Large-scale synthesis of functional spiking neural circuits

Proceedings of the IEEE

Volumn 102, Issue 5, 2014, Pages 881-898

  Stewart, Terrence C ^a   Eliasmith, Chris ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Neural computation; neural engineering framework (NEF); neural modeling; neuromorphic engineering; semantic pointer architecture (SPA); Spaun; spiking neural networks

Indexed keywords

BRAIN MODELS; COMPUTATION THEORY; COMPUTER AIDED SOFTWARE ENGINEERING; DYNAMICAL SYSTEMS; LINEAR CONTROL SYSTEMS; MAMMALS; NETWORK ARCHITECTURE; NEURAL NETWORKS; SEMANTICS; TIMING CIRCUITS;

NEURAL COMPUTATIONS; NEURAL ENGINEERING; NEURAL MODELING; NEUROMORPHIC ENGINEERING; SPAUN; SPIKING NEURAL NETWORKS;

COMPUTER SOFTWARE;

EID: 84900507774     PISSN: 00189219     EISSN: None     Source Type: Journal    
DOI: 10.1109/JPROC.2014.2306061     Document Type: Review

References (69)

1. J. R. Anderson D. Bothell M. D. Byrne S. Douglass C. Lebiere and Y. Qin "An integrated theory of the mind," Psychol. Rev. vol. 111 no. 4 pp. 1036-1060 2004.
2. J. R. Anderson How Can the Human Mind Occur in the Physical Universe Oxford U.K.: Oxford Univ. Press 2007.
3. H. Beckmann and M. Lauer "The human striatum in schizophrenia. II. Increased number of striatal neurons in schizophrenics," Psychiatry Res. vol. 68 no. 2-3 pp. 99-109 Mar. 1997.
4. T. Bekolay and C. Eliasmith "A general error-modulated STDP learning rule applied to reinforcement learning in the basal ganglia," presented at the Cogn. Syst. Neurosci. Conf. (COSYNE) Salt Lake City UT USA Feb. 24-27 2011.
5. T. Bekolay J. Bergstra E. Hunsberger T. Dewolf T. C. Stewart D. Rasmussen X. Choo A. R. Voelker and C. Eliasmith "Nengo: A Python tool for building large-scale functional brain models," Front. Neuroinf. vol. 7 Jan. 2014 DOI: 10.3389/fninf.2013. 00048.
6. T. Bekolay C. Kolbeck and C. Eliasmith "Simultaneous unsupervised and supervised learning of cognitive functions in biologically plausible spiking neural networks," in Proc. 35th Annu. Conf. Cogn. Sci. Soc. 2013 pp. 169-174.
7. E. Bienenstock L. N. Cooper and P. Munro "On the development of neuron selectivity: Orientation specificity and binocular interaction in visual cortex," J. Neurosci. vol. 2 pp. 32-48 1982.
8. X. Choo The ordinal serial encoding model: Serial memory in spiking neurons M.S. thesis Comput. Sci. Dept. Univ. Waterloo Waterloo ON Canada 2010.
9. X. Choo and C. Eliasmith "General instruction following in a large-scale biologically plausible brain model," in Proc. 35th Annu. Conf. Cogn. Sci. Soc. 2013 pp. 322-327.
10. S. Choudhary S. Sloan S. Fok A. Neckar Eric Trautmann P. Gao T. Stewart C. Eliasmith and K. Boahen "Silicon neurons that compute," in Proc. Int. Conf. Artif. Neural Netw. 2012 pp. 121-128.
11. J. Conklin and C. Eliasmith "An attractor network model of path integration in the rat," J. Comput. Neurosci. vol. 18 pp. 183-203 2005.
12. F. Corradi C. Eliasmith and G. Indiveri "Mapping arbitrary mathematical functions and dynamical systems to neuromorphic VLSI circuits for spike-based neural computation," presented at the Int. Symp. Circuits Syst. Melbourne Australia Jun. 1-5 2014.
