Chris Eliasmith

Professor
Royal Society of Canada, College of New Scholars
Canada Research Chair in Theoretical Neuroscience
Department of Philosophy
Department of Systems Design Engineering
University of Waterloo 
Waterloo, Ontario
N2L 3G1
Canada

email: celiasmith@uwaterloo.ca

[Education] [Books] [Refereed Journal Articles] [Refereed Conference Papers] [Chapters, Encyclopedia, etc.] [Edited Publications] [Awards] [Conferences] [Committees and Professional Affiliations]

Research Positions

Education

Books

Eliasmith, C. (2013) How to build a brain: A neural architecture for biological cognition. Oxford University Press. (Amazon link)

Eliasmith, C. and C. H. Anderson (2003). Neural Engineering: Computation, representation and dynamics in neurobiological systems. MIT Press. (Amazon.com).

Refereed Journal Articles

[Theoretical Neuroscience][Theoretical Psychology][Philosophy]

*Note: Many of these publications and related ones not listed can be found in full at my lab's main website.

Theoretical Neuroscience (online versions of all articles)

T.C. Stewart and C. Eliasmith. Large-scale synthesis of functional spiking neural circuits. Proceedings of the IEEE, 102(5):881-898, May 2014.

Bruce Bobier, Terrence C Stewart, and Chris Eliasmith. A unifying mechanistic model of selective attention in spiking neurons. PLoS computational biology, 10(6):e1003577, June 2014.

Trevor Bekolay, Mark Laubach, and Chris Eliasmith. A spiking neural integrator model of the adaptive control of action by the medial prefrontal cortex. The Journal of Neuroscience, 34(5):1892-1902, 2014.

Trevor Bekolay, James Bergstra, Eric Hunsberger, Travis DeWolf, Terrence C Stewart, Daniel Rasmussen, Xuan Choo, Aaron Russell Voelker, and Chris Eliasmith. Nengo: a python tool for building large-scale functional brain models. Frontiers in Neuroinformatics, 2014.

Steven Leigh, James Danckert, and Chris Eliasmith. Modelling the differential effects of prisms on perception and action in neglect. Experimental Brain Research, November 2014.

Eric Hunsberger, Matthew Scott, and Chris Eliasmith. The competing benefits of noise and heterogeneity in neural coding. Neural Computation, 2014.

Eliasmith, Chris and Oliver Trujillo. (2013) The use and abuse of large-scale brain models. Current Opinion in Neurobiology, 25:1-6, 2013. [online]

Rasmussen, Daniel and Chris Eliasmith.(2013) Modeling brain function: current developments and future prospects. JAMA Neurology, 70(10):1325-1329, 2013. [online]

Eliasmith, C., Stewart T. C., Choo X., Bekolay T., DeWolf T., Tang Y., Rasmussen, D. (2012). A large-scale model of the functioning brain. Science. 338(6111), 1202-1205. (reprint)

Stewart, T. C., Bekolay T., & Eliasmith C. (2012). Learning to select actions with spiking neurons in the basal ganglia. Frontiers in Decision Neuroscience. 6. (online)

DeWolf, T., & Eliasmith C. (2011). The neural optimal control hierarchy for motor control. The Journal of Neural Engineering. 8, 21. (online)

MacNeil, D., Eliasmith, C. (2011). Fine-tuning and the stability of recurrent neural networks. PLoS ONE. 6(9), 22885. (online)

Eliasmith, C., Martens, J. (2011). Normalization for probabilistic inference with neurons. Biological Cybernetics. 104(4), 251-262. (online)

Tripp B.P. and Eliasmith C. (2010) Population models of temporal differentiation. Neural Computation. 22(3):621-59 (.pdf penultimate version). 

Stewart, T.C., Tripp, B., Eliasmith, C. (2009) Python Scripting in the Nengo Simulator. Frontiers in Neuroinformatics. 3:7 (online).

Parisien, C., C. H. Anderson, and C. Eliasmith (2008). Solving the problem of negative synaptic weights in cortical models. Neural Computation. 20: 1473-1494 (pdf penultimate version).

Tripp, B. and C. Eliasmith (2007). Neural populations can induce reliable postsynaptic currents without observable spike rate changes or precise spike timing. Cerebral Cortex. 17:1830-1840 (on-line) (pdf penultimate version).

Singh, R. and C. Eliasmith (2006). Higher-dimensional neurons explain the tuning and dynamics of working memory cells. Journal of Neuroscience. 26: 3667-3678. (pdf version).

Kuo, D. and C. Eliasmith (2005) Integrating behavioral and neural data in a model of zebrafish network interaction. Biological Cybernetics. 93(3): 178-187 (pdf version - penultimate).

