Computation and Dynamical Models of Mind. - Chris Eliasmith (Minds Mach., 1997)

Developing and applying a toolkit from a general neurocomputational framework. - Chris Eliasmith; Charles H. Anderson (Neurocomputing, 1999)

Rethinking central pattern generators: A general approach. - Chris Eliasmith; Charles H. Anderson (Neurocomputing, 2000)

Integrating structure and meaning: a distributed model of analogical mapping. - Chris Eliasmith; Paul Thagard (Cogn. Sci., 2001)

Beyond bumps: Spiking networks that store sets of functions. - Chris Eliasmith; Charles H. Anderson (Neurocomputing, 2001)

Attractive and In-discrete. - Chris Eliasmith (Minds Mach., 2001)

A general framework for neurobiological modeling: an application to the vestibular system. - Chris Eliasmith; M. Brandon Westover; Charles H. Anderson (Neurocomputing, 2002)

Linearly decodable functions from neural population codes. - M. Brandon Westover; Chris Eliasmith; Charles H. Anderson (Neurocomputing, 2002)

The myth of the Turing machine: the failings of functionalism and related theses. - Chris Eliasmith (J. Exp. Theor. Artif. Intell., 2002)

Discreteness and Relevance: A Reply to Roman Poznanski. - Chris Eliasmith (Minds Mach., 2002)

Learning Context Sensitive Logical Inference in a Neurobiolobical Simulation. - Chris Eliasmith (AAAI Technical Report (3), 2004)

Understanding interactions between networks controlling distinct behaviours: Escape and swimming in larval zebrafish. - P. Dwight Kuo; Chris Eliasmith (Neurocomputing, 2004)

Reports on the 2004 AAAI Fall Symposia. - Nicholas L. Cassimatis; Sean Luke; Simon D. Levy; Ross W. Gayler; Pentti Kanerva; Chris Eliasmith; Timothy W. Bickmore; Alan C. Schultz; Randall Davis; James A. Landay; Robert C. Miller; Eric Saund; et al. (AI Mag., 2005)

Integrating behavioral and neural data in a model of zebrafish network interaction. - P. Dwight Kuo; Chris Eliasmith (Biol. Cybern., 2005)

A Controlled Attractor Network Model of Path Integration in the Rat. - John Conklin; Chris Eliasmith (J. Comput. Neurosci., 2005)

A Unified Approach to Building and Controlling Spiking Attractor Networks. - Chris Eliasmith (Neural Comput., 2005)

Is the Brain a Quantum Computer? - Abninder Litt; Chris Eliasmith; Frederick W. Kroon; Steven Weinstein; Paul Thagard (Cogn. Sci., 2006)

Attractor network. - Chris Eliasmith (Scholarpedia, 2007)

How to build a brain: from function to implementation. - Chris Eliasmith (Synth., 2007)

Integrating Structure and Meaning: A New Method for Encoding Structure for Text Classification. - Jonathan M. Fishbein; Chris Eliasmith (ECIR, 2008)

Methods for Augmenting Semantic Models with Structural Information for Text Classification. - Jonathan M. Fishbein; Chris Eliasmith (ECIR, 2008)

Neural affective decision theory: Choices, brains, and emotions. - Abninder Litt; Chris Eliasmith; Paul Thagard (Cogn. Syst. Res., 2008)

Solving the Problem of Negative Synaptic Weights in Cortical Models. - Christopher Parisien; Charles H. Anderson; Chris Eliasmith (Neural Comput., 2008)

Representing Context Information for Document Retrieval. - Maya Carrillo; Esaú Villatoro-Tello; Aurelio López-López; Chris Eliasmith; Manuel Montes-y-Gómez; Luis Villaseñor Pineda (FQAS, 2009)

Combining Text Vector Representations for Information Retrieval. - Maya Carrillo; Chris Eliasmith; Aurelio López-López (TSD, 2009)

Python scripting in the Nengo simulator. - Terrence C. Stewart; Bryan P. Tripp; Chris Eliasmith (Frontiers Neuroinformatics, 2009)

Neural Symbolic Decision Making: A Scalable and Realistic Foundation for Cognitive Architectures. - Terrence C. Stewart; Chris Eliasmith (BICA, 2010)

