A Computational Model of Cellular Mechanisms of Temporal Coding in the Medial Geniculate Body (MGB)

Abstract

Acoustic stimuli are often represented in the early auditory pathway as patterns of neural activity synchronized to time-varying features. This phase-locking predominates until the level of the medial geniculate body (MGB), where previous studies have identified two main, largely segregated response types: Stimulus-synchronized responses faithfully preserve the temporal coding from its afferent inputs, and Non-synchronized responses, which are not phase locked to the inputs, represent changes in temporal modulation by a rate code. The cellular mechanisms underlying this transformation from phase-locked to rate code are not well understood. We use a computational model of a MGB thalamocortical neuron to test the hypothesis that these response classes arise from inferior colliculus (IC) excitatory afferents with divergent properties similar to those observed in brain slice studies. Large-conductance inputs exhibiting synaptic depression preserved input synchrony as short as 12.5 ms interclick intervals, while maintaining low firing rates and low-pass filtering responses. By contrast, small-conductance inputs with Mixed plasticity (depression of AMPA-receptor component and facilitation of NMDA-receptor component) desynchronized afferent inputs, generated a click-rate dependent increase in firing rate, and high-pass filtered the inputs. Synaptic inputs with facilitation often permitted band-pass synchrony along with band-pass rate tuning. These responses could be tuned by changes in membrane potential, strength of the NMDA component, and characteristics of synaptic plasticity. These results demonstrate how the same synchronized input spike trains from the inferior colliculus can be transformed into different representations of temporal modulation by divergent synaptic properties.

Neural spike-timing patterns vary with sound shape and periodicity in three auditory cortical fields

“…This indicates that response jitter can limit the range of sound shapes to which a neuron or cortical field may respond. In addition, models of neural spiking suggest that an increase in response duration and jitter should limit response synchronization and the capacity to temporally encode sound modulation frequency (Escabí and Read 2003;Lu and Wang 2004;Rabang and Bartlett 2011). Accordingly, we found the proportion of response power with synchronized spiking (i.e., the maximum temporal coding fraction) decreased almost twofold between A1 and cSRAF (e.g., Fig.…”

GABAergic inhibition shapes SAM responses in rat auditory thalamus

“…Studies in unanesthetized marmoset show that MGB neurons display increasingly complex response properties to modulated sounds and click stimuli (Bartlett and Wang, 2007, 2011, Rabang and Bartlett, 2011). Wallace et al (2000, 2002, 2007) examined responses to modulated pure-tone and click stimuli in these three auditory structures and found increased complexity in coding as one ascended the auditory neuraxis.…”

“…A rate code transformation for modulated signals has been extensively described for the MGB (Bartlett and Wang, 2007, 2011, Rabang and Bartlett, 2011). We previously reported that the MGB units showed a dose-dependent, enhanced sensitivity to GABA application relative to units in the IC (Cai et al, 2013).…”

“…Bandpass units, and the lower arm of Mixed units responded to GABA A R blockade with increased responses to SAM stimuli at or near the rate best modulation frequency (rBMF). The ability of GABA circuits to shape responses at higher modulation frequencies is an emergent property of MGB units, not observed at lower levels of the auditory pathway and may reflect activation of MGB NMDA receptors (Rabang and Bartlett, 2011, Rabang et al, 2012). Together, GABA A Rs exert selective rate control over selected f ms, generally without changing the units’ response type.…”

“…Based on the identification of high affinity GABA A Rs mediating tonic inhibitory current and enhanced GABA sensitivity in auditory thalamus (Richardson et al, 2011, Cai et al, 2013), and the findings and models by Bartlett, Wang and colleagues (Bartlett and Wang, 2007, 2011, Rabang and Bartlett, 2011, Rebang et al, 2012), the present study sought to characterize the role of GABAergic inhibition in shaping responses to SAM stimuli for commonly observed SAM-response-types seen for MGB neurons.…”