Eliasmith, C. (2005). A unified approach to building and controlling spiking attractor networks. Neural Computation. 17(6): 1276-1314 (pdf version - penultimate)

Conklin, J. and C. Eliasmith (2005). An attractor network model of path integration in the rat. Journal of Computational Neuroscience. 18: 183-203 (pdf version)

Theoretical psychology (online versions of all articles)

Daniel Rasmussen and Chris Eliasmith. A spiking neural model applied to the study of human performance and cognitive decline on raven's advanced progressive matrices. Intelligence, 42:53-82, 2014.

Rasmussen, Daniel and Chris Eliasmith. God, the devil, and details: fleshing out the predictive processing framework. Behavioral and Brain Sciences, 36:223-224, 2013. [online]

Eliasmith, C. (2011). The complex systems approach: Rhetoric or revolution? Topics in Cognitive Science. doi: 10.1111/j.1756-8765.2011.01169.x (online)

Stewart, T., T. Bekolay, C. Eliasmith (2011). Neural representations of compositional structures: Representing and manipulating vector spaces with spiking neurons. Connection Science. 22(3), 145-153. (pdf)

Rasmussen, D. and C. Eliasmith (2011). A neural model of rule generation in inductive reasoning. Topics in Cognitive Science (TopICS) 3(1): 140-153. doi: 10.1111/j.1756-8765.2010.01127.x (online)

Stewart, T. C., Y. Tang, and C. Eliasmith (2011) A biologically realistic cleanup memory: Autoassociation in spiking neurons. Cognitive Systems Research, 12, 84-92. (pdf)

Litt, A, C. Eliasmith, and P. Thagard (2008) Neural affective decision theory: Choices, brains, and emotions. Cognitive Systems Research. 9:252-273 (pdf penultimate version).

Eliasmith, C. and P. Thagard (2001). Integrating structure and meaning: A distributed model of analogical mapping. Cognitive Science. (html version)

Philosophy (online versions of all articles)

Eliasmith, C. (2015) On the eve of artificial minds. In Thomas Metzinger and Jennifer Windt, editors, Open MIND. Frankfurt am Main: MIND Group.

Eliasmith, C. and Carter Kolbeck. (2015). Marr's attacks: on reductionism and vagueness. Topics in Cognitive Science, pages 1-13, 2015. doi:10.1111/tops.12133.

Eliasmith, C. (2010). How we ought to understand computation in the brain. Studies in History and Philosophy of Science. 41(3) 131-320 (pdf penultimate version).

Eliasmith, C. (2007). How to build a brain: From function to implementation. Synthese. 153(3): 373-388 (pdf penultimate version).

Litt, A. C. Eliasmith, F. Kroon, S. Weinstein and P. Thagard (2006). Is the brain a quantum computer? Cognitive Science. 30(3): 593-603 (pdf version).

Eliasmith, C. (2005). A new perspective on representational problems. Journal of Cognitive Science. 6: 97-123. (pdf version - penultimate).

Eliasmith, C. (2003). Moving beyond metaphors: Understanding the mind for what it is. Journal of Philosophy. C(10):493-520. Reprinted in Brooks and Akins (eds). Cognition and the Brain. 2005. Cambridge University Press. p.131-159. (pdf version)

Eliasmith, C. (2002). The myth of the Turing machine: The failings of functionalism and related theses. Journal of Experimental and Theoretical Artificial Intelligence. 14: 1-8. (html version)

Eliasmith, C. (2002). Discreteness and relevance: A reply to Roman Poznanski. Minds and Machines. 12: 437-438. (pdf version)

Eliasmith, C. (2001). Attractive and in-discrete: A critique of two putative virtues of the dynamicist theory of mind. Minds and Machines. 11: 417-426. (html version)

Eliasmith, C. (2000) Is the brain analog or digital?: The solution and its consequences for cognitive science. Cognitive Science Quarterly. 1(2): 147-170. (html version).

Eliasmith, C. (1997). Computation and dynamical models of mind. Minds and Machines, 7, 531-541. (html version)

Eliasmith, C. and P. Thagard (1997). Waves, particles, and explanatory coherence. British Journal of the Philosophy of Science, 48, 1-19. (html version)

Eliasmith, C. (1996). The third contender: A critical examination of the dynamicist theory of cognition. Philosophical Psychology, 9, 441-463. Reprinted in P. Thagard (Ed.). (1998). Mind readings: Introductory selections in cognitive science. MIT Press. (html version)

Refereed Conference Papers (online versions of all articles)

[Theoretical Neuroscience] [Theoretical Psychology] [Machine Intelligence][Philosophy]

Theoretical Neuroscience (online versions of all articles)

Bernd J. Kroger, Trevor Bekolay, and Chris Eliasmith. Modeling speech production using the neural engineering framework. In 2014 5th IEEE Conference on Cognitive Infocommunications, 203-208. Nov 2014.

Oliver Trujillo and Chris Eliasmith. A spiking-neuron model of memory encoding and replay in hippocampus. In BMC Neuroscience, 166. Organization for Computational Neurosciences, 2014.

