Prof Kevin Gurney (ABRG Co-director)

Contact

Department of Psychology, University of Sheffield, Sheffield, S10 2TP, UK.

Tel: +44 (0) 114 222 6566

Email: k.gurney/at/sheffield.ac.uk

Summary

I am a Professor of Computational Neuroscience at Sheffield and co-Director of the ABRG. My formal education and research experience have spanned a variety of disciplines including mathematical physics, digital systems, neural networks, experimental psychology, and computational neuroscience. Over the last decade I have specialised in the "action selection" problem and its solution in the basal ganglia and associated brain circuits. Recently this has expanded to include an investigation of sensorimotor loops controlling visual gaze. I am also interested in plasticity in basal ganglia and its role in developing new action repertoires. I have worked on models at many levels of description - from single neurons to brains systems and robots; each level of description has a role to play in our understanding of the brain. I am a firm believer in data sharing, and the need to develop powerful and widely used tools for building models in neuroscience.

Research Interests

• Computational models at multiple levels of description
• Specific brain areas/functions include basal ganglia and action selection, active vision, and intrinsically motivated learning
• Automated parameter optimization methods for conductance-based models
• Neuroinformatics: Middle-ware, high-level model description languages
• Principal Investigator on the REVERB Project
• Co-Investigator on the CARMEN e-Science Project
• Co-Investigator on the IM-CLeVeR Project

Web resources

• Computational Neuroscience lecture notes
• Best-selling Introductory book on neural networks
• InsideTrak An interactive art-meets-science exhibition of active vision
• Direct link to YouTube video of InsideTrak

Publications

2015

• Caballero JA, Humphries MD, Gurney KN (2015), Evidence for sub-optimality and large-scale recursion during decision formation in the macaque brain, Symposium on Biology of Decision Making.
• K. N. Gurney, M. D. Humphries, P. Redgrave (2015), A new framework for cortico-striatal plasticity: behavioural theory meets in vitro data at the reinforcement-action interface, PLoS Biology, 13, 1,e1002034.
• Caballero JA, Lepora NF, Gurney KN (2015), Probabilistic decision making with spikes: from ISI distributions to behaviour via information gain, PLoS ONE, 10(4):e0124787.

2014

• Ashvin Shah and Kevin N. Gurney (2014), Emergent Structured Transition from Variation to Repetition in a Biologically-Plausible Model of Learning in Basal Ganglia, Frontiers in Psychology (Cognitive Science), 5:91.
• A. Shah, K. N. Gurney, (2014), Finding minimal action sequences with a simple evaluation of actions, Frontiers in Computational Neuroscience, 8, 151.
• Fiore, V.G., Sperati, V., Mannella, F., Mirolli, M., Gurney, K., Friston, K., Dolan, R.J., Baldassarre, G. (2014), Keep focussing: striatal dopamine multiple functions resolved in a single mechanism tested in a simulated humanoid robot, Frontiers in Psychology, 5, 124.
• A. Tomkins, E. Vasilaki, K.N. Gurney, M.D. Humphries (2014), Transient and steady-state selection in the striatal microcircuit, Frontiers in Computational Neuroscience, 7.

2013

• Caballero JA, Lepora NF, Gurney KN, (2013), Decision-making out of neural events: from discrimination information to psychometric power laws, BMC Neuroscience, 14(Suppl 1):P153.
• F. Mannella, K.N. Gurney, G. Baldassare (2013), The nucleus accumbens as a nexus between values and goals in goal-directed behavior: a review and a new hypothesis, Frontiers in Behavioural Neuroscience, 7.
• Gurney K, Lepora N, Shah A, Koene A and Redgrave P (2013), Action Discovery and Intrinsic Motivation: A Biologically Constrained Formalisation, Intrinsically Motivated Learning in Natural and Artificial Systems, G Baldassarre and M Mirolli, editors. Chapter 7, pages 151--181. Published by Springer-Verlag, Berlin Heidelberg. .
• A. Blenkinsop, K.N. Gurney (2013), Multi-scale modelling with spikes and rate codes: a demonstration in a model of the basal ganglia, BMC Neuroscience, 14, Suppl. 1 P154.
• P. Richmond, A. Cope, K.N. Gurney, D.J. Allerton (2013), From Model Specification to Simulation of Biologically Constrained Networks of Spiking Neurons, Neuroinformatics, 12, 307-323.
• Redgrave, P., Gurney, K., Stafford, T., Thirkettle, M. & Lewis, J (2013), The role of the basal ganglia in discovering novel actions , Intrinsically Motivated Learning in Natural and Artificial Systems, G Baldassarre and M Mirolli, editors. p129-150. Springer-Verlag, Berlin Heidelberg.
• Martin Thirkettle, Thomas Walton, Ashvin Shah, Kevin Gurney, Peter Redgrave, Tom Stafford (2013), The path to learning: Action acquisition is impaired when visual reinforcement signals must first access cortex, Behavioural Brain Research, volume 243, pages 267--272.
• Bolado-Gomez, R., Gurney K.N. (2013), A biologically plausible embodied model of action discovery, Frontiers in Neurorobotics, 7(4).

