An excitable electronic circuit as a sensory neuron model

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An electronic circuit device, inspired on the FitzHughNagumo model of neuronal excitability, was constructed and shown to operate with characteristics compatible with those of biological sensory neurons. The nonlinear dynamical model of the electronics quantitatively reproduces the experimental obse...

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Detalles Bibliográficos
Autor principal: Medeiros, B.N.S
Otros Autores: Minces, V., Mindlin, G.B, Copelli, M., Leite, J.R.R
Formato: Capítulo de libro
Lenguaje:Inglés
Publicado: World Scientific Publishing Co. Pte Ltd 2012
Acceso en línea:Registro en Scopus
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100 1 |a Medeiros, B.N.S. 
245 1 3 |a An excitable electronic circuit as a sensory neuron model 
260 |b World Scientific Publishing Co. Pte Ltd  |c 2012 
270 1 0 |m Medeiros, B.N.S.; Departamento de Física, Universidade Federal de Pernambuco, 50670-901 Recife, PE, Brazil 
506 |2 openaire  |e Política editorial 
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504 |a Ribeiro, T.L., Copelli, M., Deterministic excitable media under Poisson drive: Power law responses, spiral waves and dynamic range (2008) Phys. Rev. e, 77, p. 051911 
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520 3 |a An electronic circuit device, inspired on the FitzHughNagumo model of neuronal excitability, was constructed and shown to operate with characteristics compatible with those of biological sensory neurons. The nonlinear dynamical model of the electronics quantitatively reproduces the experimental observations on the circuit, including the Hopf bifurcation at the onset of tonic spiking. Moreover, we have implemented an analog noise generator as a source to study the variability of the spike trains. When the circuit is in the excitable regime, coherence resonance is observed. At sufficiently low noise intensity the spike trains have Poisson statistics, as in many biological neurons. The transfer function of the stochastic spike trains has a dynamic range of 6 dB, close to experimental values for real olfactory receptor neurons. © 2012 World Scientific Publishing Company.  |l eng 
536 |a Detalles de la financiación: Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq 
536 |a Detalles de la financiación: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior 
536 |a Detalles de la financiación: Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco 
536 |a Detalles de la financiación: National Institutes of Health 
536 |a Detalles de la financiación: B. N. S. Medeiros, M. Copelli and J. R. Rios Leite acknowledge financial support from Brazilian agencies CNPq, FACEPE, CAPES and special programs PRONEX, PRONEM and INCEMAQ. G. B. Mindlin acknowledges support from NIH. It is a pleasure to thank Hugo L. D. S. Cavalcante for enlightening discussions during the preparation of this work, as well as Marcos Nascimento for technical support. 
593 |a Departamento de Física, Universidade Federal de Pernambuco, 50670-901 Recife, PE, Brazil 
593 |a Department of Cognitive Neuroscience, University of California San Diego, San Diego, CA 92093-0515, United States 
593 |a Departamento de Física, FCEN, Ciudad Universitaria, Pab. I, 1428 Buenos Aires, Argentina 
690 1 0 |a COHERENCE RESONANCE 
690 1 0 |a DYNAMIC RANGE 
690 1 0 |a ELECTRONIC CIRCUIT 
690 1 0 |a EXCITABLE ELEMENT 
690 1 0 |a HOPF BIFURCATION 
690 1 0 |a NETWORKS (CIRCUITS) 
690 1 0 |a STOCHASTIC SYSTEMS 
690 1 0 |a TIMING CIRCUITS 
690 1 0 |a COHERENCE RESONANCE 
690 1 0 |a DYNAMIC RANGE 
690 1 0 |a EXCITABLE ELEMENT 
690 1 0 |a EXPERIMENTAL VALUES 
690 1 0 |a FITZHUGH-NAGUMO MODEL 
690 1 0 |a NONLINEAR DYNAMICAL MODELS 
690 1 0 |a OLFACTORY RECEPTOR NEURONS 
690 1 0 |a POISSON STATISTIC 
690 1 0 |a NEURONS 
700 1 |a Minces, V. 
700 1 |a Mindlin, G.B. 
700 1 |a Copelli, M. 
700 1 |a Leite, J.R.R. 
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