Encyclopedia of Computational Neuroscience

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| Editors: Dieter Jaeger, Ranu Jung

Auditory Thalamocortical Transformations

  • Kazuo ImaizumiEmail author
Living reference work entry

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DOI: https://doi.org/10.1007/978-1-4614-7320-6_102-6

Keywords

Auditory Cortex Inferior Colliculus Rate Code Modulation Transfer Function High Repetition Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Definition

Auditory thalamocortical transformations arise from the ascending neural processing of spontaneous activity as well as external sound-evoked activity from the auditory thalamus, the medial geniculate body, to the thalamorecipient layers of the auditory cortex in mammals.

Detailed Description

Thalamic and Cortical Organization

The Thalamus

The thalamus is the obligate neural station conveying ascending sensory information to the cortex (Sherman and Guillery 2006; Jones 2007). Each sensory modality, except for olfaction, is represented in defined nuclei of the thalamus through which sensory information must first be processed before eventually being transmitted to the respective sensory areas of the neocortex (Sherman and Guillery 2006; Jones 2007). In the auditory system, the medial geniculate body is the main auditory thalamic nucleus (Winer 1984; Jones 2007). Classically, the medial geniculate body has been divided into three main subdivisions, i.e., the ventral division, the dorsal division, and the medial division (Winer 1984; Imig and Morel 1985). Each of these thalamic nuclei can be identified on the basis of their cytoarchitecture, physiological properties, and connections (Huang and Winer 2000; de la Mothe et al. 2006; Lee and Winer 2008a). The thalamocortical transformation is constrained by the neuroanatomical organization of projections from each of these thalamic nuclei to each of the several areas of the auditory cortex (Winer et al. 2005; Winer and Lee 2007; Lee and Winer 2011b).

The Ventral Division of the Medial Geniculate Body

The ventral division of the medial geniculate body is the principal thalamic nucleus conveying ascending auditory information to the primary auditory cortex (Huang and Winer 2000; Smith et al. 2012). A subregion within the ventral division, the pars ovoidalis, is located in the medial part of the nucleus, bordering the dorsal division and medial division (Winer 1984; Jones 2007). Neurons in the ventral division are sharply tuned to sound frequencies (Imig and Morel 1985). These neurons are arranged in laminar rostrocaudal sheets, with their dendritic fields aligned in parallel along the sheet. The neurons in each sheet respond to similarly tuned frequencies (Imig and Morel 1985). These sheets are organized lateromedially in most species studied, with neurons in the lateral regions responding to lower frequencies of sound, while neurons in the medial regions respond to higher frequencies of sound (Imig and Morel 1985). Other physiological properties, such as bandwidth tuning and aurality, are found interdigitated among neurons across these isofrequency laminae (Ehret 1997). The main ascending projection to the ventral division originates from the central nucleus of the inferior colliculus (Calford 1983; Wenstrup 2005). These projections preserve the topographic organization connecting similarly tuned regions in the inferior colliculus to matched regions in the ventral division of the medial geniculate body (Wenstrup 2005). The main output of the ventral division is to the primary auditory cortex. These projections terminate primarily in layer 4 of the primary auditory cortex but also have branched projections to layer 6 (Huang and Winer 2000; Smith et al. 2012). Similar to the projection from the inferior colliculus, these projections are also topographically organized, such that the low-frequency regions of the ventral division project to the lower-frequency regions of the primary auditory cortex (Morel and Imig 1987; Morel et al. 1993). The ventral division also connects with other tonotopically organized auditory cortical areas, which vary in number in different species, e.g., five in the cat (Reale and Imig 1980) and three in the monkey (Kaas and Hackett 2000). These areas also receive topographically organized projections from the ventral division but mainly from nonoverlapping sectors (Morel and Imig 1987). Very few neurons in the ventral division send branched projections to multiple auditory areas, i.e., low-frequency neurons in the ventral division do not connect with multiple low-frequency regions in different tonotopically organized areas (Lee et al. 2004, 2011).