Deep networks for robust visual recognition. - Yichuan Tang; Chris Eliasmith (ICML, 2010)

Concept Based Representations for Ranking in Geographic Information Retrieval. - Maya Carrillo; Esaú Villatoro-Tello; Aurelio López-López; Chris Eliasmith; Luis Villaseñor Pineda; Manuel Montes-y-Gómez (IceTAL, 2010)

Population Models of Temporal Differentiation. - Bryan P. Tripp; Chris Eliasmith (Neural Comput., 2010)

Neural Planning and Reasoning Using the Synaptic Connections of the Basal Ganglia and Thalamus. - Terrence C. Stewart; Chris Eliasmith (BICA, 2011)

Nengo and the Neural Engineering Framework: Connecting Cognitive Theory to Neuroscience. - Chris Eliasmith; Terrence C. Stewart (CogSci, 2011)

Neural Cognitive Modelling: A Biologically Constrained Spiking Neuron Model of the Tower of Hanoi Task. - Terrence C. Stewart; Chris Eliasmith (CogSci, 2011)

A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm. - Julie Dethier; Paul Nuyujukian; Chris Eliasmith; Terrence C. Stewart; Shauki A. Elasaad; Krishna V. Shenoy; Kwabena Boahen 001 (NIPS, 2011)

Normalization for probabilistic inference with neurons. - Chris Eliasmith; James Martens (Biol. Cybern., 2011)

A biologically realistic cleanup memory: Autoassociation in spiking neurons. - Terrence C. Stewart; Yichuan Tang; Chris Eliasmith (Cogn. Syst. Res., 2011)

Neural representations of compositional structures: representing and manipulating vector spaces with spiking neurons. - Terrence C. Stewart; Trevor Bekolay; Chris Eliasmith (Connect. Sci., 2011)

A Neural Model of Rule Generation in Inductive Reasoning. - Daniel Rasmussen; Chris Eliasmith (Top. Cogn. Sci., 2011)

Live Demo: Spiking ratSLAM: Rat hippocampus cells in spiking neural hardware. - Francesco Galluppi; Jörg Conradt; Terrence C. Stewart; Chris Eliasmith; Timothy K. Horiuchi; Jonathan Tapson; Bryan P. Tripp; Steve B. Furber; Ralph Etienne-Cummings (BioCAS, 2012)

Thirty years of Marr's Vision: Levels of Analysis in Cognitive Science. - Chris Eliasmith; Thomas L. Griffiths 001; Valerie Gray Hardcastle; Bradley C. Love; William Bechtel; Richard P. Cooper 002; David Peebles (CogSci, 2012)

Nengo and the Neural Engineering Framework: From Spikes to Cognition. - Chris Eliasmith; Terrence C. Stewart (CogSci, 2012)

Spaun: A Perception-Cognition-Action Model Using Spiking Neurons. - Terrence C. Stewart; Feng-Xuan Choo; Chris Eliasmith (CogSci, 2012)

Silicon Neurons That Compute. - Swadesh Choudhary; Steven Sloan; Sam Fok; Alexander Neckar; Eric Trautmann; Peiran Gao; Terry C. Stewart; Chris Eliasmith; Kwabena Boahen 001 (ICANN (1), 2012)

Real time on-chip implementation of dynamical systems with spiking neurons. - Francesco Galluppi; Sergio Davies; Steve B. Furber; Terry C. Stewart; Chris Eliasmith (IJCNN, 2012)

The Complex Systems Approach: Rhetoric or Revolution. - Chris Eliasmith (Top. Cogn. Sci., 2012)

Simultaneous unsupervised and supervised learning of cognitive functions in biologically plausible spiking neural networks. - Trevor Bekolay; Carter Kolbeck; Chris Eliasmith (CogSci, 2013)

A Neurally Plausible Encoding of Word Order Information into a Semantic Vector Space. - Peter Blouw; Chris Eliasmith (CogSci, 2013)

General Instruction Following in a Large-Scale Biologically Plausible Brain Model. - Feng-Xuan Choo; Chris Eliasmith (CogSci, 2013)