Federico Corradi, Chris Eliasmith, and Giacomo Indiveri. Mapping arbitrary mathematical functions and dynamical systems to neuromorphic vlsi circuits for spike-based neural computation. In IEEE International Symposium on Circuits and Systems (ISCAS). Melbourne, 2014.

Aaron Russell Voelker, Eric Crawford, and Chris Eliasmith. Learning large-scale heteroassociative memories in spiking neurons. In Unconventional Computation & Natural Computation. London, Ontario, 07/2014 2014.

Francesco Galluppi, Christian Denk, Matthias Meiner, Terrence C Stewart, Luis Plana, Chris Eliasmith, Steve Furber, and Jorg Conradt. Event-based neural computing on an autonomous mobile platform. In Proceedings of IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, 2014.

Eric Hunsberger, Matthew Scott, and Chris Eliasmith. Heterogeneity increases information transmission in neuronal populations. COSYNE, 2013. [online]

Daniel Rasmussen and Chris Eliasmith. A neural reinforcement learning model for tasks with unknown time delays. In Cognitive Science Society, 3257-3262. 2013. [online]

Sergio Davies, Terrence C. Stewart, Chris Eliasmith, and Steve Furber. Spike-based learning of transfer functions with the SpiNNaker neuromimetic simulator. International Joint Conference on Neural Networks, 2013. [online]

Galluppi, F., Davies S., Stewart T., Eliasmith C., & Furber S. (2012). Real Time On-Chip Implementation of Dynamical Systems with Spiking Neurons. IJCNN. (online)

Dethier, J., Nuyujukian, P., Eliasmith, C., Stewart, T., Elassaad, S.A., Shenoy, K., Boahen, K.
(2011). A brain-machine interface operating with a real-time spiking neural network control
algorithm. Neural Information Processing Systems (NIPS). [online]

Travis Dewolf & C. Eliasmith (2011). Applying optimal hierarchical controllers to neural models of motor control. International Conference on Applied Mathematics, Modeling and Computational Science (AMMCS).

Trevor Bekolay (2011). A general error-modulated STDP learning rule applied to reinforcement learning in the basal ganglia. CoSyne 2011. Salt Lake City, Utah. [online]

B. Bobier, T. Stewart. & C. Eliasmith. (2011) The Attentional Routing Circuit: Receptive Field Modulation Through Nonlinear Dendritic Interactions. CoSyne 2011. Salt Lake City, Utah. [online]

Travis Dewolf & C. Eliasmith A spiking neuron model of movement and pre-movement activity in M1. CoSyne 2011. Salt Lake City, Utah. [online]

A neuronal mechanism for linking actions to outcomes in the medial prefrontal cortex. Cosyne 2011, Salt Lake City, B. Liu, M. Caetano, N. Narayanan, C. Eliasmith, and M. Laubach.

Stewart, T.C., Choo, X. & C. Eliasmith (2010) Dynamic Behaviour of a Spiking Model of Action Selection in the Basal Ganglia. 10th International Conference on Cognitive Modeling. Aug 2010. [online]

Travis Dewolf & C. Eliasmith (2010) NOCH: A framework for biologically plausible models of neural motor control. Neural Control of Movement 20th Annual Conference. Naples, FL. June 2010. [online]

B. Bobier, T. Stewart. & C. Eliasmith. (2010) Dynamic Routing Model for Visuospatial Attention.  Cosyne. Salt Lake City. [online]

Bryan Tripp & C. Eliasmith (2008) Plasticity and population coding. COSYNE. Salt Lake City, Feb. 2008.

Bryan Tripp & C. Eliasmith (2007) Population coding in the basal ganglia. International Basal Ganglia Society IX, Amsterdam, ND, Sept.

James Martens & C. Eliasmith (2007). A neurologically plausible implementation of statistical inference applied to random dot motion. Computational Neuroscience (CNS*07), Toronto, July.

Bryan Tripp & C. Eliasmith (2007) Supervision of motor cortex by basal ganglia. Computational Neuroscience (CNS*07), Toronto, July.

Abninder Litt, Paul Thagard & C. Eliasmith (2007) A large-scale neurocomputational model of emotional decision making. Computational Neuroscience (CNS*07), Toronto, July.

Bryan Tripp & C. Eliasmith (2007) Temporal coding of continuously-varying inputs. Computational Neuroscience (CNS*07), Toronto, July.

Bryan Tripp & C. Eliasmith (2006) Comparison of neural circuits that estimate temporal derivatives. COSYNE '06, Salt Lake City, Mar.

Parisien, C., C. H. Anderson & Eliasmith, C. (2006). Biologically realistic neural inhibition in arbitrary neural circuits. COSYNE '06, Salt Lake City, Mar. chosen for highlight talk.

R. Singh & C. Eliasmith (2004) A dynamic model of working memory in the PFC during a somatosensory discrimination task. COSYNE 04, Cold Spring Harbor Labratory. Mar.