13. T. Dewolf and C. Eliasmith "The neural optimal control hierarchy for motor control," Neural Eng. vol. 8 no. 6 2011 DOI: 10.1088/1741-2560/8/6/ 065009.
14. C. Eliasmith "Neural engineering: Unraveling the complexities of neural systems," IEEE Can. Rev. vol. 43 pp. 13-15 2003.
15. C. Eliasmith "A unified approach to building and controlling spiking attractor networks," Neural Comput. vol. 17 no. 6 pp. 1276-1314 2005.
16. C. Eliasmith How to Build a Brain: A Neural Architecture for Biological Cognition. New York NY USA: Oxford Univ. Press 2013.
17. C. Eliasmith and C. H. Anderson Neural Engineering: Computation Representation and Dynamics in Neurobiological Systems. Cambridge MA USA: MIT Press 2003.
18. C. Eliasmith "The complex systems approach: Rhetoric or revolution," Top. Cogn. Sci. vol. 4 no. 1 pp. 72-77 Jan. 2012 discussion 94-102.
19. C. Eliasmith T. C. Stewart X. Choo T. Bekolay T. DeWolf Y. Tang and D. Rasmussen "A large-scale model of the functioning brain," Science vol. 338 pp. 1202-1205 2012.
20. B. J. Fischer J. L. Pena and M. Konishi "Emergence of multiplicative auditory responses in the midbrain of the barn owl," J. Neurophysiol. vol. 98 no. 3 pp. 1181-1193 Sep. 2007.
21. J. A. Fodor and Z. W. Pylyshyn "Connectionism and cognitive architecture: A critical analysis," Cognition vol. 28 pp. 3-71 1988.
22. M. J. Frank and R. C. O'Reilly "A mechanistic account of striatal dopamine function in human cognition: Psychopharmacological studies with cabergoline and haloperidol," Behav. Neurosci. vol. 120 no. 3 pp. 497-517 Jun. 2006.
23. F. Galluppi J. Conradt T. Stewart C. Eliasmith T. Horiuchi J. Tapson B. Tripp S. Furber and R. Etienne-Cummings "Live Demo: Spiking ratSLAM: Rat hippocampus cells in spiking neural hardware," in Proc. IEEE Biomed. Circuits Syst. Conf. 2012 DOI: 10.1109/BioCAS.2012.6418493.
24. F. Galluppi S. Davies S. Furber T. Stewart and C. Eliasmith "Real time on-chip implementation of dynamical systems with spiking neurons," in Proc. IEEE Int. Joint Conf. Neural Netw. Jun. 2012 DOI: 10.1109/IJCNN.2012. 6252706.
25. R. W. Gayler "Vector symbolic architectures answer Jackendoff's challenges for cognitive neuroscience," in Proc. ICCS/ASCS Int. Conf. Cogn. Sci. Sydney Australia 2003 pp. 133-138.
26. A. P. Georgopoulos J. T. Lurito M. Petrides A. Schwartz and J. Massey "Mental rotation of the neuronal population vector," Science vol. 243 pp. 234-236 1989.
27. S. Gershman J. Cohen and Y. Niv "Learning to selectively attend," in Proc. 32nd Annu. Conf. Cogn. Sci. Soc. 2010 pp. 1270-1275.
28. K. Gurney T. Prescott and P. Redgrave "A computational model of action selection in the basal ganglia," Biol. Cybern. vol. 84 pp. 401-423 2001.
29. H. Jaeger "The 'echo state'approach to analyzing and training recurrent neural networks," GMD German Nat. Res. Inst. Comput. Sci. Tech. Rep. 148 2001.
30. P. Hagmann L. Cammoun X. Gigandet R. Meuli C. J. Honey V. J. Wedeen and O. Sporns "Mapping the structural core of human cerebral cortex," PLoS Biol. vol. 6 no. 7 Jul. 2008 e159.
31. J. Hasler and H. B. Marr "Finding a roadmap to achieve large neuromorphic hardware systems," Front. Neurosci. vol. 7 no. 118 2013 DOI: 10.3389/fnins.2013.00118.
