A Corticothalamic Circuit Model for Sound Identification in Complex Scenes

Abstract

The identification of the sound sources present in the environment is essential for the survival of many animals. However, these sounds are not presented in isolation, as natural scenes consist of a superposition of sounds originating from multiple sources. The identification of a source under these circumstances is a complex computational problem that is readily solved by most animals. We present a model of the thalamocortical circuit that performs level-invariant recognition of auditory objects in complex auditory scenes. The circuit identifies the objects present from a large dictionary of possible elements and operates reliably for real sound signals with multiple concurrently active sources. The key model assumption is that the activities of some cortical neurons encode the difference between the observed signal and an internal estimate. Reanalysis of awake auditory cortex recordings revealed neurons with patterns of activity corresponding to such an error signal.

Compensating Level-Dependent Frequency Representation in Auditory Cortex by Synaptic Integration of Corticocortical Input

“…The described hierarchical columnar integration of spectral and intensity information might allow the proper adjustment of auditory perceptual representations in situations of variable stimulus amplitudes [80,81]. Such functional cortical circuitry within auditory cortex might be fundamental for a constant representation of ecologically relevant auditory objects over a large range of intensities within noisy acoustic environments [10,11,82]. …”

Cortical Feedback Decorrelates Olfactory Bulb Output in Awake Mice

“…Early sensory areas receive massive top-down projections from the cortex, suggesting that this feedback plays a crucial function (Otazu and Leibold, 2011;Rao and Ballard, 1999). Visual, auditory, and somatosensory experiments have proposed several roles for cortical feedback, including sharpening of sensory representations, or relaying information pertaining to expectation, reward, attention, learning, and action (Gilbert and Li, 2013;Glickfeld et al, 2013;Harris and Mrsic-Flogel, 2013;Petreanu et al, 2012).…”

Reading Out Olfactory Receptors: Feedforward Circuits Detect Odors in Mixtures without Demixing

“…Based on a linear encoding model, Bayesian inference has been shown to work well in conditions where the typical mixture contains few components (Grabska-Barwinska et al, 2013). More generally, many algorithms for blind source separation and inference could be applied to such a generative model (Bell and Sejnowski, 1995, Otazu and Leibold, 2011, Tootoonian and Lengyel, 2014, Cuevas Rivera et al, 2015). Alternatively, supervised methods, like linear classifiers could be used for discriminative learning (Galán et al, 2006, Berens et al, 2012, Shen et al, 2013).…”