Sparse codes of harmonic natural sounds and their modulatory interactions

Abstract

Sparse coding and its related theories have been successful to explain various response properties of early stages of sensory information processing such as primary visual cortex and peripheral auditory system, which suggests that the emergence of such properties results from adaptation of the nerve system to natural stimuli. The present study continues this line of research in a higher stage of auditory processing, focusing on harmonic structures that are often found in behaviourally important natural sound like animal vocalization. It has been physiologically shown that monkey primary auditory cortices (A1) have neurons with response properties capturing such harmonic structures: their response and modulation peaks are often found at frequencies that are harmonically related to each other. We hypothesize that such relations emerge from sparse coding of harmonic natural sounds. Our simulation shows that similar harmonic relations emerge from frequency-domain sparse codes of harmonic sounds, namely, piano performance and human speech. Moreover, the modulatory behaviours can be explained by competitive interactions of model neurons that capture partially common harmonic structures.

The Opponent Channel Population Code of Sound Location Is an Efficient Representation of Natural Binaural Sounds

“…More recently, some aspects of the topographic structure of the auditory cortex were shown to emerge from statistics of speech sounds by Terashima and Okada [ 28 ]. Terashima and colleagues have also studied the connection between sparse codes of natural sound spectra and harmonic relationships revealed by receptive fields of macaque A1 neurons [ 29 ]. Maximizing coding efficiency by learning sparse codes has also lead to emergence of signal representations useful in spatial hearing tasks.…”

Generalization of learning by synchronous waves: from perceptual organization to invariant organization

“…Because of this, neuronal coding in the cortex may be considered sparse (Vinje and Gallant 2000;Guyonneau et al 2004). Studies on the effects of naturalistic stimulation have shown that activity in the visual field from well beyond the classically defined surround further increases the sparseness of neuronal coding in V1, by further refining the stimulus selectivity of neurons (Vinje and Gallant 2000;Terashima and Hosoya 2009;Haider et al 2010). The spatial pattern of activity in V1 neurons in the upper layers has an excitatory-centre/inhibitory-surround structure (Blakemore and Tobin 1972;Nelson and Frost 1978;Allman et al 1985).…”

Simultaneous Estimation of Nongaussian Components and Their Correlation Structure

“…Previously, sparse coding (Olshausen & Field, 1996) and ICA-related-methods have been applied to audio data to investigate the basic properties of cells in the primary auditory cortex (A1) (Klein, König, & Körding, 2003;Terashima & Hosoya, 2009;Terashima, Hosoya, Tani, Ichinohe, & Okada, 2013). More recently, topographic ICA (TICA) (Hyvärinen et al, 2001) was employed to analyze spectrogram data, and feature maps were learned which are similar to the tonotopic maps in A1 (Terashima & Okada, 2012).…”