2012

• Lepora N and Gurney K (2012), The basal ganglia optimize decision making over general perceptual hypotheses, Neural Computation, 24(11): 2924-2945.
• Lepora N, Sullivan JC, Mitchinson B, Pearson M, Gurney K and Prescott T (2012), Brain-inspired Bayesian perception for biomimetic robot touch, proceedings of IEEE International Conference on Robotics and Automation (ICRA), St. Paul, MN, 5111-5116.
• Lepora N, Fox C, Evans M, Diamond M, Gurney K and Prescott T (2012), Optimal decision-making in mammals: insights from a robot study of rodent texture discrimination, Royal Society Interface , 9 (72): 1517-1528.
• Lepora N, Overton P and Gurney K (2012), Efficient fitting of conductance-based model neurons from somatic current clamp, Journal of Computational Neuroscience, 32(1): 1-24.
• Beste, C., Tomkins, A., Vasilaki, E., Saft, C., Gurney, K., & Humphries, M (2012), J07 Increased cognitive functioning in manifest HD - empirical evidence and computational modelling, Journal of Neurology, Neurosurgery & Psychiatry , (Proceedings of European Huntington’s Disease Network 2012 plenary meeting), 83: A37-A38.
• Baldassarre, G., Mannella, F., Fiore, V.G., Redgrave, P., Gurney K.N., Miroli, M. (2012), Intrinsically motivated action–outcome learning and goal-based action recall: A system-level bio-constrained computational model, Neural Networks, 41: 168-187.
• Gurney, K., & Humphries, M. (2012), Methodological Issues in Modelling at Multiple Levels of Description, Computational Systems Neurobiology, 259-281 (Springer).
• Humphries, M. D., & Gurney, K. (2012), Network effects of subthalamic deep brain stimulation drive a unique mixture of responses in basal ganglia output., European Journal of Neuroscience, 36(2): 2240-2251.
• Humphries, M. D., Khamassi, M., & Gurney, K. (2012), Dopaminergic control of the exploration-exploitation trade-off via the basal ganglia, Frontiers in Decision Neuroscience, 6(9).
• Tom Stafford, Martin Thirkettle, Tom Walton, Nicolas Vautrelle, Len Hetherington, Michael Port, Kevin Gurney, Pete Redgrave (2012), A Novel Task for the Investigation of Action Acquisition, PLoS One, 7(6), e37749.
• Chambers, J. M., Gurney, K., Humphries, M., & Prescott, A (2012), Mechanisms of choice in the primate brain: a quick look at positive feedback, Modelling Natural Action Selection, 390-420 (Cambridge Univ. Press).
• Tomkins, A., Humphries, M., Beste, C., Vasilaki, E., & Gurney, K. (2012), How Degrading Networks Can Increase Cognitive Functions, Artificial Neural Networks and Machine Learning – ICANN 2012: Lecture Notes in Computer Science, 185-192.
• Blenkinsop, A., Kadirkamanthan, V., Anderson, S., & Gurney, K. N. (2012), A firing rate model of the basal ganglia incorporating action selection with parameter estimation by approximate Bayesian computation, Presented at the Society for Neuroscience Annual Meeting, New Orleans.

2011

• Lepora N, Fox C, Evans M, Mitchinson B, Motiwala A, Sullivan J, Pearson M, Welsby J, Pipe T, Gurney K and Prescott T (2011), A general classifier of whisker data using stationary naïve Bayes: Application to BIOTACT robots, proceedings of Towards Autonomous Robotic Systems (TAROS), Sheffield, Springer LNCS 6856:13-23.
• J.M. Lewis, J.M. Chambers, P. Redgrave, K.N. Gurney (2011), Flexible sequential action selection in a computational model of multiple basal ganglia-thalamocortical loops., 2011 Neuroscience Meeting Planner. Washington, DC: Society for Neuroscience, 2011. Online.
• Lepora N, Blomeley C, Hoyland D, Bracci P, Overton P and Gurney K (2011), A simple method for characterizing passive and active neuronal properties applied to striatal neurons, European Journal of Neuroscience, 34(9):1390-1405.
• Caballero JA, Lepora NL, Gurney KN (2011), Sequential tests and biologically grounded multi-alternative decision making, BMC Neuroscience, 12(Suppl 1):P137.
• Jennifer M Lewis, Jonathan M Chambers, Peter Redgrave, Kevin Gurney (2011), A computational model of interconnected basal ganglia-thalamocortical loops for goal directed action sequences, BMC Neuroscience, 12(Suppl 1), 136.
• Cope, A., & Gurney, K. N. (2011), A biologically based model of active vision, Proceedings of AISB’11 - Architectures for Active Vision, 13-20.
• Tom Stafford, Leanne Ingram and Kevin N. Gurney (2011), Pieron’s Law holds during Stroop conflict: insights into the architecture of decision making, Cognitive Science 35, 1553–1566.
• Cope, A., Chambers, J., & Gurney, K. (2011), A Systems Integration Approach to Creating Embodied Biomimetic Models of Active Vision, Towards Autonomous Robotic Systems, 6856: 372-373 (Springer).
• Ashvin Shah and Kevin Gurney (2011), Dopamine-mediated action discovery promotes optimal behavior ‘for free’, Twentieth Annual Computational Neuroscience Meeting (CNS*2011), Poster presentation. Abstract available in BMC Neuroscience 2011, 12 (Suppl 1):P138 (18 July 2011). .
• Tomkins, A., Humphries, M. D., Vasilaki, E., & Gurney, K. N. (2011), How Degrading Networks Can Increase Select Cognitive Functions, Frontiers in Computational Neuroscience, Presented at the BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting, Freiburg, Germany.
• Tom Stafford and Kevin N. Gurney (2011), Additive factors do not imply discrete processing stages: a worked example using models of the Stroop task, Frontiers in Psychology, 2:287.

