Channel-noise-induced stochastic facilitation in an auditory brainstem neuron model

Abstract

Neuronal membrane potentials fluctuate stochastically due to conductance changes caused by random transitions between the open and closed states of ion channels. Although it has previously been shown that channel noise can nontrivially affect neuronal dynamics, it is unknown whether ion-channel noise is strong enough to act as a noise source for hypothesized noise-enhanced information processing in real neuronal systems, i.e., "stochastic facilitation". Here we demonstrate that biophysical models of channel noise can give rise to two kinds of recently discovered stochastic facilitation effects in a Hodgkin-Huxley-like model of auditory brainstem neurons. The first, known as slope-based stochastic resonance (SBSR), enables phasic neurons to emit action potentials that can encode the slope of inputs that vary slowly relative to key time constants in the model. The second, known as inverse stochastic resonance (ISR), occurs in tonically firing neurons when small levels of noise inhibit tonic firing and replace it with burstlike dynamics. Consistent with previous work, we conclude that channel noise can provide significant variability in firing dynamics, even for large numbers of channels. Moreover, our results show that possible associated computational benefits may occur due to channel noise in neurons of the auditory brainstem. This holds whether the firing dynamics in the model are phasic (SBSR can occur due to channel noise) or tonic (ISR can occur due to channel noise).

A review of methods for identifying stochastic resonance in simulations of single neuron models

“…It was often found that with close to c , spiking was terminated completely after one or a few spikes. Similar results were obtained when (a) the noise was conductance-based, as opposed to current-based, as is more appropriate for synaptic inputs, (b) current-based noise was introduced (turned-on) at a random chosen time point ), (c) channel noise was used instead of current noise (Schmerl and McDonnell 2013;Uzuntarla et al 2013), and (d) coloured noise instead of white Gaussian noise was used (Guo 2011). These variations established that the occurrence of minima in firing rate with increasing noise when is close to c is robust to significant model changes.…”

“…In the case of many types of neuron (and also cardiac cells) the rhythmic behaviour is called pacemaker activity and can be driven or intrinsic. The inhibiting effect of noise on such rhythmic activity was first explored experimentally in the squid axon (Paydarfar et al 2006) and theoretically in Hodgkin-Huxley (HH) or related models of ordinary differential equations Gutkin et al 2009;Guo 2011;Tuckwell and Jost 2012;Schmerl and McDonnell 2013;Uzuntarla et al 2013) (the specific contributions of these papers are explained below) and the partial differential equation HH model (Tuckwell andJost 2010, 2011). A brief summary will be given of the results for some of the inhibitory effects of white noise in the HH systems.…”

“…a focus on the fact that stochastic facilitation could potentially occur at different scales of organisation in the nervous system, ranging from molecular to large brain networks-here we only discussed single neurons, but examples of stochastic facilitation at the other scales is discussed in McDonnell and Ward (2011); . a focus on building realistic models with biophysically realistic noise sources, such as channel noise-some initial studies in this direction have been made already (Ashida and Kubo 2010;Gong et al 2012;Schmerl and McDonnell 2013); . studying these models with biologically appropriate signals;…”

“…. In the direction of studying stochastic facilitation arising from diverse biophysical noise sources, there have now been several reports of ion-channel noise (White et al 2000) as the noise source for stochastic facilitation effects in neuronal spiking (Ashida and Kubo 2010;Schmerl and McDonnell 2013). .…”

Optimising threshold levels for information transmission in binary threshold networks: Independent multiplicative noise on each threshold

“…Given that multiplicative noise has been modelled in the peripheral auditory system [26], we expect that our findings may be important for future work on more complex models in these areas and when applied to models in which recently discovered noise enhanced phenomena have been observed [40,41].…”