A Model for the Diffusive Filling-In Algorithm Operating in Spike Mode

Genildo Nonato Santos, José Gabriel Rodriguez Carneiro Gomes

Abstract


To run cortical circuit simulations in spike mode, i.e. taking into account the neural representation of information in terms of sequences of electrical pulses (also known as spikes), the use of customized hardware, which is specific for this purpose, is recommended. Simulations using more traditional hardware can be prohibitive. In this context, theoretical predictions are important for customized hardware design. For example, theoretical predictions lead to an adequate neuron model choice. To make such theoretical predictions, the cortical circuit simulations are carried out in amplitude mode. Differently from the spike mode, in amplitude mode information is represented by sequences of scalar values that describe neural input and output spike rates. In this paper, it was proposed amplitude and spike mode simulations of a cortical algorithm, namely the diffusive filling-in algorithm, to investigate whether predictions based on the amplitude-mode results approximate well the behavior of the customized hardware (spike mode results). The diffusive filling-in algorithm was chosen because it is simple enough for spike-mode simulation in a conventional computer, but the proposed amplitude-mode prediction method is the same for more complex algorithms or circuits. We provide a highly realistic comparison between amplitude-mode and spike-mode in the diffusive filling-in case, which suggests that the amplitude mode is reliable for theoretical predictions useful for customized hardware design for cortical circuit simulation. The goal of this paper is not to bring closure to these discussions but to suggest a way of avoiding possible issues that could compromise the success of the customized device design.


Keywords


diffusive filling-in; visual system; silicon retina; spike encoding

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References


CHOUDHARY, S. et al. Artificial neural networks and

machine learning – icann 2012: 22nd international conference on artificial neural networks, lausanne, switzerland, september 11-14, 2012, proceedings, part i. In: . Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. cap. Silicon Neurons That Compute, p. 121–128. ISBN 978-3-642-33269-2. Dispon ́ıvel em: ⟨http://dx.doi.org/10.1007/978-3-642-33269-2 16⟩.

XUE, Y. Recent development in analog computation: a brief overview. Analog Integrated Circuits and Signal Processing, v. 86, n. 2, p. 181–187, 2016. ISSN 1573-1979. Dispon ́ıvel em: ⟨http://dx.doi.org/10.1007/s10470-015-0668-y⟩.

MEROLLA, P. et al. A digital neurosynaptic core usingembedded crossbar memory with 45pj per spike in 45nm. In: Custom Integrated Circuits Conference (CICC), 2011 IEEE. [S.l.: s.n.], 2011. p. 1–4. ISSN 0886-5930.

BENJAMIN, B. V. et al. Neurogrid: A mixed-analog- digital multichip system for large-scale neural simulations. Proceedings of the IEEE, v. 102, n. 5, p. 699–716, 2014. Dispon ́ıvel em: ⟨http://dblp.uni-trier.de/db/journals/pieee/ pieee102.html#BenjaminGMCCBAAMB14⟩.

CASSIDY, A. S.; GEORGIOU, J.; ANDREOU,

A. G. Design of silicon brains in the nano-cmos era: Spiking neurons, learning synapses and neural architecture optimization. Neural Networks, v. 45, n. 1, p. 4 – 26, 2013. ISSN 0893-6080. Neuromorphic Engineering: From Neural Systems to Brain-Like Engineered Systems. Dispon ́ıvel em: ⟨http://www.sciencedirect.com/science/article/pii/ S0893608013001597⟩.

CAO, Y.; GROSSBERG, S. Stereopsis and 3d surface perception by spiking neurons in laminar cortical circuits: A method for converting neural rate models into spiking models. Neural Networks, v. 26, p. 75 – 98, 2012. ISSN 0893-6080. Dispon ́ıvel em: ⟨http://www.sciencedirect.com/ science/article/pii/S0893608011002723⟩.

SANTOS, G. N.; GOMES, J. G. R. C. Implementation of a biologically inspired diffusive filling-in algorithm

for focal-plane image processing applications. In: Proc, BRIC-CCI and CBIC, Recife, Brazil, Conference on Computational Intelligence. [S.l.: s.n.], 2013.

KUNKEL, S. et al. Spiking network simulation code for petascale computers. Frontiers in Neuroinformatics,

v. 8, n. 78, 2014. ISSN 1662-5196. Dispon ́ıvel em: ⟨http://www.frontiersin.org/neuroinformatics/10.3389/fninf. 2014.00078/abstract⟩.