2010

• Ben Mitchinson, Tak-Shing Chan, Jon Chambers, Martin Pearson, Mark Humphries, Charles Fox, Kevin Gurney, Tony J. Prescott (2010), BRAHMS: Novel middleware for integrated systems computation, Advanced Engineering Informatics, 24(1):49-61.
• Lepora N, Pearson M, Mitchinson B, Evans M, Fox C, Pipe T, Gurney K and Prescott T (2010), Naive Bayes novelty detection in a whiskered moving robot, proceedings of IEEE International Conference on Robotics and Biomimetics (ROBIO 2010), 131-136.
• Cope, A., J.M. Chambers, D Barr, P. Dudek, and K. N. Gurney (2010), Systems level model integration and embodiment: a case study with gaze control, Bernstein Conference on Computational Neuroscience 2010 (BCCN 2010). Berlin..
• Gurney, K.N. (2010), Implications of recent experimental findings for models of the basal ganglia, Physiological Society Meeting 2010, Manchester.
• Caballero JA, Gurney KN (2010), Optimal decision making with biologically realistic neural signals, BMC Neuroscience, 11(Suppl 1):P25.
• Humphries, M D, Wood, R. and Gurney, K. (2010), Reconstructing the three dimensional GABAergic microcircuit of the striatum, PLoS Computational Biology, 6:e1001011. [Code]
• Gurney, K.N. (2010), Dopamine-modulated dynamics of the GABAergic striatal microcircuit, 10th IBAGS Meeting. New Jersey..
• Stewart, R.D. and K.N. Gurney (2010), Spiking neural network simulation: memory-optimal synaptic event scheduling, Journal of Computational Neuroscience, 30(3):721-728.
• Brohan, K., K.N. Gurney, and P. Dudek (2010), Using reinforcement learning to guide the development of self-organising feature maps for visual orienting, ICANN 2010, Thessaloniki.
• Lepora, N, M Evans, C. Fox, M.E. Diamond, K.N. Gurney and T.J. Prescott (2010), Naive Bayes texture classification applied to whisker data from a moving robot, IEEE World Congress on Computational Intelligence.
• Brohan, K., A. Cope, and K.N. Gurney (2010), Robotic Implementation of Visual Attention Based on Reward Modulated self-Organised Map of Feature Space, EPIROB 2010. Orenas Slott, Sweden.

2009

• Humphries, M. D., Wood, R. and Gurney, K. (2009), Dopamine-modulated dynamic cell assemblies generated by the GABAergic striatal microcircuit, Neural Networks, 22:1174-1188. [Code]
• Humphries, M.D., N. Lepora, R. Wood, and K.N. Gurney (2009), Reduced models of striatal neurons: dopamine modulation and dynamics, BMC Neuroscience, 10, (1) 321.
• Gurney, K., A. Hussain, J. Chambers, and R. Abdullah (2009), Controlled and automatic processing in animals and machines with application to autonomous vehicle control, Lecture Notes in Computer Science, 5768 LNCS, 198-207, Springer.
• Stafford, T., Gurney, K.N. & Ingram, L. (2009), Pieron’s Law holds in conditions of response conflict, Proceedings of the 31th Annual Conference of the Cognitive Science Society, (pp 2238-2243, eds N.A. Taatgen & H. van Rijn). Cognitive Science Society.
• Lepora, N,, P. Overton, and K.N. Gurney (2009), Efficient current-based optimization techniques for parameter estimation in multi-compartment neuronal models, BMC Neuroscience, 10 (1) 247.
• Cope, A., J.M. Chambers, and K.N. Gurney (2009), Object-based biasing for attentional control of gaze: a comparison of biologically plausible mechanisms, BMC Neuroscience, 10, (1) 19.
• Wood, R,, M.D. Humphries, and K.N. Gurney (2009), Does striatum support competitive dynamics? A test of this hypothesis using a biologically realistic model of the striatal microcircuit, BMC Neuroscience, 10, page 317.
• Gurney, K.N. (2009), Computational Models in Neuroscience: From membranes to Robots, Computational Modelling in Behavioural Neuroscience: Closing the gap between neurophysiology and behaviour, 107-136, Advances in Behavioural Brain Science. East Sussex, UK: Psychology Press.
• Bolado-Gomez, R., J.M. Chambers, and K.N. Gurney (2009), The basal ganglia and the 3-factor learning rule: reinforcement learning during operant conditioning, Frontiers in Computational Neuroscience, Proceedings of BCCN Conference.
• Humphries, M. D., Lepora, N., Wood, R. and Gurney, K. (2009), Capturing dopaminergic modulation and bimodal membrane behaviour of striatal medium spiny neurons in accurate, reduced models, Frontiers in Computational Neuroscience, 3:26. [Code]
• Gurney, K.N. (2009), Reverse Engineering the Vertebrate Brain: Methodological Principles for a Biologically Grounded Programme of Cognitive Modelling, Cognitive Computation, 1 (1) 29-41.
• Gurney, K.N., M.D. Humphries, and P. Redgrave (2009), Cortico-striatal plasticity for action-outcome learning using spike timing dependent eligibility, BMC Neuroscience, 10, (1) 135.

