Research paperNeural coding strategies in auditory cortex
Introduction
In this review, I will address two related issues in cortical coding of acoustic signals, temporal firing patterns of auditory cortex neurons and their relationship with time-varying structures of sounds, based on a series of recent studies carried out in awake marmosets in our laboratory. I will use findings from these studies to illustrate two types of neural representations of acoustic information in auditory cortex: the isomorphic (or faithful) representation of the acoustic structures and the non-isomorphic transformation of acoustic features. While the experimental evidence cited in this article is largely based on the studies in marmosets, the neural coding strategies discussed here are likely applicable to the auditory cortex of other primate and mammalian species.
For the auditory system, time is an essential variable of sensory inputs. This is fundamentally different from other sensory systems (e.g., visual and somatosensory systems) where sensory inputs can be static. In audition, one can hardly define a “static” sound. Acoustic signals are like flowing water in a stream; they are constantly changing as a function of time. The same time axis is also used by neural discharges throughout the auditory pathway. A major distinction between the audition and vision is the temporal precision of sensory receptors and resulting peripheral representations. For the auditory system, the temporal precision is in the order of less than 1 ms (or greater than 1000 Hz), whereas it is in the order of ∼100 ms (or ∼10 Hz) for the visual system. Traditionally, auditory researchers have focused on how the time axis of acoustic signals is preserved by neural firings, for example, by analyzing “phase-locking” to the carrier frequency or the envelope of sounds. At the level of the auditory nerve, the two time axes (of acoustic signals and neural firings) are well matched (to the limit of the phase-locking). It has become clear after many years of research that the two time axes begin to diverge from each other at successive processing stations. The question addressed in this review is how a time-varying signal is mapped onto a spike train of auditory cortex neurons that is a function of time itself. Understanding this problem helps up better understand neural coding strategies in auditory cortex.
The neural representation of time-varying signals in auditory cortex is of special interest to our understanding of mechanisms underlying speech processing. Time-varying signals are fundamental components of communication sounds such as human speech and animal vocalizations, as well as musical sounds (Rosen, 1992, Wang, 2000). Low-frequency modulations are important for speech perception and melody recognition, while higher-frequency modulations produce other types of sensations such as pitch and roughness (Houtgast and Steeneken, 1973, Rosen, 1992). Both humans and animals are capable of perceiving the information contained in temporally modulated sounds across a wide range of time scales from a few millisecond to tens and hundreds of milliseconds. Neural representations of time-varying signals begin at the auditory periphery where auditory-nerve fibers faithfully represent fine structures of complex sounds in their temporal discharge patterns (Johnson, 1980, Joris and Yin, 1992, Palmer, 1982, Wang and Sachs, 1993). At subsequent processing stations along the ascending auditory pathway, the upper limit of the temporal representation of repetitive signals gradually decreases (e.g., cochlear nucleus: Blackburn and Sachs, 1989, Frisina et al., 1990, Wang and Sachs, 1994, Rhode and Greenberg, 1994; inferior colliculus: Langner and Schreiner, 1988, Batra et al., 1989, Muller-Preuss et al., 1994, Krishna and Semple, 2000, Liu et al., 2006; medial geniculate body: Creutzfeldt et al., 1980, de Ribaupierre et al., 1980, Rouiller et al., 1981, Preuss and Muller-Preuss, 1990, Bartlett and Wang, 2007; auditory cortex: Schreiner and Urbas, 1988, de Ribaupierre et al., 1972, Eggermont, 1991, Eggermont, 1994, Gaese and Ostwald, 1995, Bieser and Müller-Preuss, 1996, Lu and Wang, 2000, Lu et al., 2001b, Wallace et al., 2002, Liang et al., 2002, Phan and Recanzone, 2007), due to biophysical properties of neurons and temporal integration of converging inputs from one station to the next. By the time neural signals encoding acoustic information reach auditory cortex, temporal firing patterns alone are inadequate to represent the entire range of time-varying sounds. The mechanism by which the auditory cortex solves the problem of representing time-varying signals serves as a good example to illustrate a fundamental principle of cortical processing: the transformation of stimulus features into internal representations that are no long faithful replicas of their physical structures.
Section snippets
The responsiveness of auditory cortex
It has been well documented that neurons in auditory cortex of barbiturate- or ketamine-anesthetized animals generally display transient responses to acoustic stimulation and typically respond to a brief stimulus with one or few spikes (Phillips, 1985, Calford and Semple, 1995, Heil, 1997, Schnupp et al., 2001, DeWeese et al., 2003). For short tone stimuli (duration less than 100–200 ms), the number of spikes evoked by each stimulus usually does not increase with increasing stimulus duration (
Dual mechanisms for representing time-varying signals: functional mplications of synchronized and non-synchronized cortical responses
In the above section, we point out that a prominent feature of neural responses in awake auditory cortex is sustained firing. What functional roles can the sustained firing play? Our work in awake marmosets showed that the sustained firing in auditory cortex can serve to represent rapid time-varying signals (Lu et al., 2001b). It has long been noticed that neurons in the auditory cortex do not faithfully follow rapidly changing stimulus components (de Ribaupierre et al., 1972, Goldstein et al.,
Discrimination of acoustic transients: an example of firing rate coding
An often-raised concern on coding mechanisms based on firing rate is that firing rate is a relative measure, unlike discharge synchrony. In order to directly “read out” a stimulus parameter from firing rate (e.g., repetition rate), another representational dimension is needed, for example, a map or spatial distribution of neurons tuned to different repetition rates by their firing rates. On the other hand, the change in firing rate can encode the relative change in a stimulus parameter.