Kuo, D. and C. Eliasmith (2004). Understanding interactions between networks controlling distinct behaviors: Escape and swimming in larval zebrafish. Neurocomputing. 58-60: 541-547. (pdf version)

C. Eliasmith (2003) A general framework for large-scale simulations applied to working memory. ICCNS'03, Boston, MA. May

Eliasmith, C., M. B. Westover, and C. H. Anderson (2002). A general framework for neurobiological modeling: An application to the vestibular system. Neurocomputing. 46: 1071-1076. (pdf version).

M. B. Westover, C. Eliasmith and C. H. Anderson C. Eliasmith (2002). Linearly decodable functions from neural population codes. Neurocomputing. 45: 691-696. (pdf version)

Eliasmith, C. and C. H. Anderson (2001). Beyond bumps: Spiking networks that store smooth n-dimensional functions. Neurocomputing. 38, 581-586. (pdf version)

C. Eliasmith (2001) A general framework for neurobiological modeling: An application to the vestibular system. Computational Neuroscience (CNS*2001). Monterey, CA. July.

Eliasmith, C. and C. H. Anderson (2000). Rethinking central pattern generators: A general framework. Neurocomputing. 32-33(1-4): 735-740 (pdf version)

C. Eliasmith (2000) Beyond bumps: Spiking networks that store smooth n-dimensional functions. Computational Neuroscience (CNS *2000). Brugge, Belgium. July.

Eliasmith, C. and C. H. Anderson (1999). Developing and applying a toolkit from a general neurocomputational framework. Neurocomputing, 26(1): 1013-1018 (pdf version)

C. Eliasmith (1999) Rethinking central pattern generators: A general framework. Computational Neuroscience (CNS *99). Pittsburgh, PA. July.

Theoretical Psychology (online versions of all articles)

Terrence C. Stewart, Xuan Choo, and Chris Eliasmith. Sentence processing in spiking neurons: a biologically plausible left-corner parser. In 36th Annual Conference of the Cognitive Science Society, 1533-1538. Cognitive Science Society, 2014.

Daniel Rasmussen and Chris Eliasmith. A neural model of hierarchical reinforcement learning. In Paul Bello, Marcello Guarini, Marjorie McShane, and Brian Scassellati, editors, Proceedings of the 36th Annual Conference of the Cognitive Science Society, 1252-1257. Austin, 2014. Cognitive Science Society. URL: https://mindmodeling.org/cogsci2014/papers/221/index.html.

Eric Crawford, Matthew Gingerich, and Chris Eliasmith. Biologically plausible, human-scale knowledge representation. Cognitive Science Society, 412-417. 2013. [online]

Trevor Bekolay, Carter Kolbeck, and Chris Eliasmith. Simultaneous unsupervised and supervised learning of cognitive functions in biologically plausible spiking neural networks. In Cognitive Science Society, 169-174, 2013. [online]

Carter Kolbeck, Trevor Bekolay, and Chris Eliasmith. A biologically plausible spiking neuron model of fear conditioning. In International Conference on Cognitive Modeling, 53-58, 2013. [online]

Eric Hunsberger, Peter Blouw, James Bergstra, and Chris Eliasmith. A neural model of human image categorization. Cognitive Science Society, 633-638, 2013. [online]

Peter Blouw and Chris Eliasmith. A neurally plausible encoding of word order information into a semantic vector space. In Cognitive Science Society, 1905-1910. 2013. [online]

Travis DeWolf and Chris Eliasmith. A neural model of the development of expertise. The 12th International Conference on Cognitive Modelling, 2013. [online]

Xuan Choo and Chris Eliasmith. General instruction following in a large-scale biologically plausible brain model. In Cognitive Science Society, 322-327, 2013. [online]

Terrence C. Stewart and Chris Eliasmith. Parsing sequentially presented commands in a large-scale biologically realistic brain model. In Cognitive Science Society, 3460-3467, 2013. [online]

Aziz Hurzook, Oliver Trujillo, and Chris Eliasmith. Visual motion processing and perceptual decision making. In Cognitive Science Society, 2590-2595, 2013. [online]

Terry Stewart & C. Eliasmith (2011) Neural Cognitive Modelling: A Biologically Constrained Spiking Neuron Model of the Tower of Hanoi Task. (Carlson, L., Hölscher C., & Shipley T., Ed.). 33rd Annual Conference of the Cognitive Science Society. [online]

Stewart, T.C., Choo, X. & C. Eliasmith (2010) Symbolic Reasoning in Spiking Neurons: A Model of the Cortex/Basal Ganglia/Thalamus Loop. 32nd Annual Meeting of the Cognitive Science Society. Aug. 2010. [online]

Choo, X. & C. Eliasmith (2010) A Spiking Neuron Model of Serial-Order Recall. In Richard Cattrambone & Stellan Ohlsson (Eds.), 32nd Annual Conference of the Cognitive Science Society. Portland, OR: Cognitive Science Society. Aug 2010. [online]