2008

• Redgrave, P., K.N. Gurney, and J. Reynolds (2008), What is reinforced by phasic dopamine signals?, Brain Research Reviews, 58, (2), 322-339.
• Ben Mitchinson, Tak-Shing Chan, Jon Chambers, Mark Humphries, Kevin Gurney and Tony Prescott (2008), BRAHMS: Novel middleware for integrated systems computation, Frontiers Neuroinformatics Conference Abstract: Neuroinformatics.
• Hoyland, D.S., N Lepora, R Wood, P.G. Overton, and K.N. Gurney (2008), Integrated data collection and automated parameter estimation in the construction of conductance-based models of neostriatal neurons, SfN 2008, Washington, Neuroscience Meeting Planner, Poster 694.4/UU65.
• Chambers, J.M., and K.N. Gurney (2008), A computational model of ‘inaction-selection’ in multiple domains of basal ganglia, SfN 2008 Washington., Neuroscience Meeting Planner, Poster 472.7/NN28.
• Wood, R, M. D Humphries, P.G. Overton, and K.N. Gurney (2008), Validation and dynamics of a computational model of the neostriatal microcircuit, SfN 2008, Washington, Neuroscience Meeting Planner, Poster 578.13/QQ15.
• Fox, C., N. Girdhar, and K.N. Gurney (2008), A causal Bayesian network view of reinforcement learning, Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21, 109-110.
• Humphries, M.D, P Redgrave, and K.N. Gurney (2008), Action-outcome contingency and stimulus-response habits as an exploration/exploitation action selection trade-off, SfN 2008 Washington., Neuroscience Meeting Planner Poster 98.3/UU33.
• Hussain, A., K.N. Gurney, R. Abdullah, and J.M. Chambers (2008), Emergent Common Functional Principles in Control Theory and the Vertebrate Brain: A Case Study with Autonomous Vehicle Control, Proceedings of the 18th international conference on Artificial Neural Networks, Part II, 949-958, Springer.
• Humphries, M. D. & Gurney, K. (2008), Network `small-world-ness': a quantitative method for determining canonical network equivalences, PLoS One, 3:e0002051.
• Gurney, K.N., and P Redgrave (2008), A model of sensory reinforced corticostriatal plasticity in the anaesthetised rat, SfN 2008, Washington, Neuroscience Meeting Planner Poster 180.6/RR3.
• Mitchinson, B., T.S. Chan, J.M. Chambers, M D Humphries, C. Fox, K.N. Gurney, and T J Prescott (2008), BRAHMS: Novel middleware for integrated systems computation, SFN Washington, Soc. Neurosci. Abstracts 798.10.

2007

• Humphries, M. D., Gurney, K. & Prescott, T. J. (2007), Is there a brainstem substrate for action selection?, Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1485):1627-1639. [Code]
• Pearson M. J., Pipe A. G., Mitchinson B., Gurney K., Melhuish C., Gilhespy I., & Nibouche M. (2007), Implementing spiking neural networks for real-time signal-processing and control applications: A model-validated FPGA approach, IEEE Transactions on Neural Networks, 18(5):1472-1487.
• Tony Prescott, Ben Mitchinson, Martin Pearson, Ian Gilhespy, Tony Pipe, Chris Melhuish, Mohktar Nibouche, Kevin Gurney and Peter Redgrave (2007), A robotic model of sensorimotor coordination in the rat whisker system, Barrels 2007 La Jolla, USA, abstract in Somatosensory and Motor Research 24(3):139-162.
• Stafford, T. & Gurney, K.N. (2007), Biologically constrained action selection improves cognitive control in a model of the Stroop task, Philosophical Transactions of the Royal Society B: Biological Sciences, 362 (1485), 1671-1684.
• Stewart, R.D, and K.N. Gurney (2007), Flexible, extensible, spiking neural network simulation, SfN SanDiego 2007, Neuroscience Meeting Planner 102.19/ZZ21.
• Bogacz, R., and K.N. Gurney (2007), The basal ganglia and cortex implement optimal decision making between alternative actions, SfN San Diego 2007, Neuroscience Meeting Planner 622.23/TT8.
• Bogacz R, Gurney K (2007), The basal ganglia and cortex implement optimal decision making between alternative actions. 19:442-477., Neural Computation, 19:442-477.
• Humphries, M. D. & Gurney, K. (2007), Solutions methods for simple model neurons, Neural Computation, 19:3216-3225. [Code]
• Humphries, M.D, and K.N. Gurney (2007), Deep brain stimulation of the subthalamic nucleus causes paradoxical inhibition of output in a computational model of the “parkinsonian” basal ganglia, SfN San Diego 2007, Neuroscience Meeting Planner #622.
• Charles Fox, Ben Mitchinson, Martin Pearson, Ian Gilhespy, Mohktar Nibouche, Tony Pipe, Chris Melhuish, Kevin Gurney and Tony Prescott (2007), A simple texture classifier from artificial whisker strain data, Barrels 2007 La Jolla, USA, abstract in Somatosensory and Motor Research 24(3):139-162.
• Humphries, M. D., & K.N.Gurney (2007), A means to an end: validating models by fitting experimental data, Neurocomputing, 70:1892-1896. [Code]
• Gurney, K., J. Chambers, and P. Redgrave (2007), A model of reinforcement learning in basal ganglia for action-outcome association, IBAGS IX Meeting.
• Gurney, K., J. Chambers, and P. Redgrave (2007), A model of reinforcement learning in basal ganglia for action-outcome association, IBAGS IX Meeting.