The Dorsal Division of the Medial Geniculate Body

The dorsal division of the medial geniculate body is the part of the auditory thalamus that connects to non-tonotopically organized areas of the auditory cortex (de la Mothe et al. 2006; Lee and Winer 2008a). This nucleus is composed of various subnuclei, including the dorsal superficial, deep dorsal, dorsocaudal, and ventrolateral nuclei (Winer et al. 2005; Lee and Winer 2008a). Neurons in the dorsal division are broadly tuned to frequencies, many with multi-peaked and complex receptive fields (Morel and Imig 1987; Winer et al. 2005). In contrast to the organization of the ventral division of the medial geniculate body, neurons in the dorsal division do not exhibit an oriented laminar pattern of organization (Winer 1984). The dendritic arborizations of neurons in the dorsal division are more isotropically organized. The subdivision of this nucleus is based primarily on cytoarchitectonic densities and connections with several non-tonotopic auditory areas (Lee 2013). Projections to the dorsal division originate primarily from the non-tonotopic area of the inferior colliculus, i.e., the dorsal cortex (Wenstrup 2005). The dorsal division nuclei project broadly to the non-tonotopic areas of the auditory cortex, which also include multimodal and limbic-related areas, whose relation to auditory processing in part is derived from the direct inputs received from thalamocortical sources (Lee and Winer 2008a, 2011a). Although a functional metric, such as tonotopy, appears to be absent in the nuclei of the dorsal division, their projections to the non-tonotopic areas of the auditory cortex are highly topographic, similar in extent to the topography of projections from the ventral division to the tonotopic regions of the auditory cortex (Lee and Winer 2005; Schreiner and Winer 2007). The termination pattern of the dorsal division projections to the non-tonotopic cortical areas, in particular, the secondary auditory cortical region, is similar to that of the ventral division projection to the primary auditory cortex, i.e., terminations primarily in layers 4 and 6 (Huang and Winer 2000). However, the synaptic terminals of the dorsal division projection to the secondary auditory cortex are slightly larger on average than the projections from the ventral division to the primary auditory cortex (Smith et al. 2012). Again, although the dorsal division projects broadly to several non-tonotopic auditory areas, very few neurons within the dorsal division send branched projections to multiple areas (Kishan et al. 2008; Lee and Winer 2008a; Lee et al. 2011).

The Medial Division of the Medial Geniculate Body

The medial division of the medial geniculate body projects widely across all auditory cortical areas, with a pattern of axonal termination that contrasts with the projections from the ventral division and the dorsal division of the medial geniculate body (Huang and Winer 2000; Jones 2007). Neurons in the medial division exhibit highly complex receptive fields, which often extend beyond purely auditory responses, with many neurons responding to stimuli from several modalities, i.e., visual and somatosensory (Bordi and LeDoux 1994). Neurons in the medial division differ from those in the ventral division and dorsal division, in that they display a wide range of sizes and dendritic arborization patterns (Bartlett and Smith 1999; Smith et al. 2007). The medial division contains the magnocellular neurons, which are the largest neurons in the medial geniculate body (Winer 1984). Like the dorsal division, the neurons in the medial division do not appear to be organized isotropically along any particular anatomical domain and are loosely packed compared with the other divisions (Winer 1984). The medial division receives its primary input from both the dorsal cortex and external cortex regions of the inferior colliculus, which also contain non-tonotopic and multimodal responsive neurons (Wenstrup 2005). Every area of the auditory cortex receives a projection from the medial division (Lee and Winer 2008a), but unlike the ventral division and the dorsal division, the laminar terminations of axons in these areas are primarily concentrated in layer 1 (Huang and Winer 2000). While axonal divergence from the medial geniculate body is low in general, the highest proportion of neurons projecting to multiple auditory cortical areas is found in the medial division; however, these comprise on average less than 2 % of neurons in the medial division of the medial geniculate body (Kishan et al. 2008, 2011; Lee et al. 2011).

Inhibitory Circuits in the Thalamus

The thalamic reticular nucleus, although not a specific constituent of the medial geniculate body, is intimately intertwined with the operations of both the thalamus and cortex and thus is an essential structural component of the thalamocortical transformation (Winer and Larue 1996; Crabtree et al. 1998; Pinault 2004; Sherman and Guillery 2006). The thalamic reticular nucleus is composed of inhibitory GABAergic neurons that form a shell surrounding the thalamus, roughly located along the lateral border of the thalamus and extending rostrocaudally along nearly its entire length (Pinault 2004; Lam and Sherman 2005, 2007, 2010). In general, each nucleus of the thalamus innervates a specific sector of the thalamic reticular nucleus and receives reciprocal topographic inhibitory feedback projections from that region of the thalamic reticular nucleus (Lam and Sherman 2005). In addition, feedback projections from layer 6 of the neocortex en route to the thalamus branch to innervate the thalamic reticular nucleus (Lam and Sherman 2010), which establishes an extended thalamocorticothalamic inhibitory feedback loop (Pinault 2004). The thalamic reticular nucleus and the inferior colliculus are the main sources of inhibition in the medial geniculate body of rodents, which contain very few local inhibitory neurons (Winer and Larue 1996). In humans, local inhibitory neurons in the medial geniculate body comprise ~20 % of the total number of neurons, which is accompanied by a proportional reduction in inhibitory projections from the thalamic reticular nucleus, compared with rodents and other species, which have fewer than 1 % of local inhibitory neurons in the medial geniculate body (Winer and Larue 1996).