A General Purpose Architecture for Building Spiking Neuron Models of Biological Cognition. - Chris Eliasmith; Terrence C. Stewart (CogSci, 2013)

A Neural Model of Human Image Categorization. - Eric Hunsberger; Peter Blouw; James Bergstra; Chris Eliasmith (CogSci, 2013)

Visual motion processing and perceptual decision making. - Aziz Hurzook; Oliver Trujillo; Chris Eliasmith (CogSci, 2013)

A neural reinforcement learning model for tasks with unknown time delays. - Daniel Rasmussen; Chris Eliasmith (CogSci, 2013)

Parsing Sequentially Presented Commands in a Large-Scale Biologically Realistic Brain Model. - Terrence C. Stewart; Chris Eliasmith (CogSci, 2013)

Spike-based learning of transfer functions with the SpiNNaker neuromimetic simulator. - Sergio Davies; Terry C. Stewart; Chris Eliasmith; Steve B. Furber (IJCNN, 2013)

Nengo: a Python tool for building large-scale functional brain models. - Trevor Bekolay; James Bergstra; Eric Hunsberger; Travis DeWolf; Terrence C. Stewart; Daniel Rasmussen; Feng-Xuan Choo; Aaron Voelker; Chris Eliasmith (Frontiers Neuroinformatics, 2013)

Modeling speech production using the Neural Engineering Framework. - Bernd J. Kröger; Trevor Bekolay; Chris Eliasmith (CogInfoCom, 2014)

Neural Field Coding of Short Term Memory. - Dimitris A. Pinotsis; Chris Eliasmith (CogSci, 2014)

A neural model of hierarchical reinforcement learning. - Daniel Rasmussen; Chris Eliasmith (CogSci, 2014)

Sentence processing in spiking neurons: A biologically plausible left-corner parser. - Terrence C. Stewart; Feng-Xuan Choo; Chris Eliasmith (CogSci, 2014)

Event-based neural computing on an autonomous mobile platform. - Francesco Galluppi; Christian Denk; Matthias C. Meiner; Terrence C. Stewart; Luis A. Plana; Chris Eliasmith; Stephen B. Furber; Jörg Conradt (ICRA, 2014)

Mapping arbitrary mathematical functions and dynamical systems to neuromorphic VLSI circuits for spike-based neural computation. - Federico Corradi; Chris Eliasmith; Giacomo Indiveri (ISCAS, 2014)

Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn. - Brent Komer; James Bergstra; Chris Eliasmith (SciPy, 2014)

The Competing Benefits of Noise and Heterogeneity in Neural Coding. - Eric Hunsberger; Matthew Scott 001; Chris Eliasmith (Neural Comput., 2014)

Large-Scale Synthesis of Functional Spiking Neural Circuits. - Terrence C. Stewart; Chris Eliasmith (Proc. IEEE, 2014)

A Unifying Mechanistic Model of Selective Attention in Spiking Neurons. - Bruce Bobier; Terrence C. Stewart; Chris Eliasmith (PLoS Comput. Biol., 2014)

Constraint-Based Parsing with Distributed Representations. - Peter Blouw; Chris Eliasmith (CogSci, 2015)

A Spiking Neural Model of the n-Back Task. - Jan Gosmann; Chris Eliasmith (CogSci, 2015)

Spiking Deep Networks with LIF Neurons. - Eric Hunsberger; Chris Eliasmith (CoRR, 2015)

Marr's Attacks: On Reductionism and Vagueness. - Chris Eliasmith; Carter Kolbeck (Top. Cogn. Sci., 2015)

A scaleable spiking neural model of action planning. - Peter Blouw; Chris Eliasmith; Bryan P. Tripp (CogSci, 2016)

Towards a Cognitively Realistic Representation of Word Associations. - Ivana Kajic; Jan Gosmann; Terrence C. Stewart; Thomas Wennekers; Chris Eliasmith (CogSci, 2016)

A Neural Model of Context Dependent Decision Making in the Prefrontal Cortex. - Sugandha Sharma; Brent Komer; Terrence C. Stewart; Chris Eliasmith (CogSci, 2016)

Real-Time FPGA Simulation of Surrogate Models of Large Spiking Networks. - Murphy Berzish; Chris Eliasmith; Bryan P. Tripp (ICANN (1), 2016)