2006

• Gurney K.N. (2006), Neural networks for perceptual processing: from simulation tools to theories. , Philos Trans R Soc Lond B Biol Sci , 362:339-353..
• Humphries, M. D., Gurney, K. & Prescott, T. J. (2006), The brainstem reticular formation is a small-world, not scale-free, network, Proceedings of the Royal Society B: Biological Sciences, 273:503-511. [Code]
• Redgrave, P. & Gurney, K. (2006), The short-latency dopamine signal: a role in discovering novel actions? , Nature Reviews Neuroscience, 7:967-975..
• Gurney K.N, Prescott AJ, Redgrave P (2006), A Theoretical Treatment of Basal Banglia Function, Nova Science Pub Inc., (Bezard E, ed).
• Redgrave, P., and K.N. Gurney (2006), Timing is everything: Dopamine signals in temporal difference learning, 15thth Annual Computational Neuroscience Meeting, Edinburgh.
• Wood, R., M.D Humphries, P. Overton, R. Stewart, and K.N. Gurney (2006), Selection in a model of striatum with fast spiking interneurons, 15thth Annual Computational Neuroscience Meeting, Edinburgh.
• Bogacz, R., and K.N. Gurney (2006), The Brain Implements Optimal Decision Making between Alternative Actions, 28th Annual Conference of the Cognitive Science Society.
• T. J. Prescott, B. Mitchinson, M. J. Pearson, I. Gilhespy, M. Nibouche, A. G. Pipe, C. Melhuish, K. Gurney, P. Redgrave (2006), A robot model of sensorimotor co-ordination in the rat whisker system, SFN Atlanta, Soc. Neurosci. Abstracts 145.13.
• Humphries, M.D, and K.N. Gurney (2006), A means to an end: validating models by fitting experimental data, Neurocomputing , 20 (10, 1892-1896.
• Humphries, M. D., Stewart, R. D. & Gurney, K. (2006), A physiologically plausible model of action selection and oscillatory activity in the basal ganglia, Journal of Neuroscience, 26(50):12921-42. [Code]
• Stafford, T. & Gurney, K.N. (2006), Computational Models of Cognition, An Introduction to Cognitive Psychology: Processes and Disorders. Second Edition, Ed. David Groome. Hove, UK: Psychology Press..
• Pearson, M., M. Nibouche, I. Gilhespy, K.N. Gurney, C. Melhuish, B. Mitchinson, and A.J. Pipe (2006), A Hardware Based Implementation of a Tactile Sensory System for Neuromorphic Signal Processing Applications, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Toulouse.
• Bogacz, R., K.N. Gurney, and T Larsen (2006), The brain implements optimal decision making mechanisms, 15th Annual Computational Neuroscience Meeting, Edinburgh.
• Prescott, T. J., Montes-Gonzalez, F., Gurney, K., Humphries, M. D. and Redgrave, P. (2006), A robot model of the basal ganglia: Behavior and intrinsic processing, Neural Networks, 19:31-61.

2005

• Chambers, J. M., Gurney, K., Humphries, M. & Prescott, T. (2005), Mechanisms of choice in the primate brain: a quick look at positive feedback, Modelling Natural Action Selection, J. J. Bryson, T. J. Prescott & A. Seth, pp. 45-52, AISB Press..
• Gurney, K.N., M.D. Humphries, and R. Stewart (2005), A spiking neuron model of basal ganglia for action selection can account for dopamine - modulated oscillatory phenomena, SfN 2005, Washington, Neuroscience Meeting Planner #517.
• Pearson M, Gilhespy I, Gurney K, Melhuish C, Mitchinson B, Nibouche M, Pipe A (2005), A real-time, FPGA based, biologically plausible neural network processor, proceedings of International Conference on Artificial Neural Networks (ICANN), Warsaw, Springer LNCS 3697:1021-1026.
• Stafford, T., and K.N. Gurney (2005), The role of response mechanisms in determining reaction time performance: Pieron's law revisited, Psychon Bull Rev, 11, no. 6: 975-987.
• Mitchinson, B., A.J. Prescott, K.N. Gurney, M. Pearson, I. Gilhespy, and A.J. Pipe (2005), A computational model of a brainstem loop for whisker pattern generation, SFN Washington, Soc. Neurosci. Abstracts 625.4.
• Humphries, M. D., Gurney, K. & Prescott, T. J. (2005), Is there an integrative center in the vertebrate brain-stem? A robotic evaluation of a model of the reticular formation viewed as an action selection device, Adaptive Behaviour, 13:97-113.
• Humphries, M., Gurney, K. & Prescott, T. (2005), Action selection in a macroscopic model of the brainstem reticular formation, Modelling Natural Action Selection, J. J. Bryson, T. J. Prescott & A. Seth, pp. 61-68, AISB Press.
• Stafford, T, & Gurney, K. (2005), The basal ganglia as the selection mechanism in a cognitive task, Modelling Natural Action Selection, J. J. Bryson, T. J. Prescott & A. Seth (Eds.), pp. 77-83, AISB Press.
• Pearson, M., I. Gilhespy, K.N. Gurney, C. Melhuish, B. Mitchinson, M. Nibouche, and A.J. Pipe (2005), Design and FPGA Implementation of an Embedded Real-Time Biologically Plausible Spiking Neural Network Processor, proceedings of 15th International Conference on Field Programmable Logic and Applications (FPL), Tampere, Finland, ISBN 0-7803-9362-7.
• Pipe, A.J., C. Melhuish, M. Pearson, I. Gilhespy, B. Mitchinson, K.N. Gurney, and A.J. Prescott (2005), Artificial, active vibrissal array modeling the mechanics and neurology of rat whisking, SFN Washington, Soc. Neurosci. Abstracts 625.5.

2004

• Gurney, K., Humphries, M., Wood, R., Prescott, T.J. & Redgrave, P. (2004), Testing computational hypotheses of brain systems function: a case study with the basal ganglia, Network: Computation in Neural Systems, 15:263-290. [Code]
• Humphries, M., R. Stewart, T.J. Prescott, P. Redgrave, and K.N. Gurney (2004), A spiking neuron model of basal ganglia demonstrating plausible action selection and switching, International Basal Ganglia Society (IBAGS) VIIIth International Triennial Meeting. Crieff, Scotland..
• Mitchinson, B., Gurney, K. N., Redgrave, P., Melhuish, C., Pipe, A. G., Pearson, M., Gilhespy, I. & Prescott, T. J. (2004), Empirically inspired simulated electro-mechanical model of the rat mystacial follicle-sinus-complex, Proceedings of the Royal Society B: Biological Sciences, 271:2509-2516.
• Stafford, T. & Gurney, K. N. (2004), The role of response mechanisms in determining reaction time performance: Pieron’s Law revisited, Psychonomic Bulletin & Review, 11:975-987.
• Gurney, K., Prescott, T.J., Wickens, J. R. & Redgrave, P. (2004), Computational models of the basal ganglia: from robots to membranes, Trends in Neurosciences, 27:453-459.
• Gurney, K. N., A.J. Prescott, J.R. Wickens, and P Redgrave (2004), Mechanism mining and mapping in models of the basal ganglia, 8th IBAGS Meeting, Crieff, Scotland.
• Wood, R., Gurney, K. N. & Wilson, C. J. (2004), A novel parameter optimisation technique for compartmental models applied to a model of a striatal medium spiny neuron, Neurocomputing, 58-60:1109-1116.
• Gurney, K. N. & Overton, P. G. (2004), A model of short and long range selective processing in neostriatum, Neurocomputing, 58-60:555-562.
• Stewart, R., K.N. Gurney, and M.D Humphries (2004), A spiking neuron model of cortically-evoked response patterns in the basal ganglia, International Basal Ganglia Society (IBAGS) VIIIth International Triennial Meeting. Crieff, Scotland..

