Ion Channel Stochasticity May Be Critical in Determining the Reliability and Precision of Spike Timing

Abstract

The firing reliability and precision of an isopotential membrane patch consisting of a realistically large number of ion channels is investigated using a stochastic Hodgkin-Huxley (HH) model. In sharp contrast to the deterministic HH model, the biophysically inspired stochastic model reproduces qualitatively the different reliability and precision characteristics of spike firing in response to DC and fluctuating current input in neocortical neurons, as reported by Mainen & Sejnowski (1995). For DC inputs, spike timing is highly unreliable; the reliability and precision are significantly increased for fluctuating current input. This behavior is critically determined by the relatively small number of excitable channels that are opened near threshold for spike firing rather than by the total number of channels that exist in the membrane patch. Channel fluctuations, together with the inherent bistability in the HH equations, give rise to three additional experimentally observed phenomena: subthreshold oscillations in the membrane voltage for DC input, "spontaneous" spikes for subthreshold inputs, and "missing" spikes for suprathreshold inputs. We suggest that the noise inherent in the operation of ion channels enables neurons to act as "smart" encoders. Slowly varying, uncorrelated inputs are coded with low reliability and accuracy and, hence, the information about such inputs is encoded almost exclusively by the spike rate. On the other hand, correlated presynaptic activity produces sharp fluctuations in the input to the postsynaptic cell, which are then encoded with high reliability and accuracy. In this case, information about the input exists in the exact timing of the spikes. We conclude that channel stochasticity should be considered in realistic models of neurons.

Capacitance fluctuations causing channel noise reduction in stochastic Hodgkin–Huxley systems

“…We extended a stochastic description of the HH model accounting for inherent channel noise by including gating current effects, which equivalently can be described in terms of capacitance fluctuations; see in equation (12). Our study revealed that while the deterministic HH model with gating charge effects does not differ dramatically from the original model for the standard set of parameters, the corresponding stochastic model does behave very differently for intermediateto-large membrane patch sizes.…”

Intrinsic Noise in Cultured Hippocampal Neurons: Experiment and Modeling

“…More recently, there has been a reinvigoration of interest in the field. Noise that is intrinsic or extrinsic to the neuron is now understood to have important effects on its information processing capabilities, changing action potential dynamics (Chow and White, 1996;White et al, 1998;Kretzberg et al, 2001b), enhancing signal detection (Wiesenfeld and Moss, 1995;Gammaitoni et al, 1998), altering the spike-timing reliability (Schneidman et al, 1998;van Rossum et al, 2003), and affecting the tuning properties of the cell (Hunter et al, 1998;Hunter and Milton, 2003;Schreiber et al, 2004).…”

Methods for short time series analysis of cell-based biosensor data

“…However, as mentioned above, trying to measure various dynamical quantities such as local expansion rates or any dynamical statistic from a deterministic model of the ISI requires much more data than are present in just ISI data. Therefore, we choose to analyze the entire time series, and make use of the fact that although noise is present and may affect the onset and timing variability of the spikes (Harris- Warrick et al, 1995;Guckenheimer et al, 1997), the analysis may also be critical in producing reliability in spike timing (Schneidman et al, 1998). Our hypothesis is that minute concentrations of toxins can affect the local statistics of the dynamics, and by local we mean those time intervals which are sufficiently short such that determinism dominates any stochastic behavior that is present.…”