Efficient SpiNNaker simulation of a heteroassociative memory using the Neural Engineering Framework. - James C. Knight; Aaron R. Voelker; Andrew Mundy; Chris Eliasmith; Steve B. Furber (IJCNN, 2016)

Concepts as Semantic Pointers: A Framework and Computational Model. - Peter Blouw; Eugene Solodkin; Paul Thagard; Chris Eliasmith (Cogn. Sci., 2016)

Biologically Plausible, Human-Scale Knowledge Representation. - Eric Crawford; Matthew Gingerich; Chris Eliasmith (Cogn. Sci., 2016)

BioSpaun: A large-scale behaving brain model with complex neurons. - Chris Eliasmith; Jan Gosmann; Xuan Choo (CoRR, 2016)

Training Spiking Deep Networks for Neuromorphic Hardware. - Eric Hunsberger; Chris Eliasmith (CoRR, 2016)

Modeling Interactions between Speech Production and Perception: Speech Error Detection at Semantic and Phonological Levels and the Inner Speech Loop. - Bernd J. Kröger; Eric Crawford; Trevor Bekolay; Chris Eliasmith (Frontiers Comput. Neurosci., 2016)

Function approximation in inhibitory networks. - Bryan P. Tripp; Chris Eliasmith (Neural Networks, 2016)

Human-Inspired Neurorobotic System for Classifying Surface Textures by Touch. - Ken E. Friedl; Aaron R. Voelker; Angelika Peer; Chris Eliasmith (IEEE Robotics Autom. Lett., 2016)

Continuous and Parallel: Challenges for a Standard Model of the Mind. - Terrence C. Stewart; Chris Eliasmith (AAAI Fall Symposia, 2017)

Inferential Role Semantics for Natural Language. - Peter Blouw; Chris Eliasmith (CogSci, 2017)

A Spiking Independent Accumulator Model for Winner-Take-All Computation. - Jan Gosmann; Aaron Voelker; Chris Eliasmith (CogSci, 2017)

A Biologically Constrained Model of Semantic Memory Search. - Ivana Kajic; Jan Gosmann; Brent Komer; Ryan W. Orr; Terrence C. Stewart; Chris Eliasmith (CogSci, 2017)

A Spiking Neural Bayesian Model of Life Span Inference. - Sugandha Sharma; Aaron Voelker; Chris Eliasmith (CogSci, 2017)

Extending the neural engineering framework for nonideal silicon synapses. - Aaron R. Voelker; Ben Varkey Benjamin; Terrence C. Stewart; Kwabena Boahen 001; Chris Eliasmith (ISCAS, 2017)

An adaptive spiking neural controller for flapping insect-scale robots. - Taylor S. Clawson; Terrence C. Stewart; Chris Eliasmith; Silvia Ferrari (SSCI, 2017)

Methods for applying the Neural Engineering Framework to neuromorphic hardware. - Aaron R. Voelker; Chris Eliasmith (CoRR, 2017)

Point Neurons with Conductance-Based Synapses in the Neural Engineering Framework. - Andreas Stöckel; Aaron R. Voelker; Chris Eliasmith (CoRR, 2017)

Automatic Optimization of the Computation Graph in the Nengo Neural Network Simulator. - Jan Gosmann; Chris Eliasmith (Frontiers Neuroinformatics, 2017)

Improving With Practice: A Neural Model of Mathematical Development. - Sean Aubin; Aaron R. Voelker; Chris Eliasmith (Top. Cogn. Sci., 2017)

The Effects of Guanfacine and Phenylephrine on a Spiking Neuron Model of Working Memory. - Peter Duggins; Terrence C. Stewart; Xuan Choo; Chris Eliasmith (Top. Cogn. Sci., 2017)

Supervised Learning of Action Selection in Cognitive Spiking Neuron Models. - Terrence C. Stewart; Sverrir Thorgeirsson; Chris Eliasmith (CogSci, 2018)

Implementing NEF Neural Networks on Embedded FPGAs. - Benjamin Morcos; Terrence C. Stewart; Chris Eliasmith; Nachiket Kapre (FPT, 2018)

Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware. - Peter Blouw; Xuan Choo; Eric Hunsberger; Chris Eliasmith (CoRR, 2018)

Inhibiting Basal Ganglia Regions Reduces Syllable Sequencing Errors in Parkinson's Disease: A Computer Simulation Study. - Valentin Senft; Terrence C. Stewart; Trevor Bekolay; Chris Eliasmith; Bernd J. Kröger (Frontiers Comput. Neurosci., 2018)

Improving Spiking Dynamical Networks: Accurate Delays, Higher-Order Synapses, and Time Cells. - Aaron R. Voelker; Chris Eliasmith (Neural Comput., 2018)

Hyperopt-Sklearn. - Brent Komer; James Bergstra; Chris Eliasmith (Automated Machine Learning, 2019)

A neural representation of continuous space using fractional binding. - Brent Komer; Terrence C. Stewart; Aaron Voelker; Chris Eliasmith (CogSci, 2019)

Representing spatial relations with fractional binding. - Thomas Lu; Aaron Voelker; Brent Komer; Chris Eliasmith (CogSci, 2019)

A Geometric Interpretation of Feedback Alignment. - Andreas Stöckel; Terrence C. Stewart; Chris Eliasmith (CogSci, 2019)

A Mixture-of-Experts Model for Vehicle Prediction Using an Online Learning Approach. - Florian Mirus; Terrence C. Stewart; Chris Eliasmith; Jörg Conradt (ICANN (3), 2019)

Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks. - Aaron Voelker; Ivana Kajic; Chris Eliasmith (NeurIPS, 2019)

Passive nonlinear dendritic interactions as a general computational resource in functional spiking neural networks. - Andreas Stöckel; Chris Eliasmith (CoRR, 2019)

Vector-Derived Transformation Binding: An Improved Binding Operation for Deep Symbol-Like Processing in Neural Networks. - Jan Gosmann; Chris Eliasmith (Neural Comput., 2019)

Braindrop: A Mixed-Signal Neuromorphic Architecture With a Dynamical Systems-Based Programming Model. - Alexander Neckar; Sam Fok; Ben Varkey Benjamin; Terrence C. Stewart; Nick N. Oza; Aaron R. Voelker; Chris Eliasmith; Rajit Manohar; Kwabena Boahen 001 (Proc. IEEE, 2019)

A spiking neural architecture for conscious chaining of mental operations. - Hugo Chateau-Laurent; Chris Eliasmith; Serge Thill (CogSci, 2020)

Accurate representation for spatial cognition using grid cells. - Nicole Dumont; Chris Eliasmith (CogSci, 2020)

Efficient navigation using a scalable, biologically inspired spatial representation. - Brent Komer; Chris Eliasmith (CogSci, 2020)

A Biologically Plausible Spiking Neural Model of Eyeblink Conditioning in the Cerebellum. - Andreas Stöckel; Terrence C. Stewart; Chris Eliasmith (CogSci, 2020)

Event-Driven Signal Processing with Neuromorphic Computing Systems. - Peter Blouw; Chris Eliasmith (ICASSP, 2020)

A Spike in Performance: Training Hybrid-Spiking Neural Networks with Quantized Activation Functions. - Aaron R. Voelker; Daniel Rasmussen; Chris Eliasmith (CoRR, 2020)

Hardware Aware Training for Efficient Keyword Spotting on General Purpose and Specialized Hardware. - Peter Blouw; Gurshaant Malik; Benjamin Morcos; Aaron R. Voelker; Chris Eliasmith (CoRR, 2020)

Low-Power Low-Latency Keyword Spotting and Adaptive Control with a SpiNNaker 2 Prototype and Comparison with Loihi. - Yexin Yan; Terrence C. Stewart; Xuan Choo; Bernhard Vogginger; Johannes Partzsch; Sebastian Höppner; Florian Kelber; Chris Eliasmith; Steve B. Furber; Christian Mayr 001 (CoRR, 2020)

Nengo and Low-Power AI Hardware for Robust, Embedded Neurorobotics. - Travis DeWolf; Pawel Jaworski; Chris Eliasmith (Frontiers Neurorobotics, 2020)