2003

• Prescott, T. J., Gurney, K., and Redgrave, P. (2003), Basal Ganglia, The Handbook of Brain Theory and Neural Networks, M. A. Arbib (Ed.), 2nd Edition .
• Pegg, E.J., K.N. Gurney, R.W. Parks, and A.J. Prescott (2003), Reliability and Validity of a Paced Computer Stroop Task for fMRI Reliability and validity of a paced computer Stroop task for fMRI, Archives of Clinical Neuropsychology, 18, (7), 755.
• Girard B, Cuzin V, Guillot A, Gurney KN, Prescott TJ (2003), Comparison of a “winner-takes-all” and a vertebrate inspired model of action selection embedded in a Lego Mindstorms robot , Journal of Integrative Neuroscience , 2:179-200.
• Parks, R.W., M.D. Hunter, K.N. Gurney, T.J. Prescott, D.S. Levine, I.D. Wilkinson, F. Clay, E. Pegg, P.W.R. Woodruff, and S.A. Spence (2003), A stroop task protocol with overt verbal response during fMRI, uman Brain Mapping Conference, New York, USA..
• Wood, R. & Gurney, K.N. (2003), A Rapid Search Technique for Finding Maximal Conductances in Biophysical Models of Neural Membranes, British Neurosci. Assoc. Abstr., 17:111.
• Prescott, T. J. Gurney, K. Gonzalez, F. M., Humphries, M. & Redgrave, P. (2003), Action selection in a robot model of the basal ganglia: The role of simulated dopamine, Society for Neuroscience, Program No. 915.5.
• Humphries, M. D., Prescott, T. J. & Gurney, K. N. (2003), The interaction of recurrent axon collateral networks in the basal ganglia, Artifical Neural Networks and Neural Information Processing, O. Kaynak, E. Alpaydin, E. Oja & L. Xu (Eds.), ICANN/ICONIP 2003 (LNCS 2714) pp. 797-804, Berlin: Springer-Verlag.
• Redgrave, P., Prescott, T.J. & Gurney, K. (2003), Selection and the basal ganglia: A role for dopamine, Basal Ganglia VI,, Graybiel, A.M and DeLong, M. (Eds.). New York: Kluwer.

2002

• Prescott, T. J., Gurney, K., Montes-Gonzalez, F., Humphries, M. and Redgrave, P. (2002), The robot basal ganglia: Action selection by an embedded model of the basal ganglia, Basal Ganglia VII, L. F. B. Nicholson and R. Faulls (Eds.) New York: Plenum Press.
• Wood, R. & Gurney, K.N. (2002), A New Parametric Search Technique for Biophysical Models: A Study in the Rat Neostriatal Projection Neuron, Society For Neuroscience Abstracts, Program No. 764.16, Washington, DC..
• Girard, B., Cuzin, V., Guillot, A., Gurney, K., and Prescott, T.J. (2002), Comparing a brain-inspired robot action selection mechanism with winner-takes-all, From Animals to Animats 7: Proceedings of the Seventh International Conference on Simulation of Adaptive Behaviour, MIT Press, Cambridge, MA, pp. 75–84.
• Prescott, T.J., K.N. Gurney, and P. Redgrave (2002), The Basal Ganglia, The Handbook of Brain Theory and Neural Networks. MIT Press..
• Humphries, M. D. & Gurney, K. N. (2002), The role of intra-thalamic and thalamocortical circuits in action selection, Network: Computation in Neural Systems, 13:131-156. [Code]

2001

• Prescott, T.J., F. Montes-Gonzales, K.N. Gurney, M. Humphries, and P. Redgrave (2001), Why is efference copy information sent to the basal ganglia? inspiration from an embodied model, BNA 16th National Meeting, 91.
• Humphries, M.D., and K.N. Gurney (2001), Slow oscillatory activity in a computational model of the basal ganglia, BNA 16th National Meeting, Harrogate, UK .
• Prescott, T.J., K.N. Gurney, F. Montes-Gonzalez, M. Humphries, and P. Redgrave (2001), The robot basal ganglia: Control of action selection by an embedded model of the basal ganglia, VIth Triennial neeting of the International basal Ganglia Society. Bay of Islands, New Zealand..
• Wood, R., Gurney, K. & Redgrave, P. (2001), 'Direct Pathway' Connections to Globus Pallidus in a Computational Model of the Basal Ganglia, British Neurosci. Assoc. Abstr., 16:79.
• Gurney, K.N., R. Wood, and P. Redgrave (2001), 'Direct pathway' connections to globus pallidus in a computational model of the basal ganglia, SfN 2001 Meeting San Diego, Neuroscience Meeting Planner #514.
• McHaffie, G.J., T.J. rescott, F. Montes-Gonzales, K.N. Gurney, M. Humphries, T.R. Stanford, and P Redgrave (2001), Why is efference copy information directed to the basal ganglia? Inspiration from an embodied model, SfN 2001 Meeting San Diego, Neuroscience Meeting Planner #514.
• Humphries, M. D. & Gurney, K. N. (2001), A pulsed neural network model of bursting in the basal ganglia, Neural Networks, 14:845-863.
• Gurney, K. N. (2001), Information processing in dendrites I. Input pattern generalisation, Neural Networks, 14:991-1004.
• Gurney, K., Redgrave, P. & Prescott, T.J. (2001), A computational model of action selection in the basal ganglia I. A new functional anatomy, Biological Cybernetics, 84:401-410.
• Gurney, K. N. (2001), Information processing in dendrites II. Information theoretic complexity, Neural Networks, 14:1005-1022.
• Gurney, K., Redgrave, P. & Prescott, T.J. (2001), A computational model of action selection in the basal ganglia II. Analysis and simulation of behaviour, Biological Cybernetics, 84:411-423.