Differences between awake and anesthetized auditory cortex as revealed by responses to time-varying signals
Goldstein et al. (1959) showed that click-following rates of cortical evoked potentials were higher in unanesthetized cats than in anesthetized ones. We have studied responses of A1 neurons to click train stimuli in both awake marmosets (Lu et al., 2001b) and anesthetized cats (Lu and Wang, 2000) in our laboratory. There were several important differences between response properties observed in these two preparations. First, in contrast to A1 neurons in anesthetized cats, which responded
What is the role of spike timing in awake auditory cortex?
The issue of spike timing in auditory cortex has long been of interest to auditory researchers. It was shown that the precision of the first spike latency in A1 of anesthetized cats was comparable to (Phillips and Hall, 1990, Phillips, 1993) or even better than (Heil and Irvine, 1997) that of auditory never fibers. A recent study took it further by claiming “binary spiking” in A1 of anesthetized rats (DeWeese et al., 2003). Because these previous studies were conducted in anesthetized animals,
Functional implications of temporal-to-rate transformation in auditory cortex
The significant reduction in the upper limit of stimulus-synchronized discharges and the emergence of non-synchronized discharges in the auditory cortex, vs the auditory periphery, have important functional implications. First, it shows that considerable temporal-to-rate transformations have taken place by the time auditory signals reach the auditory cortex. The importance of “non-synchronized” neural responses is that they represent processed (or non-isomorphic) instead of preserved (or
Acknowledgements
The research in our laboratory has been supported by grants from NIH-NIDCD (DC03180 and DC005808) and by a US Presidential Early Career Award for Scientists and Engineers (to X. Wang). I thank Ashley Pistorio for her long-term support of our research (1998–2006) and for proof-reading this manuscript.
References (69)
- et al.
Cortical representations of pitch monkeys and of humans
Curr. Opin. Neurobiol.
(2006) - et al.
Tonal response patterns of primary auditory cortex neurons in alert cats
Brain Res.
(2002) - et al.
Cortical coding of repetitive acoustic pulses
Brain Res.
(1972) - et al.
Transmission delay of phase-locked cells in the medial geniculate body
Hear Res.
(1980) Rate and synchronization measures of periodicity coding in cat primary auditory cortex
Hearing Res.
(1991)Temporal modulation transfer functions for AM and FM stimuli in cat auditory cortex. Effects of carrier type, modulating waveform and intensity
Hearing Res.
(1994)- et al.
Encoding of amplitude modulation in the gerbil cochlear nucleus: I. A hierarchy of enhancement
Hearing Res.
(1990) - et al.
Neural encoding of amplitude modulation within the auditory midbrain of squirrel monkeys
Hearing Res.
(1994) Temporal response features of cat auditory cortex neurons contributing to sensitivity to tones delivered in the presence of continuous noise
Hearing Res.
(1985)Response profiles of auditory cortical neurons to tones and noise in behaving macaque monkeys
Hearing Res.
(2000)
Neural coding of repetitive clicks in the medial geniculate body of cat
Hearing Res.
Representation of amplitude modulation in the auditory cortex of the cat. II. Comparison between cortical fields
Hearing Res.
Phase-locked responses to pure tones in the primary auditory cortex
Hearing Res.
Cortical processing of temporal modulations
Speech Commun.
Changes of single unit activity in the cat’s auditory thalamus and cortex associated to different anesthetic conditions
Neurosci. Res.
Frequency representation in auditory cortex of the common marmoset (Callithrix jacchus jacchus)
J. Comput. Neurol.
Connections of the primary auditory cortex in the common marmoset, Callithrix jacchus jacchus
J. Comput. Neurol.
Discrimination of wideband noises modulated by a temporally asymmetric function
J. Acoust. Soc. Am.
Contrast tuning in auditory cortex
Science
Auditory cortical responses elicited in awake primates by random spectrum stimuli
J. Neurosci.
Neural representations of temporally–modulated signals in the auditory thalamus of awake primates
J. Neurophysiol.
Temporal coding of envelopes and their interaural delays in the inferior colliculus of the unanesthetized rabbit
J. Neurophysiol.
The neuronal representation of pitch in primate auditory cortex
Nature
Auditory responsive cortex in the squirrel monkey: neural responses to amplitude-modulated sounds
Exp. Brain Res.
Classification of unit types in the anteroventral cochlear nucleus: post-stimulus time histograms and regularity analysis
J. Neurophysiol.
Monaural inhibition in cat auditory cortex
J. Neurophysiol.
Thalamocortical transformation of responses to complex auditory stimuli
Exp. Brain Res.
Primary cortical representation of sounds by the coordination of action-potential timing
Nature
Optimizing sound features for cortical neurons
Science
Binary spiking in auditory cortex
J. Neurosci.
Firing rate and firing synchrony distinguish dynamic from steady state sound
Neuroreport
Classification of unit responses in the auditory cortex of the unanesthetized and unrestrained cat
J. Physiol.
Temporal coding of amplitude and frequency modulation in the rat auditory cortex
Eur. J. Neurosci.
Responses of the auditory cortex to repetitive acoustic stimuli
J. Acoust. Soc. Am.
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