Rasmussen, D. & C. Eliasmith (2010). A neural model of rule generation in inductive reasoning. In Richard Cattrambone & Stellan Ohlsson (Eds.), 32nd Annual Conference of the Cognitive Science Society. Portland, OR: Cognitive Science Society. Won Best Paper Award. [online]

Terrence C. Stewart & C. Eliasmith (2008) Building production systems with realistic spiking neurons. Cognitive Science Conference. Washington, DC. August. [online]

Abninder Litt, Paul Thagard & C. Eliasmith (2006) When matter matters: understanding brains to enrich behavioral explanations of judgments and decisions Society for Judgment and Decision Making. Houston, Nov.

Abninder Litt, Paul Thagard & C. Eliasmith (2006) Emotions shape decisions -- but how? A neurocomputational account Computational Cognitive Neuroscience Conference, Houston, Nov.

C. Eliasmith (2005) BioSLIE: Context-sensitive Linguistic Inference in a Biologically Realistic Simulation. Computational Cognitive Neuroscience Meeting. Washington DC : Nov.

C. Eliasmith (2005) Cognition with neurons: A large-scale, biologically realistic model of the Wason task. Cognitive Science Society Conference. Stresa , Italy : July

C. Eliasmith (2004) Learning context sensitive inference in a neurobiological simulation. AAAI Fall Symposium on Compositional Connectionism. Oct.

C. Eliasmith (2004) A neurobiological simulation of deductive reasoning. COSYNE 04, Cold Spring Harbor Labratory. Mar.

Eliasmith, C. (2004). Learning context sensitive logical inference in a neurobiological simulation. in Levy, S. and Gayler, R. (eds.). Compositional Connectionism in Cognitive Science. AAAI Fall Symposium. AAAI Press. p. 17-20. (pdf version).

Machine Intelligence (online versions of all articles)

Brent Komer, James Bergstra, and Chris Eliasmith. Hyperopt-sklearn: automatic hyperparameter configuration for scikit-learn. In ICML 2014 AutoML Workshop, 8. 2014.

Brent Komer, James Bergstra, and Chris Eliasmith. Hyperopt-sklearn: automatic hyperparameter configuration for scikit-learn. In Proceedings of the 13th Python in Science Conference, 33-39. 2014.

James Bergstra, Brent Komer, Chris Eliasmith, and David Warde-Farley. Preliminary evaluation of hyperopt algorithms on hpolib. In ICML 2014 AutoML Workshop, 7. 2014.

Tang, Y., Eliasmith, C. (2010). Deep networks for robust visual recognition. International Conference on Machine Learning. (pdf)

Carrilo et al. (2010) Concept Based Representations for Ranking in Geographic Information Retrieval. IceTAL 2010 7th International Conference on Natural Language Processing.

Carrillo, M.,  E. Villatoro-Tello, A. Lopez-Lopez, C. Eliasmith, M. Montes-y-Gomez and L. Villasenor-Pineda (2009) “Concept representations in Geographic Information Retrieval as Re-ranking Strategies” in 18th ACM Conference on Information and Knowledge Management. (pdf version)

Carrillo, M., C. Eliasmith , and A. Lopez-Lopez (2009) “Combining Text Vector Representations for Information Retrieval” in V. Matousek, P. Mautner (eds) Text, Speech and Dialogue. Proceedings of the 12th International Conference TSD 2009. (pdf version)

Carrillo, M., Villatoro-Tello, E., Lopez-Lopez, A., Eliasmith, C., Montes-y-Gomez, M., Villasenor-Pineda, L. (2009). Representing Context Information for Document Retrieval. In T. Andreasen et al. (Ed.), Flexible Query Answering Systems, FQAS 2009. 239-250. (pdf version)

Fishbein, J and C. Eliasmith (2008) Methods for augmenting semantic models with structural information for text classification. Advances in Information Retrieval. Springer. pp. 575-579. DOI 10.1007/978-3-540-78646-7_58 (online version)

Fishbein, J and C. Eliasmith (2008) Integrating structure and meaning: A new method for encoding structure for text classification. Advances in Information Retrieval. Springer. pp. 514-521.(online version)

Jon Fishbein & C. Eliasmith (2007) A new method for encoding structure for text classification. MMKD 2007, Waterloo, Oct. won best poster award.

Philosophy (online versions of all articles)

C. Eliasmith (2000) The myth of the Turing machine: The failings of functionalism and related theses. American Philosophical Association, Central Division. Chicago, IL. April.

C. Eliasmith (1998) Attractive and in-discrete: A critique of two putative virtues of the dynamicist theory of mind. Society for Philosophy and Psychology. June, 1998. Poster. Southern Society for Philosophy and Psychology. April.