The Cerebral Cortex

The cerebral cortex is phylogenetically the newest structure in the mammalian brain, responsible for the higher-order processing of sensory, motor, and limbic information (Kaas 2008). Broadly, the cerebral cortex is composed of regions of “gray” matter and “white” matter, the former residing near the outer cortical surface and containing all of the neuronal cell bodies and the latter residing beneath the gray matter and composed of the axonal fiber tracts of afferent and efferent projections (Nieuwenhuys 2013). Although regional variations exist, the gray matter of the cerebral cortex has a laminar organization divided into cytoarchitectonically distinguishable layers, such that neuronal cell bodies are situated in structural and functional groups relative to their location along the pial to white matter axis (Mountcastle 1997). These layers of neuronal cell bodies have specific afferent and efferent connections with other cortical regions and with subcortical structures, in particular the thalamus (Sherman and Guillery 2006). In total, there are six classically defined layers of the cortex (Mountcastle 1997). Of these, layer 4 of the cerebral cortex is the main recipient layer for sensory information ascending from the primary sensory thalamic nuclei (Sherman and Guillery 2006). In addition, layer 6 receives branched projection from these primary sensory thalamic nuclei (Huang and Winer 2000; Smith et al. 2012; Lee and Imaizumi 2013), and layer 1 is the recipient of thalamocortical inputs from nonspecific thalamic nuclei, e.g., the medial division of the medial geniculate body (Huang and Winer 2000; Jones 2007). In addition, layer 6 is the source of neurons that send feedback projections to the thalamic nucleus that provides its main thalamocortical input in layers 4 and 6, which establishes a thalamocorticothalamic feedback loop (Sherman and Guillery 2006). Cortical layer 5 is the source of feedforward nonreciprocal thalamocortical projections, which serve as a conduit for communication between cortical areas via a corticothalamocortical route (Sherman and Guillery 2006).

The surface of the cerebral cortex is regionally specified into distinct functional areas that are involved in processing sensory and motor information (Kaas 2008). The boundaries of these cortical areas, including those involved with audition, are broadly defined based on their cytoarchitectural organization, connections with other structures, and physiological responses of constituent neurons, although precise borders and definitions for many cortical areas in different organisms still remain elusive (Kaas and Hackett 2000; Hackett 2011). Although all mammals have cortical regions devoted to the processing of auditory information, the number of auditory cortical areas varies widely among different species (Lee and Winer 2008b, 2011b). Despite this heterogeneity, all mammals studied thus far have an identifiable auditory cortical region that receives direct input from the ventral division of the medial geniculate body (Lee and Winer 2011b). This primary auditory cortical area is also defined on the basis of a clearly identifiable tonotopic map, which reflects the frequency segregation established in the cochlea and propagated along the auditory pathway (Ehret 1997).

Although the primary auditory cortical area is the most conserved across species, constellations of other cortical regions devoted to the processing of sound exist among the cortices of different mammalian species (Kaas 2008). However, their physiological and anatomical organization is generally less well understood relative to that of the primary auditory cortex. Nevertheless, these other areas can be broadly grouped into distinct categories: tonotopic, non-tonotopic, multimodal, and limbic related (Lee and Winer 2011a). The tonotopic regions, like the primary auditory cortex, contain identifiable maps of frequency across their surface, generally with frequency reversals at their borders, and receive strong projections from the ventral division of the medial geniculate body (Reale and Imig 1980; Hackett et al. 1998; Hackett et al. 2011). The non-tonotopic areas contain disordered representation of frequency and generally receive more prominent inputs from the dorsal division of the medial geniculate body (Schreiner and Cynader 1984; Smith et al. 2012). Multimodal and limbic areas reside at the limits of the classical auditory areas and receive inputs from multimodal and limbic thalamic nuclei and areas and have complex responses reflective of these convergent inputs (Bowman and Olson 1988; Clarey and Irvine 1990; Clascá et al. 1997; de la Mothe et al. 2006). As discussed above, features of the thalamocortical inputs to these other areas resemble those to the primary auditory cortex (Huang and Winer 2000; Smith et al. 2012), which could serve as an anatomical basis for common thalamocortical transformations across the expanse of auditory cortical areas (Lee and Sherman 2008, 2011).

Receptive Fields and the Thalamocortical Transformation

Spectral Receptive Field

An important physiological parameter in the auditory thalamocortical transformation is the spectral receptive field. The spectral receptive field is, in general, measured based on a frequency-threshold tuning curve (response to sound level as a function of sound frequency). A common measure is the Q factor by which characteristic frequency is divided by a linear measure of bandwidth at a given sound level above threshold (e.g., Q10; Q value at 10 dB above threshold) (Imaizumi et al. 2004). Because the Q value is a normalized measure, the larger the Q value, the more sharply tuned are the neurons. Another measure is the square root transformation:
$$ \sqrt{F2}-\sqrt{F1} $$
where F2 and F1 are the highest and lowest edges of bandwidth at a given sound level (Rouiller et al. 1981; Calford 1983). Unlike the Q value, the smaller the square root transformation value, the more sharply tuned are the neurons.

