Iontronic Nanopore Model for Artificial Neurons: The Requisites of Spiking
Metadatos
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INVESTIGACIONMetadatos
Título
Iontronic Nanopore Model for Artificial Neurons: The Requisites of SpikingAutoría
Fecha de publicación
2023-10-02Editor
American Chemical SocietyISSN
1948-7185Cita bibliográfica
Bisquert, J. Iontronic Nanopore Model for Artificial Neurons: The Requisites of Spiking. J. Phys. Chem. Lett. 2023,14 (40), 9027-9033. DOI: 10.1021/acs.jpclett.3c02562Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
Brain-inspired neuromorphic computing is currently being investigated for effective artificial intelligence (AI) systems. The development of artificial neurons and synapses is imperative to creating efficient comput ... [+]
Brain-inspired neuromorphic computing is currently being investigated for effective artificial intelligence (AI) systems. The development of artificial neurons and synapses is imperative to creating efficient computational biomimetic networks. Here we propose the minimal configuration of an effective iontronic spiking neuron based on a conical nanofluidic pore ionic diode. The conductance is composed of a Boltzmann open channel probability and a blocking inactivation function, forming the structure of a memristor. The presence of a negative resistance and the combination of activation–deactivation dynamics cause a Hopf bifurcation. Using the characteristic frequencies of small perturbation impedance spectroscopy, we discuss the conditions of spiking, in which the system enters a limit cycle oscillation. We arrive at the conclusion that an excitable neuron-like system can be made with a single active channel instead of the more complex combination of multiple channels that occurs in the Hodgkin–Huxley neuron model. [-]
Publicado en
The Journal of Physical Chemistry Letters, 2023, vol. 14, no 40Entidad financiadora
Ministerio de Ciencia e Innovación
Código del proyecto o subvención
EUR2022-134045
Título del proyecto o subvención
Memristores de perovskita para redes de impulsos
Derechos de acceso
Copyright © American Chemical Society
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