The Influence of Natural Scene Dynamics on Auditory Cortical Activity

Abstract

The efficient cortical encoding of natural scenes is essential for guiding adaptive behavior. Because natural scenes and network activity in cortical circuits share similar temporal scales, it is necessary to understand how the temporal structure of natural scenes influences network dynamics in cortical circuits and spiking output. We examined the relationship between the structure of natural acoustic scenes and its impact on network activity [as indexed by local field potentials (LFPs)] and spiking responses in macaque primary auditory cortex. Natural auditory scenes led to a change in the power of the LFP in the 2-9 and 16 -30 Hz frequency ranges relative to the ongoing activity. In contrast, ongoing rhythmic activity in the 9 -16 Hz range was essentially unaffected by the natural scene. Phase coherence analysis showed that scene-related changes in LFP power were at least partially attributable to the locking of the LFP and spiking activity to the temporal structure in the scene, with locking extending up to 25 Hz for some scenes and cortical sites. Consistent with distributed place and temporal coding schemes, a key predictor of phase locking and power changes was the overlap between the spectral selectivity of a cortical site and the spectral structure of the scene. Finally, during the processing of natural acoustic scenes, spikes were locked to LFP phase at frequencies up to 30 Hz. These results are consistent with an idea that the cortical representation of natural scenes emerges from an interaction between network activity and stimulus dynamics.

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

“…Together, these results suggest that the informativeness of LFP and CSD phases about complex stimuli seen by both us and others (Kayser et al, 2009;Chandrasekaran et al, 2010) arises from phase alignments occurring at specific, discrete time points during stimulus presentation rather than from an internal clock providing a reliable phase signal at all times. The fact that the wideband analysis gives very similar results to those seen in the bandpass analysis not only confirms that filter ringing artifacts do not significantly affect our results, it also underlines the broadband, "impulse-like" nature of the phase-resetting events.…”

“…Previous work (Luo and Poeppel, 2007;Kayser et al, 2009;Chandrasekaran et al, 2010) reported that complex sounds are encoded by the instantaneous phase of low-frequency (Ͻ15 Hz) LFPs and MEG signals recorded form auditory cortex, because the phase of these signals reliably aligns at specific times during complex auditory stimulation. It is natural to hypothesize that the discrete, large-amplitude CSD events that we reported above may be related to the phase encoding reported in previous studies.…”

“…In the natural environment, animals are continuously exposed to acoustic scenes containing many sources of sound energy with a wide range of timescales and spectral properties (Rosen, 1992;Plack et al, 2005;Chandrasekaran et al, 2010). Unraveling how auditory systems successfully encode such complex stimuli is central for understanding auditory processing.…”

“…The phase of local field potential (LFP), magnetoencephalogram (MEG), and electroencephalogram (EEG) signals in sensory cortices reliably aligns at specific points during the presentation of complex stimuli (Luo and Poeppel, 2007;Kayser et al, 2009;Chandrasekaran et al, 2010;Schyns et al, 2011). As a consequence of phase reliability, spike-phase relationships carry substantial amounts of information about naturalistic stimuli above and beyond that carried by firing rates and even stabilize the information content of spike times against the detrimental effect of noise in the sensory environment (Montemurro et al, 2008;Kayser et al, 2009).…”

Neurons with Stereotyped and Rapid Responses Provide a Reference Frame for Relative Temporal Coding in Primate Auditory Cortex

“…However, using oscillations as intrinsic temporal reference naturally constrains the speed of computations by the cycle length of the respective oscillation. For slow oscillations such as the auditory theta rhythm (Luo and Poeppel, 2007;Kayser et al, 2009;Chandrasekaran et al, 2010) this would result in a relatively slow encoding process, which seems at odds with the fast speed at which sensory systems can detect or recognize natural stimuli (Thorpe et al, 1996;VanRullen et al, 2005;Murray et al, 2006). The high speed of perception seems better accommodated by intrinsic references that are immediately available following stimulus occurrence and which do not necessitate integration over longer time windows.…”