Plasticity of Spectral Processing
Introduction
One of the major discoveries in sensory neuroscience in the last two decades has been the extent to which the stimulus selectivity of sensory cortical neurons and the functional organization of sensory cortices can be modified in adult animals as a consequence of altered patterns of input or of procedures that alter the significance of particular sensory inputs. Prior to recognition of this plasticity in adult sensory systems, it was generally assumed that these characteristics, while modifiable during development, were stable features of the adult brain. In parallel with the physiological studies of adult sensory plasticity, psychophysical studies of improvements in perceptual discriminative ability with practice also indicated that this “perceptual learning” might reflect plastic changes in sensory cortical processing mechanisms. In the case of the auditory system, physiological studies of adult plasticity have been focused on the frequency selectivity of individual neurons and the frequency organization of the primary auditory cortex (AI), changes which would be expected to have consequences for an organism's spectral processing ability. Such changes in performance have been demonstrated directly in psychophysical studies of various forms of spectral processing in humans and in non‐human animals. In this chapter, we will review the evidence from physiological and psychophysical studies for plasticity in spectral processing, the mechanisms underlying this plasticity, and its therapeutic applications. Before examining this evidence, the nature of the changes that constitute evidence of plasticity requires consideration.
The term “plasticity” is used in this chapter to refer to a range of experience‐ and injury‐induced changes in auditory processing at the psychophysical and neurophysiological levels. The most ubiquitous manifestation of behavioral plasticity is learning, and the bulk of the evidence to be reviewed in both domains is derived from studies of various forms of learning. Just as learning must be distinguished from changes in behavior attributable to other factors, neural plasticity (a phrase which is widely used but seldom clearly defined) must be distinguished from other forms of neural change.
In an early review of synaptic plasticity, Tsukahara (1981) defined plasticity as “any persistent change in the functional properties of single neurons or neuronal aggregates” (p. 351). The use of the term “functional properties” serves to indicate that not all changes in neuronal responsiveness constitute cases of plasticity. For example, the response of a cortical or subcortical neuron in any sensory system might change (increase or decrease in magnitude) as a function of an arousal state or of other variables having a general influence on brain excitability. Generalized changes in discharge rate of this sort without any change in the neuron's stimulus selectivity are not evidence of plasticity.
This definition is still rather too broad, however, as not all changes in stimulus selectivity constitute evidence for plasticity. For example, the frequency selectivity of auditory nerve (AN) fibers and of neurons at higher levels of the pathway is immediately changed by damage to outer hair cells in the region of the cochlea from which their input is derived (e.g., Dallos and Harris, 1978). These changes are a direct (or passive) reflection of the altered input to the AN fibers from the inner hair cells, and should not be regarded as instances of plasticity. In the same way, the reduced sensitivity and spectral processing ability of a listener with this form of hearing loss would not be regarded as an instance of plasticity. For a change in a neuron's response selectivity consequent on altered patterns of input to be considered a manifestation of plasticity, the change must not be explicable as a direct reflection of the altered input, but must involve some kind of active process that is initiated or triggered by the change in input. Such active processes might involve changes in intrinsic neuronal characteristics or in the number or efficacy of synapses, either at the level of the neuron from which recordings are made or in neuron(s) in the pathways over which its input is derived. Unfortunately, the distinction between active and passive processes, the extent to which they can be separated, and the determination of their involvement in particular changes, are less simple matters than might at first appear (see Calford, 2002). Nevertheless, the definition of physiological plasticity as changes in neuronal stimulus selectivity and/or in the associated functional organization of populations of neurons that are not explicable in terms of general changes in excitability or as passive reflections of changes in input provides an adequate basis for examination of the evidence on plastic changes in spectral processing mechanisms.
In the case of behavioral plasticity, it is also important, as previously noted, to distinguish plastic changes in sensory performance from those associated with changes in state or that are explicable as direct consequences of altered input. A more complex issue arises from the fact that perceptual discriminative ability is always measured experimentally using some sort of task on which the participants (human or animal) must be trained. Learning to perform that task—what is commonly termed “procedural learning”—is itself a form of plasticity and will lead to improved performance, but improved performance attributable to such learning is not a manifestation of the plasticity in sensory processing that is of interest here.
Section snippets
Overview
Neurophysiological studies of plasticity in spectral processing have examined the effects of a variety of manipulations of auditory input on the frequency selectivity of single neurons or multi‐neuron “clusters” at various levels of the auditory pathway. Although there is a large body of evidence on the occurrence of these effects at subcortical auditory centers and in different auditory cortical fields, this account will concentrate on data relating to primary auditory cortex (AI). Most of
Overview
Psychophysical studies of plasticity in spectral processing have examined the effects of practice on a variety of spectral discrimination and detection tasks. The following brief review of these reports will focus on data obtained from humans trained over multiple daily sessions (for shorter training periods, see e.g., Hawkey et al., 2004) in purely behavioral investigations. The evidence for spectral‐processing plasticity derives from demonstrations of training‐induced improvements on: (1)
Therapeutic Applications of Spectral Processing Plasticity
The plasticity of spectral processing is receiving increasing attention as a basis for the treatment of auditory disorders. There are several reports that various populations known to have poor frequency‐discrimination skills improve those abilities with training. For example, both children (Gengel, 1969) and adults (Turner and Nelson, 1982) with hearing impairment showed training‐induced learning on pure‐tone frequency discrimination. However, even after 25 to 40 hours of practice in some
Summary and Conclusions
In this chapter we have reviewed what is now a considerable body of evidence for plasticity in various perceptual aspects of human spectral processing and in the characteristics of the neural circuits in which such spectral processing occurs. It must be acknowledged, however, that there remains a substantial division between the human psychophysical and animal electrophysiological evidence for such plasticity. The bulk of the human psychophysical evidence has been derived from studies of
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