32. D. O. Hebb The Organization of Behavior. New York NY USA: Wiley 1949.
33. G. E. Hinton and R. R. Salakhutdinov "Reducing the dimensionality of data with neural networks," Science vol. 313 no. 5786 pp. 504-507 Jul. 2006.
34. J. R. Hollerman and W. Schultz "Dopamine neurons report an error in the temporal prediction of reward during learning," Nature Neurosci. vol. 1 no. 4 pp. 304-309 Aug. 1998.
35. P. Jonas G. Major and B. Sakmann "Quantal components of unitary EPSCs at the mossy fibre synapse on CA3 pyramidal cells of rat hippocampus," J. Physiol. vol. 472 no. 1 pp. 615-663 1993.
36. M. Laubach M. S. Caetano B. Liu N. J. Smith N. S. Narayanan and C. Eliasmith "Neural circuits for persistent activity in medial prefrontal cortex," in Soc. Neurosci. Abstracts 2010 p. 200.18.
37. P. Lieberman Toward an Evolutionary Biology of Language. Cambridge MA USA: Belknap Press 2006.
38. P. Lieberman "The evolution of human speech: Its anatomical and neural bases," Current Anthropol. vol. 48 no. 1 pp. 39-66 2007.
39. A. Litt C. Eliasmith and P. Thagard "Neural affective decision theory: Choices brains and emotions," Cogn. Syst. Res. vol. 9 pp. 252-273 2008.
40. B. Liu M. Caetano N. Narayanan C. Eliasmith and M. Laubach "A neuronal mechanism for linking actions to outcomes in the medial prefrontal cortex," presented at the Cogn. Syst. Neurosci. Conf. Salt Lake City UT USA Feb. 24-27 2011.
41. W. Maass T. Natschläger and H. Markram "Real-time computing without stable states: A new framework for neural computation based on perturbations," Neural Comput. vol. 14 no. 11 pp. 2531-2560 2002.
42. D. MacNeil and C. Eliasmith "Fine-tuning and the stability of recurrent neural networks," PLoS ONE vol. 6 no. 9 Sep. 2011 e22885.
43. T. V. Maia "Reinforcement learning conditioning and the brain: Successes and challenges," Cogn. Affective Behav. Neurosci. vol. 9 no. 4 pp. 343-364 Dec. 2009.
44. S. Menon S. Fok and K. A. Boahen "Controlling robot dynamics with spiking neurons," presented at the Neural Inf. Process. Syst. Conf. Lake Tahoe NV USA Dec. 5-8 2013.
45. C. Parisien C. H. Anderson and C. Eliasmith "Solving the problem of negative synaptic weights in cortical models," Neural Comput. vol. 20 pp. 1473-1494 2008.
46. T. A. Plate "Holographic reduced representations: Convolution algebra for compositional distributed representations," in Proc. 12th Int. Joint Conf. Artif. Intell. J. Mylopoulos Ed. 1991 pp. 30-35.
47. T. A. Plate "Estimating analogical similarity by dot-products of holographic reduced representations Advances in Neural Information Processing Systems 6 J. D. Cowan G. Tesauro and J. Alspector Eds. San Mateo CA USA: Morgan Kaufmann 1994 pp. 1109-1116.
48. D. Rasmussen and C. Eliasmith "A neural model of rule generation in inductive reasoning," Top. Cogn. Sci. vol. 3 no. 1 pp. 140-153 2011.
49. D. Rasmussen and C. Eliasmith "A neural reinforcement learning model for tasks with unknown time delays," in Proc. 35th Annu. Conf. Cogn. Sci. Soc. 2013 pp. 3257-3262.
50. D. Rasmussen and C. Eliasmith "A spiking neural model that accounts for human performance and cognitive decline on Raven's advanced progressive matrices," Intelligence vol. 42 pp. 53-82 2014.