2000

• Stafford, T., and K.N. Gurney (2000), A computational model of response selection in the Stroop task, BPS Cognitive Psychology Section 17th Annual Conference, Essex, UK, British Psychological Society Vol 9, No 2.
• Humphries, M. D. & Gurney, K. (2000), A computational model of action selection in the basal ganglia: Thalamic and cortical interactions, European Journal of Neuroscience, 12, supp. 11, 260.10.
• Montes Gonzalez, F. Prescott, T.J., Gurney, K. Humphries, M., & Redgrave, P. (2000), An embodied model of action selection mechanisms in the vertebrate brain, From Animals to Animats 6: Proceedings of the Sixth International Conference on Simulation of Adaptive Behaviour, J-A. Meyer, A. Berthoz, D. Floreano, H. Roitblat, & S. W. Wilson (Eds.), Cambridge MA: MIT Press.
• Gonzalez, F.Montes, T.J. Prescott, K.N. Gurney, M. Humphries, and P. Redgrave (2000), The robot basal ganglia: control of robot action selection by an embodied model of the vertebrate basal ganglia, In Federation of European Neurosciences Meeting, Brighton, UK, vol 12, supp 11.

1999

• Humphries, M. D. & Gurney, K. (1999), A computational model of action selection in the basal ganglia: Thalamic and cortical interactions, Technical Report AIVRU132, The University of Sheffield.
• Groome, D., H. Dewart, A. Esgate, K.N. Gurney, and N. Towell (1999), Computational Models of Cognition, An Introduction to Cognitive Psychology: Processes and Disorders, 261-291. Psychology Press, 1st Ed..
• Prescott, T.J., Redgrave, P., & Gurney, K. (1999), Layered control architectures in robots and vertebrates, Adaptive Behavior, 7:99-127.
• Redgrave, P., Prescott, T.J. & Gurney, K. (1999), The basal ganglia: a vertebrate solution to the selection problem?, Neuroscience, 89:1009-1023.
• Wright, M.J., and K.N. Gurney (1999), Visual discrimination of direction changes based upon two types of angular motion, Vision Research, 39, 1927-1941.
• Redgrave, P., Prescott, T.J. & Gurney, K. (1999), Is the short latency dopamine burst too short to signal reinforcement error?, Trends in Neurosciences, 22:146-151.

1998

• Redgrave, P., T.J. Prescott, and K.N. Gurney (1998), Selection and the basal ganglia: A Role for Dopamine, Vth Triennial neeting of the International basal Ganglia Society. Basal Ganglia. Cape Cod, USA..
• Gurney, K.N., P. Redgrave, and T.J. Prescott (1998), computational model of selective properties within basal ganglia: a reinterpretation of functional anatomy, SfN 1998, Los Angeles, Neuroscience Meeting Planner #24:649.
• Gurney, K.N., T.J. Prescott, and P. Redgrave (1998), A model of intrinsic processing in the basal ganglia, Second International conference on cognitive and neural systems, pp47. Boston..
• Gurney, K., Prescott, T.J., & Redgrave, P. (1998), The basal ganglia viewed as an action selection device, The Proceedings of the International Conference on Artificial Neural Networks, Springer-Verlag: Berlin, pp1033-1038.
• Gurney, K., Redgrave, P. & Prescott, T.J. (1998), Analysis and simulation of a model of intrinsic processing in the basal ganglia, Technical Report AIVRU131, The University of Sheffield.

1997

• Wright, M.J., and K.N. Gurney (1997), Coherence and motion transparency in rigid and non-rigid plaids, Perception , 553-567.
• Redgrave, P., Prescott, T.J., & Gurney, K. (1997), The basal ganglia: a neural architecture for conflict resolution, Soc Neurosci Abstr, 23:192.
• Gurney, K.N. (1997), An Introduction to Neural Networks, UCL Press (Taylor and Francis group)..

1996

• Wright, M.J., and K.N. Gurney (1996), Thresholds for detecting change in shape, Applied Vision Association Newsletter, 117: 33-34.
• Gurney, K.N., and M.J. Wright (1996), A biologically plausible model of early visual motion processing I: Theory and implementation, Biological Cybernetics, 74: 339-348..
• Gurney, K.N., and M.J. Wright (1996), Rotation and radial motion thresholds support a 2-stage model of differential motion analysis, Perception, 25: 5-26.
• Gurney, K.N., and M.J. Wright (1996), A model for the spatial integration and differentiation of velocity signals, Vision Research, 36: 2939-2955.
• Gurney, K.N., and M.J. Wright (1996), A biologically plausible model of early visual motion processing II: Psychophysical application, Biological Cybernetics, 74: 349-358.

