Processing of complex stimuli and natural scenes in the auditory cortex
Introduction
Research into signal coding in primary auditory cortex (A1) has enjoyed renewed popularity in recent years. All modern methodologies, including new slice preparations for studying thalamo–cortical interactions [1], intracellular and extracellular single neuron recordings 2., 3.••, 4.••, evoked electrical and magnetic fields (EEG and MEG) 5., 6.•, and functional magnetic resonance imaging (fMRI) 7., 8., are being applied to this system in a variety of model animals, including rodents, bats, cats, primates, and humans.
Despite this accumulation of information, the nature of the representation of complex sounds in A1 remains the subject of heated debate. This is not due to a lack of data, but rather because of the fact that the data are often contradictory. Whereas some studies emphasize a relatively simple cortical representation, other studies show a large degree of complexity in the neuronal responses.
Here, I review evidence that indicates that simplicity and complexity co-exist in A1. Evidence with converging implications suggests that the co-existence of simplicity and complexity in A1 is due to its participation in processes that are often implicitly assigned to higher brain areas. In particular, I review evidence that suggests the involvement of auditory cortex in processes such as the on-line extraction of statistical regularities from the auditory scene and the organization of the auditory scene in terms of auditory objects.
Section snippets
Precise and imprecise temporal coding
One of the complexities in auditory cortex is the interplay among multiple time scales that determine the neural responses. For example, cortical neurons respond to some auditory events with stereotypical response bursts at a fixed latency (‘locking’). The variance of the latency of such bursts might be similar to that of peripheral neurons. However, the same neurons may show sluggish responses to other features of the sounds.
Temporal coding is usually tested using repetitive stimuli, such as
Feature detection or something else?
It seems that depending upon the circumstances, a cortical neuron can choose to be sluggish or precise, linear or non-linear. Thus, the feature sensitivity of a neuron, as determined, for example, by its STRF, cannot be used as an invariant essential characterization of its responses. The multiple time scales at which cortical neurons process sounds provide another argument against a pure role in feature-detection for auditory cortex neurons [25]. Feature detectors are expected to be sensitive
Adaptation and plasticity
The plastic capabilities of auditory cortex have been studied in several preparations on many time scales. Significant changes in electrical and magnetic brain potentials (EEG and MEG) occur during training for the performance of tasks such as the perception of virtual pitch [5] and fine pitch discrimination [37]. Even simple exposure to different auditory environments can substantially change auditory cortical organization and responses: thus, raising rats in an enriched environment increases
Auditory scene analysis in auditory cortex
Several recent studies, using a variety of techniques, suggest a role for auditory cortex in segregation and grouping of sound components. For example, at the brain potential level, Dyson and Alain [50] reported that the amplitude of the mid-latency potentials increased when a harmonic was mistuned, potentially creating two auditory objects instead of one. Furthermore, the enhanced amplitude was correlated with an increased likelihood of reporting two concurrent auditory objects. Krumbholz et al
Speculative synthesis and conclusions
Most of the interesting auditory features might already be extracted from the incoming sounds by the level of the IC, which should therefore be considered as the auditory analog of the primary visual cortex (V1). The role of auditory cortex is to organize these features into auditory objects (Figure 1). To do that, auditory cortex has to use temporal and spectral context at several time scales. The large adaptive and plastic capacity of auditory cortex is used to tune the neural circuits to the
References and recommended reading
Papers of particular interest, published within the annual period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Acknowledgements
Supported by grants from the Israeli Science Foundation (ISF), the German-Israeli Foundation (GIF) and the Volkswagenstiftung.
References (63)
- et al.
Auditory thalamocortical synaptic transmission in vitro
J Neurophysiol
(2002) - et al.
Intracortical pathways determine breadth of subthreshold frequency receptive fields in primary auditory cortex
J Neurophysiol
(2004) - et al.
Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex
Nature
(2003) - et al.
Topography and synaptic shaping of direction selectivity in primary auditory cortex
Nature
(2003) - et al.
Music and learning-induced cortical plasticity
Ann N Y Acad Sci
(2003) - et al.
Morphology of Heschl’s gyrus reflects enhanced activation in the auditory cortex of musicians
Nat Neurosci
(2002) - et al.
Amplitude and frequency-modulated stimuli activate common regions of human auditory cortex
Cereb Cortex
(2003) - et al.
Neural correlates of sensory and decision processes in auditory object identification
Nat Neurosci
(2004) - et al.
Neural processing of amplitude-modulated sounds
Physiol Rev
(2004) - et al.
Temporal and rate representations of time-varying signals in the auditory cortex of awake primates
Nat Neurosci
(2001)
Information content of auditory cortical responses to time-varying acoustic stimuli
J Neurophysiol
The spectro-temporal receptive field. A functional characteristic of auditory neurons
Biol Cybern
Spectro-temporal response field characterization with dynamic ripples in ferret primary auditory cortex
J Neurophysiol
Spectrotemporal structure of receptive fields in areas AI and AAF of mouse auditory cortex
J Neurophysiol
Optimizing sound features for cortical neurons
Science
Spectrotemporal receptive fields in the lemniscal auditory thalamus and cortex
J Neurophysiol
Neural model for physiological responses to frequency and amplitude transitions uncovers topographical order in the auditory cortex
J Neurophysiol
Response timing constraints on the cortical representation of sound time structure
J Acoust Soc Am
Responses to linear and logarithmic frequency-modulated sweeps in ferret primary auditory cortex
Eur J Neurosci
Sensitivity of neurons in cat primary auditory cortex to tones and frequency-modulated stimuli. I: Effects of variation of stimulus parameters
Hear Res
Dynamics of precise spike timing in primary auditory cortex
J Neurosci
The representation of peripheral neural activity in the middle-latency evoked field of primary auditory cortex in humans(1)
Hear Res
Auditory brainstem responses with optimized chirp signals compensating basilar-membrane dispersion
J Acoust Soc Am
Auditory edge detection: a neural model for physiological and psychoacoustical responses to amplitude transients
J Neurophysiol
Primary auditory cortex of cats: feature detection or something else?
Biol Cybern
Prediction of the responses of auditory neurons in the midbrain of the grass frog based on the spectro-temporal receptive field
Hear Res
Linearity of cortical receptive fields measured with natural sounds
J Neurosci
Linear processing of spatial cues in primary auditory cortex
Nature
Acoustic factors govern developmental sharpening of spatial tuning in the auditory cortex
Nat Neurosci
Contrast tuning in auditory cortex
Science
Responses of neurons in cat primary auditory cortex to bird chirps: effects of temporal and spectral context
J Neurosci
Cited by (178)
Inference of network connectivity from temporally binned spike trains
2024, Journal of Neuroscience MethodsA cortical circuit mechanism for structural knowledge-based flexible sensorimotor decision-making
2021, NeuronCitation Excerpt :The category boundary estimate is a modality-specific variable in the auditory domain, and therefore it is likely that this variable is encoded in the ACx. The ACx in rodents plays important roles in hearing-related cognition by not only encoding bottom-up sensory features (Nelken, 2004) but also by representing task-related information (Francis et al., 2018; Fritz et al., 2005; Polley et al., 2006; Runyan et al., 2017; Tsunada et al., 2016; Xin et al., 2019) involving top-down feedback modulation from higher cortical areas (Jaramillo and Zador, 2011; Schneider et al., 2014; Zhong et al., 2019). We thus examined how ACx neurons encode important variables related to inference-based auditory categorization by imaging population neuronal activity in the ACx using in vivo two-photon microscopy during task performance.