GROSSBERG, S.; KUHLMANN, L.; MINGOLLA, E. A neural model of 3d shape-from-texture: Multiple-scale filtering, boundary grouping, and surface filling-in. Vision Research, v. 47, n. 5, p. 634 – 672, 2007. ISSN 0042-6989. Dispon ́ıvel em: ⟨http://www.sciencedirect.com/science/ article/pii/S0042698906004950⟩.

BOAHEN, K. A. Retinomorphic Vision Systems: Reverse Engineering the Vertebrate Retina. Tese (Ph.D. thesis) — California Institute of Technology, Pasadena, CA, 1997.

ZAGHLOUL, K. A.; BOAHEN, K. A silicon retina that reproduces signals in the optic nerve. Journal of Neural Engineering, v. 3, n. 4, p. 257, 2006. Dispon ́ıvel em: ⟨http://stacks.iop.org/1741-2552/3/i=4/a=002⟩.

CHEN, E. P.; FREEMAN, A. W. A model for spatiotemporal frequency responses in the x cell pathway of the cat’s retina. Vision Research, v. 29, n. 3, p. 271 – 291, 1989. ISSN 0042-6989. Dispon ́ıvel em: ⟨http://www. sciencedirect.com/science/article/pii/004269898990076X⟩.

MARR, D. Vision : a computational investigation into the human representation and processing of visual information. San Francisco: W.H. Freeman, 1982. ISBN 0-7167-1284-9. Dispon ́ıvel em: ⟨http: //opac.inria.fr/record=b1079861⟩.

IZHIKEVICH, E. Which model to use for cortical spiking neurons? Neural Networks, IEEE Transactions on, v. 15, n. 5, p. 1063–1070, Sept 2004. ISSN 1045-9227.

LEVITT, J. B.; KIPER, D. C.; MOVSHON, J. A. Receptive fields and functional architecture of macaque v2. Journal of Neurophysiology, American Physiological Society, v.71,n.6,p.2517–2542,1994.ISSN0022-3077.Dispon ́ıvel em: ⟨http://jn.physiology.org/content/71/6/2517⟩.

GOVE, A.; GROSSBERG, S.; MINGOLLA,

E. Brightness perception, illusory contours, and corticogeniculate feedback. Visual Neuroscience, v. 12, p. 1027–1052, 11 1995. ISSN 1469-8714. Dispon ́ıvel em: ⟨http: //journals.cambridge.org/article S0952523800006702⟩.

YAZDANBAKHSH, A.; GROSSBERG, S. Fast synchronization of perceptual grouping in laminar visual cortical circuits. Neural Networks, v. 17, n. 5-6, p. 707–718, 2004. Dispon ́ıvel em: ⟨http://dx.doi.org/10.1016/j.neunet.2004.06.005⟩.

SEO, J.; SHNEIDERMAN, B. A rank-by-feature framework for

interactive exploration of multidimensional data. Information VisualizationInformation Visualization, v. 4, p. 96 – 113, 2005/06/20/ 2005. Dispon ́ıvel em: ⟨http://ivi.sagepub.com/content/4/2/96⟩.

COHEN, M. A.; GROSSBERG, S. Neural dynamics of brightness perception: Features, boundaries, diffusion, and resonance. Perception & Psychophysics, v. 36,

n. 5, p. 428–456. ISSN 1532-5962. Dispon ́ıvel em: ⟨http://dx.doi.org/10.3758/BF03207497⟩.

BOSKING, W. H. et al. Orientation Selectivity and the Arrangement of Horizontal Connections in Tree Shrew Striate Cortex. The Journal of Neuroscience, Society for Neuroscience, v. 17, n. 6, p. 2112–2127, mar. 1997. ISSN 1529-2401. Dispon ́ıvel em: ⟨http://www.jneurosci.org/content/17/6/2112.abstract⟩.

AZZI, J. a. C. B. et al. Precise visuotopic organization of the blind spot representation in primate V1. Journal of Neurophysiology, American Physiological Society, v. 113, n. 10, p. 3588–3599, jun. 2015. ISSN 1522-1598. Dispon ́ıvel em: ⟨http://dx.doi.org/10.1152/jn.00418.2014⟩.




DOI: https://doi.org/10.22456/2175-2745.86439

Copyright (c) 2019 Genildo Nonato Santos, José Gabriel Rodriguez Carneiro Gomes

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