C. Eliasmith (1997) Structure without symbols: Providing a distributed account of high-level cognition. Southern Society for Philosophy and Psychology. March. (html version)

Chapters, Reviews, Encyclopedia, etc. (chronological) (online versions of all articles)

Eliasmith, C. (2015). Building a behaving brain. In Gary Marcus and Jeremy Freeman, editors, The Future of the Brain: Essays by the World's Leading Neuroscientists. Princeton University Press, 2015.

Stewart, T. and C. Eliasmith. (2011) Compositionality and biologically plausible models. In W. Hinzen, E. Machery, and M. Werning (eds.) Oxford Handbook of Compositionality. Oxford University Press. (pdf penultimate version)

Eliasmith, C. (2009). Neurocomputational models: Theory and applications. In J. Bickle (Eds.) Oxford Handbook of Philosophy of Neuroscience. Oxford University Press. pp. 346-369 (pdf penultimate version).

Eliasmith, C. (2009). Dynamics, control, and cognition. In P. Robbins and M. Aydede (Eds.) Cambridge Handbook of Situated Cognition. Cambridge University Press. pp. 134-154 (pdf version)

Stewart, T. C., Tripp, B., & Eliasmith, C. (2008). Supplementing Neural Modelling with ACT-R. Proceedings of the 15th Annual ACT-R Workshop, Pittsburgh, USA.

Eliasmith, C. (2008) Review of The great brain debate: Nature or nurture? by John E. Dowling. The Quarterly Review of Biology. 83(2):210-211 (pdf penultimate version)

Stewart, T. C. & Eliasmith, C. (2008). Implementing the ACT-R Production System in Spiking Neurons. Proceedings of the 15th Annual ACT-R Workshop, Pittsburgh, USA.

Chris Eliasmith (2007) Attractor network. Scholarpedia 2(10):1380.

Eliasmith, C. (2007). Computational neuroscience. In P. Thagard (ed.), Philosophy of Psychology and Cognitive Science. Handbook of Philosophy of Science (Vol. 4). Amsterdam: Elsevier. (pdf version)

Eliasmith, C. (2006). Neurosemantics and categories. In C. Lefebvre and H. Cohen (eds.). Handbook of Categorization in Cognitive Science. Amsterdam: Elsevier. (pdf version)

Eliasmith, C. (2005). Cognition with neurons: A large-scale, biologically realistic model of the Wason task. In G. Bara, L. Barsalou, and M. Bucciarelli (Eds)., Proceedings of the 27 th Annual Meeting of the Cognitive Science Society. Stresa , Italy (pdf version).

Eliasmith, C. (2005). Large-scale neural modeling. In J. Wooley and H. Lin (eds.). Catalyzing Inquiry at the Interface of Computing and Biology. Washington: The National Academies Press. 173-178.

Eliasmith, C. (2003). Neural engineering: Unraveling the comlexities of neural systems. IEEE Canadian Review (43): 13-15. (pdf version).

Eliasmith, C. and W. Bechtel (2003). Symbolic vs. subsymbolic. In Lynn Nadel (Ed.), Encyclopedia of Cognitve Science. MacMillian.

Clark, A. and C. Eliasmith (2002). Philosophical issues in brain theory and connectionism. In M. Arbib (Ed.), Handbook of brain theory and neural networks (2nd ed.). Cambridge, MA: MIT Press. pp. 886-888. (html version)

Thagard, P., C. Eliasmith, P. Rusnock, and C. Shelley (2002). Epistemic coherence. In R. Elio (Ed.), Common sense, reasoning, and rationality. Vancouver Studies in Cognitive Science (Vol. 11). Oxford University Press. pp. 104-131. (html version)

Eliasmith, C. (1999). Review of C. Dilworth's The metaphysics of science: An account of modern science in terms of principles, laws, and theories. Dialogue, 37, 656-8. (html version )

Eliasmith, C. (1998). Dynamical models and van Gelder's dynamicism: Two different things. Commentary on van Gelder, T. (1998). The dynamical hypothesis in cognitive science. Behavioral and Brain Sciences, 21, 616-665. (html version)

Eliasmith, C. (1998). Review of William Lyons' Modern philosophy of mind. Philosophical Psychology, 11, 3.(html version)

Edited Publications

Eliasmith, C. (Ed.) (1998). Dictionary of philosophy of mind. Department of Philosophy, University of Waterloo. http://philosophy.uwaterloo.ca/MindDict/.

Awards/Grants

Selected Talks, Tutorials, Workshops, etc.

How to build a brain. McMaster University Neuroscience Colloqium, 2013. Invited talk.

How to build a brain. University of Michigan Psychology Colloqium, 2013. Invited talk.

From single neurons to cognition. Janelia Farms Colloqium, 2013. Invited talk.

Computation for a brain. Google Waterloo, 2013. Invited talk.

Building a brain: From single neurons to cognition. AAAI Symposium on Integrated Cognition, 2013. Keynote.

