The Laminar and Temporal Structure of Stimulus Information in the Phase of Field Potentials of Auditory Cortex

Abstract

Recent studies have shown that the phase of low-frequency local field potentials (LFPs) in sensory cortices carries a significant amount of information about complex naturalistic stimuli, yet the laminar circuit mechanisms and the aspects of stimulus dynamics responsible for generating this phase information remain essentially unknown. Here we investigated these issues by means of an information theoretic analysis of LFPs and current source densities (CSDs) recorded with laminar multi-electrode arrays in the primary auditory area of anesthetized rats during complex acoustic stimulation (music and broadband 1/f stimuli). We found that most LFP phase information originated from discrete "CSD events" consisting of granular-superficial layer dipoles of short duration and large amplitude, which we hypothesize to be triggered by transient thalamocortical activation. These CSD events occurred at rates of 2-4 Hz during both stimulation with complex sounds and silence. During stimulation with complex sounds, these events reliably reset the LFP phases at specific times during the stimulation history. These facts suggest that the informativeness of LFP phase in rat auditory cortex is the result of transient, large-amplitude events, of the "evoked" or "driving" type, reflecting strong depolarization in thalamo-recipient layers of cortex. Finally, the CSD events were characterized by a small number of discrete types of infragranular activation. The extent to which infragranular regions were activated was stimulus dependent. These patterns of infragranular activations may reflect a categorical evaluation of stimulus episodes by the local circuit to determine whether to pass on stimulus information through the output layers.

A high-density, high-channel count, multiplexed μECoG array for auditory-cortex recordings

“…5). Prior current source density analyses find the largest local field potential responses to dynamic sounds reside within the thalamic input layers of auditory cortex (Szymanski et al 2011). Although we cannot determine the primary source of the ECoG STRF responses in the present study, the spatial resolution and organization of ECoG responses in the auditory cortex is consistent with that observed with local field potential responses to pure tones and dynamic sounds Ohl et al 2000).…”

Dynamic representation of spectral edges in guinea pig primary auditory cortex

“…Synaptic inhibition has been proposed to be involved in the neural changes produced by two-tone sequences and after hearing loss (Calford 2002;Calford et al 1993;Rajan, 2001;Rhode and Greenberg 1994;Shamma and Symmes 1985;Wang et al 2002). Although synaptic inhibition may contribute to shape the tuning of cortical neurons, its effects are, however, limited in frequency range (cortical excitation and inhibition are approximatively co-tuned) and time (i.e., up to 100 ms after the stimulus onset) (Tan et al 2004;Wehr andZador 2003, 2005). One notes, however, that the respective tuning of excitation and inhibition may be different at the subcortical than at the cortical level.…”

“…This procedure provided a rough estimate of the tonotopy and the amplitude of LFPs. Electrodes were placed at a depth where the (negative) amplitude of stimulus-induced LFPs was near maximal (region of the border between layer III and IV; Szymanski et al 2011).…”

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

“…Binned MI measured have already been applied to these sorts of problems [Belitski et al, 2010; Gross et al, 2013; Kayser et al, 2009; Schyns et al, 2011; Szymanski et al, 2011], and a number of other techniques have also been developed [Kempter et al, 2012; Lachaux et al, 1999; Voytek et al, 2013]. In such analyses, it is unclear how best to model or bin the data, because phase, the angle of the complex spectral signal, is a circular variable which “wraps around” and has no clear ranking or extremal values [Berens, 2009; Lee, 2010].…”