Modeling elucidates how refractory period can provide profound nonlinear gain control to graded potential neurons

Abstract

Refractory period (RP) plays a central role in neural signaling. Because it limits an excitable membrane's recovery time from a previous excitation, it can restrict information transmission. Classically, RP means the recovery time from an action potential (spike), and its impact to encoding has been mostly studied in spiking neurons. However, many sensory neurons do not communicate with spikes but convey information by graded potential changes. In these systems, RP can arise as an intrinsic property of their quantal micro/nanodomain sampling events, as recently revealed for quantum bumps (single photon responses) in microvillar photoreceptors. Whilst RP is directly unobservable and hard to measure, masked by the graded macroscopic response that integrates numerous quantal events, modeling can uncover its role in encoding. Here, we investigate computationally how RP can affect encoding of graded neural responses. Simulations in a simple stochastic process model for a fly photoreceptor elucidate how RP can profoundly contribute to nonlinear gain control to achieve a large dynamic range.

Investigating Refractoriness in Collision Perception Neuronal Model

“…Though comparative experiment, this estimation shows close match between the observed locust auditory receptor neurons spike trains, presenting the significance of refractoriness to artificial neural networks [24]. Though Song et al touched the combination of refractoriness and flies' photoreceptor, and elucidated what role RP plays in the encoding of graded neural responses, exploiting it for collision detection has not been considered [25].…”

Modeling biological refractory periods and synaptic depression in an artificial neuron

“…The original quantron model required consecutive presynaptic APs to occur at a constant frequency. The refractory period is a biological phenomenon known to affect the time interval between consecutive APs [40,41,43]. For example, emission is impossible during the absolute refractory period and is less likely during the relative refractory period [18,20].…”

“…Some have incorporated it in a perceptron [32], a discrete time network [33], an adaptive filter [34], a Hopfield network [35,36] and attractor networks [9,12,37]. Refractory periods have been included in phenomenological models [11,20,23,38,39] and approximated using stochastic processes [18,21,[40][41][42][43]. RATEN networks [44], self-organizing maps [27], enhanced Hopfield networks [45] and others [46,47] have used refractory periods properties to improve their models.…”

“…More realistic biologically inspired models like the FitzHugh-Nagumo [39,48,49] and Integrate-and-Fire [50][51][52] also considered refractory periods. Other works [12,15,19,31,36,41,43,[53][54][55][56] showed that some properties of synaptic depression and refractory periods significantly alter the signals sent by neurons. However, to our knowledge, no studies investigated the combination of both phenomena in order to boost the performance of an artificial neuron.…”