Rapid Neural Adaptation to Sound Level Statistics

Abstract

Auditory neurons must represent accurately a wide range of sound levels using firing rates that vary over a far narrower range of levels. Recently, we demonstrated that this "dynamic range problem" is lessened by neural adaptation, whereby neurons adjust their inputoutput functions for sound level according to the prevailing distribution of levels. These adjustments in input-output functions increase the accuracy with which levels around those occurring most commonly are coded by the neural population. Here, we examine how quickly this adaptation occurs. We recorded from single neurons in the auditory midbrain during a stimulus that switched repeatedly between two distributions of sound levels differing in mean level. The high-resolution analysis afforded by this stimulus showed that a prominent component of the adaptation occurs rapidly, with an average time constant across neurons of 160 ms after an increase in mean level, much faster than our previous experiments were able to assess. This time course appears to be independent of both the timescale over which sound levels varied and that over which sound level distributions varied, but is related to neural characteristic frequency. We find that adaptation to an increase in mean level occurs more rapidly than to a decrease. Finally, we observe an additional, slow adaptation in some neurons, which occurs over a timescale of tens of seconds. Our findings provide constraints in the search for mechanisms underlying adaptation to sound level. They also have functional implications for the role of adaptation in the representation of natural sounds.

Context Effects in the Discriminability of Spatial Cues

“…Our approach addresses several issues that remain unresolved. First, single-neuron recordings suggest that neurons adapt almost fully to novel sound distributions in less than 1s (Dean et al 2008). In contrast, the study of Kashino (1998) used prolonged adaptation (60s followed by 5s top-ups).…”

“…Here, psychophysical data were collected with time constants closer to those suggested from neurophysiological recordings (approx. 1s; Dean et al 2008). Second, both Kashino (1998) and Getzmann (2004) used a single spatial location for the preceding sound in any given block, which may not be fully representative of the dynamic changes that arise in realistic auditory scenes.…”

Emergence of band-pass filtering through adaptive spiking in the owl's cochlear nucleus

“…Filtering properties are dynamic in that they adapt to stimulus statistics such as mean or variance in neurons of the visual (Fairhall et al 2001) and auditory (Dean et al 2008;Lesica and Grothe 2008a;Nagel and Doupe 2006;Wen et al 2009) systems. In the auditory system, changes in stimulus intensity can evoke changes in both spectral (preferred acoustic frequency) and temporal (preferred modulation frequency) processing (Frisina 2001;Krishna and Semple 2000;Lesica and Grothe 2008a;Nagel and Doupe 2006).…”

“…In the auditory system, changes in stimulus intensity can evoke changes in both spectral (preferred acoustic frequency) and temporal (preferred modulation frequency) processing (Frisina 2001;Krishna and Semple 2000;Lesica and Grothe 2008a;Nagel and Doupe 2006). Neural adaptation to input statistics has important consequences regarding coding efficiency (Dean et al 2008; Lesica and Grothe 2008b;Rodrí-guez et al 2010a) and information transfer (Chacron et al 2007).…”

Sparseness, Antisparseness and Anything in Between: The Operating Point of a Neuron Determines Its Computational Repertoire

“…The neuron then discovers nothing about the regularities in its environment precisely because it has adapted its output statistics in a manner that largely ignores its input statistics. One of the major features of sensory neurons, however, is that they adapt to their input statistics, changing their thresholds and gains dynamically and rapidly as their input statistics change (Barlow and Mollon, 1982;Shapley and Enroth-Cugell, 1984;Meister and Berry, 1999;Kvale and Schreiner, 2004;Zaghloul et al, 2005;Bonin et al, 2006;Dean et al, 2008).…”