1995

• Wright, M.J., and K.N. Gurney (1995), The discrimination of dynamic orientation changes in gratings and plaids, Perception , 24: 665-679.
• Wright, M.J., and K.N. Gurney (1995), Discrimination of rotation sense in gratings and plaids, ARVO, Vol. 34. Investigative Ophthalmology & Visual Science,.
• Wright, M.J., and K.N. Gurney (1995), Discrimination of direction changes in drifting plaids and gratings, 18th European Conference on Visual Perception, Tubingen, 24:(supplement):103. Perception. .
• Wright, M.J., and K.N. Gurney (1995), From spatiotemporal tuning to velocity analysis, Basic and Clinical Perspectives on Vision Research. Plenum Press..
• Wright, M.J., and K.N. Gurney (1995), Discrimination of changes in direction of motion, AVA conference on motion perception. Reading, UK..
• Gurney, K.N (1995), Towards a theory of neural-processing complexity, International Workshop on Information Processing in Cells and Tissues. Liverpool..

1994

• Wright, M.J., and K.N. Gurney (1994), Discrimination of dynamic orientation changes, 17th European Conference on Visual Perception: Eindhoven, Perception Vol 23.
• Gurney, K.N., and M.J. Wright (1994), Spatial integration of velocity signals via Markov Random Fields, 17th European Conference on Visual Perception: Eindhoven, Perception Vol 23.
• Wright, M.J., and K.N. Gurney (1994), Is there a special motion system for detecting orientation change, AVA conference on motion perception. UK..
• Walpole, T., K.N. Gurney, and M.J. Wright (1994), Phenomenology of transient motion coherence, 17th European Conference on Visual Perception: Eindhoven, Perception Vol 23.
• Hui, T., P. Morgan, H. Bolouri, and K.N. Gurney (1994), VLSI Implementation of a digital neural network with Reward-Penalty learning, In VLSI for Neural Networks and Artificial Intelligence, Plenum Press..
• Bolouri, H., P. Morgan, and K.N. Gurney (1994), Design, manufacture and evaluation of a scalable high-performance neural system, Electronics Letters, 30: 426..

1993

• Gurney, K.N., and M.J. Wright (1993), A quantitative model of early vision accounts for velocity discrimination performance of windowed gratings, 15th European Conference on Visual Perception. Edinburgh, Perception, 22:(supplement):90-91.
• Ferguson, A., P. Morgan, H. Bolouri, and K.N. Gurney (1993), Learning and topology organisation in large scale hypernets, Weightless Neural Network Workshop. York..

1992

• Gurney, K.N. (1992), Training Nets of Hardware Realisable Sigma-Pi Units, Neural Networks, 5: 289-303.
• Wright, M.J., and K.N. Gurney (1992), Lower threshold of motion for one- and two- dimensional patterns in central and peripheral vision, Vision Research, 32: 121-134.
• Gurney, K.N. (1992), Training Recurrent Nets of Hardware realisable Sigma-Pi Units, International Journal of Neural Systems, 3: 31-42.
• Gurney, K.N., and M.J. Wright (1992), Representing Image velocity in a self-organised neural network, Physiology Society Meeting, Newcastle, Journal of the Physiological Society 452, p287.
• Wright, M.J., and K.N. Gurney (1992), Dependence of stereomotion on the orientation of spatial frequency components, Journal of Physiological and Ophthalmic Optics Journal of Physiological and Ophthalmic Optics, 12: 264-268.
• Gurney, K.N., and M.J. Wright (1992), A self-organising neural network model of image velocity encoding, Biological Cybernetics, 68: 173-181.
• Gurney, K.N. (1992), Training Nets of Stochastic Units using System Identification, Neural Networks, 6: 133-145.
• Wright, M.J., and K.N. Gurney (1992), Coherence, transparency and the discrimination of rigid and non-rigid motion, ARVO Sarasota, Florida., pp 1050.
• Gurney, K.N. (1992), Weighted Nodes and RAM-Nets: A Unified Approach, Journal of Intelligent Systems, 2: 155-186.
• Hui, T., P. Morgan, K.N. Gurney, and H. Bolouri (1992), A cascadable 2048-neuron VLSI artificial network with on-board learning, ICANN , Aleksander and Taylor, editors, Artificial Neural Networks 2.
• Gurney, K.N., and M.J. Wright (1992), The detection and analysis of local optic flow, 15th European Conference on Visual Perception, Pisa, Italy, Perception, 21:(supplement) p40.
• Gurney, K.N., and M.J. Wright (1992), Digital Nets and intelligent systems, Journal of Intelligent Systems, 2, 1-10.

1991

• Hui, T., I. Wilkes, P. Morgan, H. Bolouri, and K.N. Gurney (1991), Design of a VLSI-based Artificial Neural Network System by a Student Group, IEE Colloquium on the Eurochip project: VLSI design in education. London..
• Gurney, K.N., and M.J. Wright (1991), Coherence and transparency in non-rigid plaid motion, 14th European Conference on Visual Perception, Vilnius, Perception, 20:(supplement).

1990

• Penney, W., K.N. Gurney, and J. Stonham (1990), Reward-Penalty training for logical neural networks, IASTED International conference, Artificial Intelligence applications and neural networks. Zurich..
• Gurney, K.N., and M.J. Wright (1990), A neural model of velocity encoding, International Joint Conference on Neural Networks, Seattle, I677-I682.
• Wright, M.J., and K.N. Gurney (1990), Speed Discrimination and Motion Detection in 2-D Patterns, 13th European Conference on Visual Perception, Paris, Perception, 19:(supplement).
• Gurney, K.N., and M.J. Wright (1990), A neural model of velocity encoding, 13th European Conference on Visual Perception, Paris, Perception, 19:(supplement).

1989

• Gurney, K.N., and D.K. Milligan (1989), Training in nets of Boolean functions with hidden units using simulated annealing, Neural Networks: from Models to Applications, 407-414, IDSET Paris..