Iontronic Nanopore Model for Artificial Neurons: The Requisites of Spiking

Abstract

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.

Synaptical Tunability of Multipore Nanofluidic Memristors

“…Previous work identified these characteristics on the ion transport dynamics at different potential and scan rates in single conical nanopores, emphasizing geometrical and surface effects together with charge storage processes. − However, the potential of fluidic nanopores for synaptic function has not been addressed. Here, progress with respect to previous nanofluidic devices − is found for a wide range of external signals, thus suggesting potential applications in chemical inductors and bioelectrochemical systems. ,− …”

Hysteresis in memristors produces conduction inductance and conduction capacitance effects

“…60,61 A variety of models with negative chemical inductors have also been described. 62 Recently, we have described in a general fashion the impedance properties exhibited by chemical inductors 22,62 including neurons 63,64 and memristors. 43,65 As shown in these works, from eqn ((1) and ( 2)), it follows that the inductive-like behaviour is an inherent property to the system (with no electromagnetic induction present), what justifies the name of the ''chemical inductor'', in reference to the dynamical response of the system and not to its specific physical or chemical mechanism.…”

Enhanced LED Performance by Ion Migration in Multiple Quantum Well Perovskite

“…The temporal dynamics of the variable p is delayed by the need of the ionic diffusion effect to activate the recombination process. This is described by the equation with characteristic time τ k τ k normald p normald t = p normalo normaln ( u ) − p This last equation produces the chemical inductor effect. ,, When the variable p comes to equilibrium with the applied voltage u it has the expression p normalo normaln ( u ) = 1 1 + normale ( u − U normalo normaln ) / V p Therefore, the p variable passes from 0 to 1, and therefore blocks the radiative current when the voltage is u < U on . V p is the steepness of the rise of p on .…”

“…This behavior is explained in Figure S4 of the SI. This is a typical feature of biological and artificial ionic channels, , and occurs when the built-in voltage that controls carrier drift in the electrical field has a similar value of the turn-on voltage of the gating variable p .…”

“…The model is inspired in recent papers that describe the chemical inductor feature in halide perovskites 36−39 and iontronic conducting channels. 40,41 A schematic representation of the model is presented in Figure 5a. The total current j tot as a function of voltage u is composed of a leakage nonradiative current j L = g L u with conductance g L , a radiative current j rad , and a capacitive current with capacitance…”