A Neurocomputational Model of Prospective and Retrospective Timing. - Joost de Jong; Aaron R. Voelker; Terrence C. Stewart; Chris Eliasmith; Elkan G. Akyürek; Hedderik van Rijn (CogSci, 2021)

Continuous then discrete: A recommendation for building robotic brains. - Chris Eliasmith; P. Michael Furlong (CoRL, 2021)

Parallelizing Legendre Memory Unit Training. - Narsimha Reddy Chilkuri; Chris Eliasmith (ICML, 2021)

A Spiking Neural Network for Image Segmentation. - Kinjal Patel; Eric Hunsberger; Sean Batir; Chris Eliasmith (CoRR, 2021)

Language Modeling using LMUs: 10x Better Data Efficiency or Improved Scaling Compared to Transformers. - Narsimha Chilkuri; Eric Hunsberger; Aaron Voelker; Gurshaant Malik; Chris Eliasmith (CoRR, 2021)

Passive Nonlinear Dendritic Interactions as a Computational Resource in Spiking Neural Networks. - Andreas Stöckel; Chris Eliasmith (Neural Comput., 2021)

Simulating and Predicting Dynamical Systems With Spatial Semantic Pointers. - Aaron R. Voelker; Peter Blouw; Xuan Choo; Nicole Sandra-Yaffa Dumont; Terrence C. Stewart; Chris Eliasmith (Neural Comput., 2021)

Comparing Loihi with a SpiNNaker 2 prototype on low-latency keyword spotting and adaptive robotic control. - Yexin Yan; Terrence C. Stewart; Xuan Choo; Bernhard Vogginger; Johannes Partzsch; Sebastian Höppner; Florian Kelber; Chris Eliasmith; Steve B. Furber; Christian Mayr 001 (Neuromorph. Comput. Eng., 2021)

Connecting Biological Detail With Neural Computation: Application to the Cerebellar Granule-Golgi Microcircuit. - Andreas Stöckel; Terrence C. Stewart; Chris Eliasmith (Top. Cogn. Sci., 2021)

Reinforcement Learning, Social Value Orientation, and Decision Making: Computational Models and Empirical Validation. - Peter Duggins; Terrence C. Stewart; Chris Eliasmith (CogSci, 2022)

A model of path integration that connects neural and symbolic representation. - Nicole Sandra-Yaffa Dumont; Jeff Orchard; Chris Eliasmith (CogSci, 2022)

Fractional Binding in Vector Symbolic Architectures as Quasi-Probability Statements. - P. Michael Furlong; Chris Eliasmith (CogSci, 2022)

Debugging using Orthogonal Gradient Descent. - Narsimha Chilkuri; Chris Eliasmith (CoRR, 2022)

Computational properties of multi-compartment LIF neurons with passive dendrites. - Andreas Stöckel; Chris Eliasmith (Neuromorph. Comput. Eng., 2022)

Constructing functional models from biophysically-detailed neurons. - Peter Duggins; Chris Eliasmith (PLoS Comput. Biol., 2022)

Neuromorphic control of a simulated 7-DOF arm using Loihi. - Travis DeWolf; Kinjal Patel; Pawel Jaworski; Roxana Leontie; Joe Hays; Chris Eliasmith (Neuromorph. Comput. Eng., 2023)

Biologically-Plausible Markov Chain Monte Carlo Sampling from Vector Symbolic Algebra-Encoded Distributions. - P. Michael Furlong; Kathryn Simone; Nicole Sandra-Yaffa Dumont; Madeleine Bartlett; Terrence C. Stewart; Jeff Orchard; Chris Eliasmith (ICANN (4), 2024)

Model Predictive Control in the Legendre Domain. - Graeme Damberger; Chris Eliasmith (ACC, 2025)

Biologically-Inspired Representations for Adaptive Control with Spatial Semantic Pointers. - Graeme Damberger; Kathryn Simone; Chandan Datta; Ram Eshwar Kaundinya; Juan Escareño; Chris Eliasmith (NICE, 2025)

Improving Rule-based Reasoning in LLMs via Neurosymbolic Representations. - Varun Dhanraj; Chris Eliasmith (CoRR, 2025)