Neural engineering. Neural Engineering Transformative Technologies Summer School, 2013. Keynote talk and tutorial.

How to build a brain. Society for Computers in Psychology, 2013. Keynote talk.

Large-scale neural modeling. Stanford University MBC Colloqium, 2013. Invited talk.

How to build a brain. International Conference on Cognitive Modeling, 2013. Keynote talk.

From single cells to cognition. CIFAR Summer School, Toronto, 2013. Invited tutorial and talk.

How to build a brain. TEDxWaterloo, 2013. Invited talk. [youtube]

Integrative models of cognition. Cognitive Science Society Symposium, Berlin, 2013. Invited talk.

How to build a brain. University of Indiana Cognitive Science Seminar, 2013. Invited talk.

Can we build intelligent machines? Research Matters, McMaster University, 2013. Invited talk. [youtube]

Marr's Attacks: A gentle reminder. Marr Symposium on Levels of Description. Peebles and Cooper (organizers). Cognitive Science Society, 2012. Invited.

Integrating perception, action, and cognition in neuromorphic hardware and software. Led 3-week session. Telluride Neuromorphic Cognition Workshop. 2012. with Kwabena Boahen, Bryan Tripp, Terry Stewart (submitted proposal).

Function and form: Both matter for neural modelling. ASIC, 2012. Sardinia. Submitted talk.

Constructing a large-scale, nonlinear dynamical brain. Workshop on Nonlinear Dynamics in Complex Neural Architectures. Lyons, 2012. Invited talk.

Methods for large-scale functional modeling in neural hardware. ETHZ Zurich. 2012. Invited talk.

How to build a brain: From single cells to cognition. Coast to Coast Seminar Series. Waterloo (broadcast through Compute Canada), 2012 (slides). Invited talk.

From single cells to cognition in hardware and software. Led 3-week session. Telluride Neuromorphic Cognition Workshop. 2011. with Kwabena Boahen, Terry Stewart (submitted proposal).

How to build a brain: From single cells to cognitive systems. Rensselaer Polytechnic Institute. New York. 2011 Invited talk.

Neural-symbolic integration. 4.5h of lecture. Spring School on Interdisciplinary Cognitive Science. Meunster, Germany. 2011. Invited talks.

Large-scale neuro-cognitive modelling. One-day workshop. Cognitive Science. Boston. 2011.with Terry Stewart (submitted proposal).

Making brain dynamics useful: From single cells to cognition. Cosyne 2011 Workshops. Invited talk.

Neural circuits for persistent activity in medial prefrontal cortex. Society for Neuroscience. (poster 200.18). Nov 2010. Laubach, M., Caetano, M. S., Liu, B., Smith, N. J., Narayanan, N. S., & Eliasmith, C.

Rapid large-scale model construction: A application to working memory. John B. Pierce Lab, Yale University. Nov 2010, Invited talk.

A new learning rule for neural integrators. Cosyne Workshop on Persistent Activity. Feb, 2010. Invited talk.

Large-scale cognitive modelling. Neurocognitive Networks Workshop. Florida Atlantic. Feb. 2010. Invited talk.

Nengo: A new large-scale neural simulator. NIPS Demo, Dec. 2009.

MCMC with spiking neurons with Lloyd Elliott (first). NIPS 2009 Workshop on Bounded-rational analyses of human cognition: Bayesian models, approximate inference, and the brain.

How to build a brain. University of Montreal. Sept 2009. Invited talk.

Lage-scale cognitive modelling with spiking neurons. UQAM. Sept 2009. Invited talk.

Cognitive modelling with the NEF. Full day workshop ICCM, Aug. 2009.

Spiking attractor networks in the NEF. Columbia University. CTN Jan. 2009. Invited talk.

How to build a brain: A suggestion for how to unify the brain sciences. Perimeter Institute, Waterloo. October, 2008. invited talk.

Beyond representation: A general, unified method for characterizing neural computation over space and time. Bernstein Centre for Computational Neuroscience, Munich. May, 2008. invited talk.

How we ought to understand computation in the brain. Computation in Cognitive Science Workshop. July, 2008. Kings College, Cambridge. invited talk.

How to build a brain. Cognitive Science Seminar Series, York University, Nov, 2007. invited talk.

Modelling the mind: Unifying the new brain sciences. Annual Arts Public Lecture, University of Waterloo, Nov. 2007. invited talk.

Minds and brains through mathematics:
Theoretical neuroscience and mental representation. British Society for Philosophy of Science. Bristol, UK, July 2007, invited keynote.

Large-scale models of neural computation, Unconventional Computation: Quo Vadis? Santa Fe Institute, March 2007, invited talk.

A biologically realistic model of statistical inference applied to random dot motion. COSYNE, Salt Lake City, February, 2007, with James Martens (1st), submitted poster.

