Decision making and perceptual bistability in spike-based neuromorphic VLSI systems

Proceedings - IEEE International Symposium on Circuits and Systems

Volumn 2015-July, Issue , 2015, Pages 2708-2711

  Corradi, Federico ^a   You, Hongzhi ^b   Giulioni, Massimiliano ^c   Indiveri, Giacomo ^a  

^a UNIVERSITY OF ZURICH   (Switzerland)

^b UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^c ISTITUTO SUPERIORE DI SANITÀ   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER ARCHITECTURE; DECISION MAKING; DYNAMICS; PHYSIOLOGICAL MODELS; VLSI CIRCUITS;

COMPUTATIONAL PROPERTIES; DECISION-MAKING BEHAVIORS; DISTRIBUTED COMPUTATIONS; INFORMATION PROCESSING ARCHITECTURE; INTEGRATE-AND-FIRE NEURONS; PERCEPTUAL DECISION MAKING; PERSISTENT ACTIVITIES; WINNER-TAKE-ALL DYNAMICS;

RECURRENT NEURAL NETWORKS;

EID: 84946218727     PISSN: 02714310     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISCAS.2015.7169245     Document Type: Conference Paper

References (21)

1. G. M. Wittenberg, et al., "Synaptic reentry reinforcement based network model for long-term memory consolidation," Hippocampus, vol. 12, no. 5, pp. 637-647, 2002.
2. D. Amit, Modeling brain function: The world of attractor neural networks. Cambridge University Press, 1992.
3. X. Wang, "Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory," Journal of Neuroscience, vol. 19, pp. 9587-9603, November 1999.
4. D. Amit and N. Brunel, "Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex," Cerebral Cortex, vol. 7, pp. 237-252, 1997.
5. P. Del Giudice, et al., "Modeling the formation of working memory with networks of integrate-and-fire neurons connected by plastic synapses," Journal of Physiology Paris 97, pp. 659-681, 2003.
6. M. Rigotti, et al., "Attractor concretion as a mechanism for the formation of context representations," NeuroImage, vol. 52, no. 3, pp. 833-847, 2010.
7. S. Ganguli, et al., "One-dimensional dynamics of attention and decision making in lip," Neuron, vol. 58, no. 1, pp. 15-25, 2008.
8. G. Gigante, et al., "Bistable perception modeled as competing stochastic integrations at two levels," PLoS computational biology, vol. 5, no. 7, p. e1000430, 2009.
9. J. Braun and M. Mattia, "Attractors and noise: twin drivers of decisions and multistability," Neuroimage, vol. 52, no. 3, pp. 740-751, 2010.
10. X.-J. Wang, "Neural dynamics and circuit mechanisms of decisionmaking," Current Opinion in Neurobiology, 2012.
11. R. Ratcliff and G. McKoon, "The diffusion decision model: Theory and data for two-choice decision tasks," Neural computation, vol. 20, no. 4, pp. 873-922, 2008.
12. P. Miller and D. B. Katz, "Accuracy and response-time distributions for decision-making: linear perfect integrators versus nonlinear attractorbased neural circuits," Journal of computational neuroscience, vol. 35, no. 3, pp. 261-294, 2013.
13. S. Amari, "Dynamics of pattern formation in lateral-inhibition type neural fields," Biological Cybernetics, vol. 27, pp. 77-87, 1977.
14. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities," Proceedings of the national academy of sciences, vol. 79, no. 8, pp. 2554-2558, 1982.
15. J. I. Gold and M. N. Shadlen, "The neural basis of decision making," Annu. Rev. Neurosci., vol. 30, pp. 535-574, 2007.
16. X. Wang, "Probabilistic decision making by slow reverberation in cortical circuits," Neuron, vol. 36, no. 5, pp. 955-968, 2002.
17. K.-F. Wong and X.-J. Wang, "A recurrent network mechanism of time integration in perceptual decisions," The Journal of neuroscience, vol. 26, no. 4, pp. 1314-1328, 2006.
18. S. Moradi and G. Indiveri, "An event-based neural network architecture with an asynchronous programmable synaptic memory," Biomedical Circuits and Systems, IEEE Transactions on, vol. 8, no. 1, pp. 98-107, February 2014. [Online].
19. D. Fasnacht and G. Indiveri, "A PCI based high-fanout AER mapper with 2 GiB RAM look-up table, 0:8ms latency and 66 MHz output event-rate," in Conference on Information Sciences and Systems, CISS 2011, Johns Hopkins University, March 2011, pp. 1-6.
20. M. Giulioni, et al., "Robust working memory in an asynchronously spiking neural network realized in neuromorphic VLSI," Frontiers in Neuroscience, vol. 5, 2011.
21. F. Haake, et al., "Passage-time statistics for the decay of unstable equilibrium states," Physical Review A, vol. 23, no. 6, p. 3255, 1981.