Implementing robust neuromodulation in neuromorphic circuits

Geometric methods; Neuromodulation; Neuromorphic engineering; Realization theory; Geometric method; High-dimensional; Input-output properties; Neuromorphic; Neuromorphic circuits; Computer Science Applications; Cognitive Neuroscience; Artificial Intelligence; Mathematics - Optimization and Control; Quantitative Biology - Neurons and Cognition

Abstract :

[en] We introduce a methodology to implement the physiological transition between distinct neuronal spiking modes in electronic circuits composed of resistors, capacitors and transistors. The result is a simple neuromorphic device organized by the same geometry and exhibiting the same input–output properties as high-dimensional electrophysiological neuron models. Preliminary experimental results highlight the robustness of the approach in real-world applications.

Disciplines :

Electrical & electronics engineering

Author, co-author :

Castaños, Fernando; Centro de Investigación y de Estudios Avanzados, Instituto Politécnico Nacional, Mexico

Franci, Alessio ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Brain-Inspired Computing ; Universidad National Autonoma de México, Facultad de Ciencias, Mexico

Language :

English

Title :

Implementing robust neuromodulation in neuromorphic circuits

Publication date :

12 April 2017

Journal title :

Neurocomputing

ISSN :

0925-2312

eISSN :

1872-8286

Publisher :

Elsevier B.V.

Volume :

233

Pages :

3 - 13

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://api.elsevier.com/content/article/PII:S0925231216313492?httpAccept=text/xml

Available on ORBi :

since 26 May 2023

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Bibliography

[1] Boahen, K., Neuromorphic microchips. Sci. Am. 292:5 (2005), 56–63.
[2] Liu, S.-C., Delbruck, T., Neuromorphic sensory systems. Curr. Opin. Neurobiol. 20:3 (2010), 288–295.
[3] Hynna, K.M., Boahen, K., Thermodynamically equivalent silicon models of voltage-dependent ion channels. Neural Comput. 19:2 (2007), 327–350.
[4] Wijekoon, J.H., Dudek, P., Compact silicon neuron circuit with spiking and bursting behaviour. Neural Netw. 21:2 (2008), 524–534.
[5] Indiveri, G., Linares-Barranco, B., Hamilton, T.J., Van Schaik, A., Etienne-Cummings, R., Delbruck, T., Liu, S.-C., Dudek, P., Häfliger, P., Renaud, S., et al. Neuromorphic silicon neuron circuits. Front. Neurosci., 5, 2011.
[6] Krahe, R., Gabbiani, F., Burst firing in sensory systems. Nat. Rev. Neurosci. 5:1 (2004), 13–23.
[7] Sherman, S.S., Guillery, R.W., Exploring the thalamus, 312, 2001, Oxford Univ Press.
[8] Lee, S.-H., Dan, Y., Neuromodulation of brain states. Neuron 76:1 (2012), 209–222.
[9] Franci, A., Drion, G., Seutin, V., Sepulchre, R., A balance equation determines a switch in neuronal excitability. PLoS Comput. Biol., 2013.
[10] Franci, A., Drion, G., Sepulchre, R., Modeling the modulation of neuronal bursting: a singularity theory approach. SIAM J. Appl. Dyn. Syst. 13:2 (2014), 798–829.
[11] Schaeffer, D.G., Golubitsky, M., Singularities and groups in bifurcation theory. Appl. Math. Sci., 51, 1985.
[12] Drion, G., Franci, A., Dethier, J., Sepulchre, R., Dynamic input conductances shape neuronal spiking. Eneuro, 2(2), 2015 (pp. ENEURO–0031).
[13] A. Franci, R. Sepulchre, Realization of nonlinear behaviors from organizing centers, in: Proceedings Conference on Decision and Control, Los Angeles, CA, Dec. 2014, pp. 56 – 61.
[14] Misra, J., Saha, I., Artificial neural networks in hardware: a survey of two decades of progress. Neurocomputing 74:December (2010), 239–255.
[15] Almási, A.-D., Woźniak, S., Cristea, V., Leblebici, Y., Engbersen, T., Review of advances in neural networks: neural design technology stack. Neurocomputing 174 A:January (2016), 31–41.
[16] Ngspice. [Online]. Available: 〈 http://ngspice.sourceforge.net/〉.
[17] Hodgkin, A.L., Huxley, A.F., A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117:4 (1952), 500–544.
[18] Drion, G., Massotte, L., Sepulchre, R., Seutin, V., How modeling can reconcile apparently discrepant experimental results: the case of pacemaking in dopaminergic neurons. PLoS Comput Biol., 7, 2011, 5 (pp. e1002050–e1002050).
[19] Prinz, A.A., Billimoria, C.P., Marder, E., Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J. Neurophysiol. 90:6 (2003), 3998–4015.
[20] Guckenheimer, J., Holmes, P., Nonlinear oscillations, dynamical systems, and bifurcations of vector fields, 42, 1983, Springer Science & Business Media.
[21] C.K.R.T. Jones, Geometric singular perturbation theory, in: Dynamical systems. Springer, 1995, pp. 44–118.
[22] Sedra, A.S., Smith, K.C., Microelectronic Circuits New York. 2004, Oxford University Press.
[23] Mäkelä, M.M., Neittaanmäki, P., Nonsmooth Optimization. 1992, World Scientific, Singapore.
[24] Clarke, F.H., Optimization and Nonsmooth Analisis. 1990, Society for Industrial and Applied Mathematics, New York.
[25] Bartolozzi, C., Indiverti, G., Selective attention implemented with dynamic synapses and integrate-and-fire neurons. Neurocomputing 69:October (2006), 1971–1976.
[26] Bartolozzi, C., Indiverti, G., Global scaling of synaptic efficacy: homeostasis in silicon synapses. Neurocomputing 72:January (2009), 726–731.