51. D. Rasmussen and C. Eliasmith "Modeling brain function: Current developments and future prospects," JAMA Neurol. vol. 70 no. 10 pp. 1325-1329 Oct. 2013.
52. P. Redgrave T. Prescott and K. Gurney "The basal ganglia: A vertebrate solution to the selection problem" Neuroscience vol. 86 pp. 353-387 1999.
53. F. Rieke D. Warland R. de Ruyter van Steveninick and W. Bialek Spikes: Exploring the Neural Code. Cambridge MA USA: MIT Press 1997.
54. N. C. Rust V. Mante E. P. Simoncelli and J. A. Movshon "How MT cells analyze the motion of visual patterns," Nature Neurosci. vol. 9 no. 11 pp. 1421-1431 Nov. 2006.
55. P. Sah S. Hestrin and R. A. Nicoll "Properties of excitatory postsynaptic currents recorded in vitro from rat hippocampal interneurones," J. Physiol. vol. 430 no. 1 pp. 605-616 1990.
56. R. Salakhutdinov and G. Hinton "Semantic hashing," presented at the SIGIR Workshop Inf. Retrieval Appl. Graph. Models Amsterdam The Netherlands Jul. 23-27 2007.
57. R. Singh and C. Eliasmith "Higher-dimensional neurons explain the tuning and dynamics of working memory cells," J. Neurosci. vol. 26 pp. 3667-3678 2006.
58. T. Stewart and C. Eliasmith "Compositionality and biologically plausible models," in Oxford Handbook of Compositionality. Oxford U.K.: Oxford Univ. Press 2011.
59. T. C. Stewart X. Choo and C. Eliasmith "Dynamic behaviour of a spiking model of action selection in the basal ganglia," in Proc. 10th Int. Conf. Cogn. Model. D. D. Salvucci and G. Gunzelmann Eds. 2010 pp. 235-240.
60. T. C. Stewart F.-X. Choo and C. Eliasmith "Symbolic reasoning in spiking neurons: A model of the cortex/basal ganglia/thalamus loop," in Proc. 32nd Annu. Meeting Cogn. Sci. Soc. 2010 pp. 1100-1105.
61. T. C. Stewart F. Galluppi and J. Conradt "Learning by example with SpiNNaker Nengo and an omni-directional robot," presented at the Bernstein Sparks Workshop Tutzing Germany Dec. 2-4 2013.
62. T. C. Stewart "The neural engineering framework," AISB Quarterly pp. 2-7 2012.
63. T. C. Stewart T. Bekolay and C. Eliasmith "Neural representations of compositional structures: Representing and manipulating vector spaces with spiking neurons," Connection Sci. vol. 23 no. 2 pp. 145-153 Jun. 2011.
64. T. C. Stewart Y. Tang and C. Eliasmith "A biologically realistic cleanup memory: Autoassociation in spiking neurons," Cogn. Syst. Res. vol. 12 no. 2 pp. 84-92 Jun. 2011.
65. T. C. Stewart B. Tripp and C. Eliasmith "Python scripting in the Nengo simulator," Front. Neuroinf. vol. 3 pp. 7-16 Jan. 2009.
66. R. S. Sutton and A. G. Barto "Toward a modern theory of adaptive networks: Expectation and prediction," Psychol. Rev. vol. 88 pp. 135-170 1981.
67. Y. Tang and C. Eliasmith "Deep networks for robust visual recognition," presented at the 27th Int. Conf. Mach. Learn. Haifa Israel J. Fürnkranz and T. Joachims Eds. Jun. 21-24 2010.
68. J. Tapson and A. van Schaik "Learning the pseudo inverse solution to network weights," Neural Netw. vol. 45 pp. 94-100 2013.
69. J. S. Taube "The head direction signal: Origins and sensory-motor integration," Annu. Rev. Neurosci. vol. 30 pp. 181-207 Jan. 2007.