. (2012).  Real Time On-Chip Implementation of Dynamical Systems with Spiking Neurons. IJCNN.
Choudhary, S., Sloan S., Fok S., Neckar A., Trautmann E., Gao P., et al. (2012).  Silicon Neurons that Compute. International Conference on Artificial Neural Networks. 7552, 121-28. NEFonNeurogrid (1).pdf (926.61 KB)
Stewart, T. C., Choo X., & Eliasmith C. (2012).  Spaun: A Perception-Cognition-Action Model Using Spiking Neurons. Cognitive Science Society. 1018-1023. 2012-Spaun.pdf (1.65 MB)
Bekolay, T., Liu B., Eliasmith C., & Laubach M. (2012).  A spiking neural model of strategy shifting in a simple reaction time task. Society for Neuroscience 2012. mpfc-FINAL.pdf (9.41 MB)
Stewart, T. C. (2012).  A Technical Overview of the Neural Engineering Framework. 2012-TheNEF-TechReport.pdf (567.78 KB)
2011
Bobier, B., Stewart T. C., & Eliasmith C. (2011).  The attentional routing circuit: receptive field modulation through nonlinear dendritic interactions. Cognitive and Systems Neuroscience. Bobier.ARCCosyne.pdf (637.3 KB)
Stewart, T. C., Tang Y., & Eliasmith C. (2011).  A Biologically Realistic Cleanup Memory: Autoassociation in Spiking Neurons. Cognitive Systems Research. 12(2), 84-92. Stewart.Autoassociation.pdf (972.51 KB)
Dethier, J., Nuyujukian P., Eliasmith C., Stewart T. C., Elassaad S. A., Shenoy K., et al. (2011).  A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm. Neural Information Processing Systems (NIPS) 24. A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm.pdf (530.22 KB)
MacNeil, D., & Eliasmith C. (2011).  Fine-tuning and the stability of recurrent neural networks. PLoS ONE. 6(9), 
Bekolay, T., & Eliasmith C. (2011).  A general error-modulated STDP learning rule applied to reinforcement learning in the basal ganglia. Cognitive and Systems Neuroscience. Bekolay.RLCosyne.pdf (2.28 MB)

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