Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo

Abstract

Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo.

Emergence of band-pass filtering through adaptive spiking in the owl's cochlear nucleus

“…We provided an explanation how bandpass filtering can emerge via threshold. Even though we cannot be conclusive about the origin of threshold adaptation, whether sodium (Fontaine et al 2014;Hu et al 2009;Kuba et al 2010) or potassium (Goldberg et al 2008;Higgs and Spain 2011) channel dynamics underlie the phenomenon, we showed that a model representing sodium channels with appropriate biophysical inactivation properties can explain the data. Indeed, the shape of the threshold steady-state function was crucial in explaining the dependence of the filtering on input level.…”

“…The threshold (t) has a spike-triggered adaptive process (Benda et al 2010;Chacron et al 2007;Platkiewicz and Brette 2010), i.e., (t) increases by a fixed amount ␣ every time a spike is fired, i.e., (t) ¡ (t) ϩ ␣. The subthreshold dynamics of the threshold (t) is governed by the first-order differential equation with a steady-state function that depends on V m (t) (Farries et al 2010;Fontaine et al 2014;Higgs and Spain 2011;Platkiewicz and Brette 2010):…”

“…It has been shown, using a Hudgkin-Huxley formalism, that the threshold steady-state function can be expressed as a function of sodium channel properties (Fontaine et al 2014;Platkiewicz and Brette 2011):…”

“…Coupled with the intrinsic low-pass filter properties of the membrane, this phenomenon generates band-pass filtering. To explain the effects of the threshold adaptation to the V m , let us consider an adaptive process where the threshold V t (t) depends on the V m (t) through a first order differential equation (Farries et al 2010;Fontaine et al 2014;Higgs and Spain 2011;Platkiewicz and Brette 2010):…”

“…A model of threshold adaptation based on sodium channel kinetics previously developed was, therefore, used here to test whether threshold adaption can explain the in vitro and in vivo MTF properties. The spiking neuron model was a noisy leaky-integrate and fire neuron with an adaptive threshold that depended on the subthreshold V m (Farries et al 2010;Fontaine et al 2014;Higgs and Spain 2011;Platkiewicz and Brette 2010) and on the firing history (Benda et al 2010;Chacron et al 2007) (see MATERIALS AND METHODS). The threshold steady state function ϱ (V m ) converges to a fixed value at low V m (Higgs and Spain 2011;Platkiewicz and Brette 2010) and, therefore, can be simplified by a rectified piecewise linear function (Fig.…”