Challenges for biological bayes:
Solving normalization. COSYNE Workshop on Statistical Inference in the Brain, February, 2007, with James Martens (2nd), contributed talk.

Situated cognition: What's worth keeping? Frankfurt Institute for Advanced Studies (FIAS): Mind Group Symposium. Frankfurt, Germany, Oct. 2006. Invited keynote.

How to build a brain. IEEE Engineering in Medicine and Biology Society (EMBS), Waterloo, Oct. 2006. Invited demo.

Large-scale models of neural computation. Mind & Brain V: Physics and the Brain, Dubrovnik, Croatia, Oct. 2006. Invited talk.

Basics of computational modeling of brain function. Cognitive Systems:
Bridging Cellular to Social. Sandia National Labs, Santa Fe, June, 2006. Invited tutorial.

Classifying and evaluating theories and models
in theoretical neuroscience. Models and Prediction
in Science, Science Studies, and Public Policy, UCSD, San Diego, May, 2006. Invited talk.

PCs, primates and people: Differences in kind or degree? UTism Conference on Cognitive Science. Toronto, Mar. 2006. Invited talk.

The biological plausibility and cognitive relevance of predictive coding. Predictive Coding Workshop. Aarhus University, Denmark, Feb. 2006. Invited talk.

A generalization of central pattern generators. Program No. 753.17. Washington, DC: Society for Neuroscience, 2005 with Bryan Tripp (1st). Contributed poster.

A computational model of the effects of prism adaptation in spatial neglect Program No. 287.15. Washington, DC: Society for Neuroscience, 2005 with Michael Lerman (1st) and James Danckert (2nd). Contributed poster.

From single cells to cognition: General methods and specific models. RIKEN Brain Sciences Institute. Wako City , Japan : Aug. 2005. Invited talk.

Dynamics in neural systems. McDonnell Project in Philosophy and Neuroscience. Caltech: June 2005. Invited talk

Neural engineering: Large-scale neurobiological simulations with applications. Research Colloquium in the School of Informatics, University of Edinburgh. Jun. 2004. Invited talk.

Neurosemantics and misrepresentation. Philosophy-Neuroscience-Psychology Colloquium. Washington University in St. Louis. Oct. 2003. Invited talk.

Beyond a metaphor: Understanding the mind for what it is. Philosophy Department Colloquium. Washington University in St. Louis. Oct. 2003. Invited talk.

Inference and prediction in neocortical circuits. American Institute of Mathematics, Palo Alto, CA. Sept. 2003. Invited workshop participant.

A neurosemantics for categories. Cognitive Science Summer School, University of Quebec at Montreal, Montreal, PQ. July, 2003. Invited talk.

Neurosemantics and misrepresentation. McDonnell Project in Philosophy and the Neurosciences, Heron Island, Australia. July, 2003. Invited talk.

A general framework for constructing large-scale, biologically plausible simulations. CNS *2003, Alicante, Spain. Jun. 2003. Submitted poster and 1/2 day workshop.

Neural engineering:
How to build large-scale, biologically plausible simulations of neural systems. Mathematical Biosciences Institute, Ohio State University. Feb. 2003. Invited talk.

Beyond a metaphor: Understanding the mind for what it is. Philosophy Department Colloquium. University of Western Ontario. Oct. 2002. Invited talk.

Beyond metaphors: Computation, representation and dynamics in cognitive systems. Neuroscience and Philosophy. Carleton University. Oct. 2002. Invited talk.

A general framework for understanding neurobiological systems: An application to working memory. Center for Neuroscience, UC Davis. Apr. 2002. Invited talk.

Beyond a metaphor: Understanding the mind for what it is. Cognitive Science in the New Millennium Foundations, Directions, Applications, and Problems. CalState University, Long Beach. Apr. 2002. Invited talk.

Integrating representations, transformations, and dynamics: A general framework for understanding neurobiological systems. Caltech and Pitzer College, CA. Nov. 2001. Invited talk.

How brains represent: Sensing motion. University of Waterloo Cognitive Science Forum. June, 2001. Invited talk.

A general framework for simulating neurobiological networks applied to the vestibular system. Washington University Computational Neuroscience Series. April, 2001. Invited talk.

A change in perspective for philosophers, neuroscientists, and psychologists: A new take on representational problems. Washington University, Philosophy-Neuroscience-Psychology program work-in-progress colloquia. April, 1999. Volunteered talk.

Attractors, representation, and a neurocomputational framework. Computational Neuroscience (CNS *98). Santa Barbara, CA. July, 1998. Poster. Washington University Neuroscience Retreat. Potosi, MO. October, 1998. Invited talk.

The neural integrator: An application of the PDF modeling framework. Washington University Neuroscience Retreat. Potosi, MO. October, 1997. Poster.

Integrating structure and meaning: A distributed model of analogical mapping. Washington University, Philosophy-Neuroscience-Psychology program work-in-progress colloquia. October, 1996. Volunteered talk.

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