A fast BK-type KCa current acts as a postsynaptic modulator of temporal selectivity for communication signals

Abstract

Temporal patterns of spiking often convey behaviorally relevant information. Various synaptic mechanisms and intrinsic membrane properties can influence neuronal selectivity to temporal patterns of input. However, little is known about how synaptic mechanisms and intrinsic properties together determine the temporal selectivity of neuronal output. We tackled this question by recording from midbrain electrosensory neurons in mormyrid fish, in which the processing of temporal intervals between communication signals can be studied in a reduced in vitro preparation. Mormyrids communicate by varying interpulse intervals (IPIs) between electric pulses. Within the midbrain posterior exterolateral nucleus (ELp), the temporal patterns of afferent spike trains are filtered to establish single-neuron IPI tuning. We performed whole-cell recording from ELp neurons in a whole-brain preparation and examined the relationship between intrinsic excitability and IPI tuning. We found that spike frequency adaptation of ELp neurons was highly variable. Postsynaptic potentials (PSPs) of strongly adapting (phasic) neurons were more sharply tuned to IPIs than weakly adapting (tonic) neurons. Further, the synaptic filtering of IPIs by tonic neurons was more faithfully converted into variation in spiking output, particularly at short IPIs. Pharmacological manipulation under current- and voltage-clamp revealed that tonic firing is mediated by a fast, large-conductance Ca2+-activated K+ (KCa) current (BK) that speeds up action potential repolarization. These results suggest that BK currents can shape the temporal filtering of sensory inputs by modifying both synaptic responses and PSP-to-spike conversion. Slow SK-type KCa currents have previously been implicated in temporal processing. Thus, both fast and slow KCa currents can fine-tune temporal selectivity.

Short-Term Depression, Temporal Summation, and Onset Inhibition Shape Interval Tuning in Midbrain Neurons

“…We have included bandpass and bandstop neurons in population data to give an accurate representation of the diversity of responses of ELp neurons. However, we focused our analysis primarily on responses from high-and low-pass neurons since the majority of ELp neurons fall into one of these two categories George et al, 2011;Kohashi and Carlson, 2014). High-and low-pass neurons responded differently to shortinterval stimulation.…”

Behavioral and Single-Neuron Sensitivity to Millisecond Variations in Temporally Patterned Communication Signals

“…We classify these responses into six categories (i.e., highpass, low-pass, bandpass, bandstop, complex, and all-pass) based on quantitative measures of PSP amplitudes evoked by IPIs George et al, 2011;Baker and Carlson, 2014). However, even within these classes, responses are variable regarding parameters such as bandwidth, best IPI, and sensitivity to the direction of IPI change George et al, 2011;Ma et al, 2013;Baker and Carlson, 2014;Kohashi and Carlson, 2014). This wide variety of response properties arises from a combination of synaptic mechanisms, including temporal summation and short-term depression, as well as intrinsic membrane properties (George et al, 2011;Baker and Carlson, 2014;Kohashi and Carlson, 2014).…”

“…However, even within these classes, responses are variable regarding parameters such as bandwidth, best IPI, and sensitivity to the direction of IPI change George et al, 2011;Ma et al, 2013;Baker and Carlson, 2014;Kohashi and Carlson, 2014). This wide variety of response properties arises from a combination of synaptic mechanisms, including temporal summation and short-term depression, as well as intrinsic membrane properties (George et al, 2011;Baker and Carlson, 2014;Kohashi and Carlson, 2014). Pooling the classification probabilities of multiple neurons improved discrimination performance, with the amount of improvement depending on IPI tuning.…”

“…Previous studies have shown that circuits process these types of temporal patterns with central neurons that are selective for particular timing intervals (Rose and Capranica, 1983;Grothe, 1994;Fortune and Rose, 1997a;Edwards et al, 2002;Sakai et al, 2009;Pluta and Kawasaki, 2010;Goel and Buonomano, 2014). These response properties arise from synaptic mechanisms such as temporal summation and short-term synaptic plasticity (Fortune and Rose, 2000;George et al, 2011;Rose et al, 2011;Baker and Carlson, 2014), as well as from intrinsic postsynaptic membrane properties (Fortune and Rose, 1997b;Trussell, 1999;Fortune and Rose, 2003;Carlson and Kawasaki, 2006;Kohashi and Carlson, 2014). Although we have a good understanding of how circuits can establish single-neuron temporal selectivity, how a population of neurons with a diverse range of temporal filtering properties contributes to the detection and discrimination of natural timing patterns is unknown.…”

“…ELa sends its only output to the posterior exterolateral nucleus (ELp), where sensitivity to interpulse intervals arises . ELp neurons transform the temporal code for IPI into a population code, with neurons exhibiting a wide diversity of interval selectivity George et al, 2011;Baker et al, 2013;Ma et al, 2013;Baker and Carlson, 2014;Kohashi and Carlson, 2014).…”