The auditory thalamocortical transformation is mediated by the excitatory neurotransmitter, glutamate, from the medial geniculate body to the auditory cortex. Therefore, only the excitatory receptive field is transformed in thalamocortical projections. A problem describing spectral receptive fields in the thalamocortical transformation is the availability of data utilizing the same recording method (e.g., local field potentials, single- or multiunit extracellular recording, whole-cell recording, etc.) under similar recording conditions (e.g., anesthesia: isoflurane, ketamine, or pentobarbital; depth of anesthesia; awake states; restricted or freely moving) in the same animal species. This creates some controversy and readers should use caution for the following section.

Functional organization of the spectral receptive field has been well appreciated in the auditory cortices of several species. The most well-known example is the primary auditory cortex of the mustached bat. A large area, called the DSCF (Doppler-shifted constant frequency) area, of the primary auditory cortex is devoted to a particular frequency range (60.6–62.3 kHz) for their prey-hunting behavior (Suga 1994). Neurons in the DSCF area are extremely sharply tuned to characteristic frequency (Q50 values range from ~10 to 500 or higher) (Suga and Manabe 1982; Suga et al. 1997). Neurons in the anterior and posterior parts of the DSCF area are more broadly tuned. Similarly organized clusters of sharply or broadly tuned neurons are also found in the auditory cortex of carnivores and primates (Recanzone et al. 1999; Cheung et al. 2001; Read et al. 2001; Imaizumi et al. 2004; Philibert et al. 2005; Imaizumi and Schreiner 2007). In particular, the cat primary auditory cortex has an interesting functional organization of spectral receptive fields. Sharply or broadly tuned neurons are clustered alternatively along the dorsoventral axis only in the mid-frequency range (5–20 kHz) (Imaizumi and Schreiner 2007). Unlike those animal species, functional organization of spectral receptive fields in the rodent auditory cortex is not clear (Polley et al. 2007). Whereas rich information of functional organization of spectral receptive fields is available in the auditory cortex, only limited information is available in the medial geniculate body. Neurons in the ventral division are, in general, more sharply tuned than in the dorsal division (Rouiller et al. 1981; Calford 1983; Edeline et al. 1999). However, no clear spatial organization is available due to the deep location in the brain.

In general, it is believed that spectral receptive fields become broader through the thalamocortical transformation (Kaur et al. 2004, 2005). However, more recent studies using in vivo whole-cell recordings combined with pharmacological manipulation (blocking cortical input by muscimol and SCH50911) have revealed that thalamocortical input is not as sharp as was thought (Liu et al. 2007). These examples above are based on studies in rodents. Spectral receptive fields, however, vary widely across species. In the human auditory cortex, neurons are extremely sharply tuned (Bitterman et al. 2008), which suggests that spectral receptive fields may become sharper through the thalamocortical transformation. How does this opposite trend occur in thalamocortical transformation? Clear evidence is available in the awake mustached bat. Based on Q10, Q30, and Q50 values, DSCF neurons in the primary auditory cortex are more sharply tuned than those in the medial geniculate body (Suga et al. 1997). Similar evidence is also available in the awake marmoset (Bartlett et al. 2011). Thus, the sharpening of spectral receptive fields through the thalamocortical transformation may relate to behavioral and/or ethological functions.

For thalamocortical transformation of spectral receptive fields, limited experimental cases are available using in vivo single-unit recordings in awake guinea pigs (Creutzfeldt et al. 1980) and anesthetized cats (Miller et al. 2001, 2002). For secure functional transformation, medial geniculate body and auditory cortex neurons require an alignment of less than 1/3 octave difference in best frequency (sound frequency evoked the best response in a neuron at a given sound level) (Miller et al. 2001). To fully activate a neuron in the auditory cortex, synaptic convergence from 20 to 25 neurons in the medial geniculate body is required (Miller et al. 2001). Using a ripple noise stimulus and computational analytical approach of reverse correlation technique, Miller et al. (2002) estimated spectral modulation rates through thalamocortical transformation. Both thalamic and cortical neurons show lower spectral modulation rates. Whereas cortical neurons have significantly lower spectral modulation rates than thalamic neurons based on the best spectral modulation rates, the overall spectral filter properties between thalamic and cortical neurons are similar.

Overall, spectral receptive (modulation) fields in the thalamocortical transformation are not simple. Depending on animals and the behavioral significance of sound frequency, spectral receptive fields become broader or sharper through the thalamocortical transformation.

Temporal Receptive Field

An important function of the central auditory system is to decode species-specific communications and human speech sounds. Periodic modulations are ubiquitous temporal features of species-specific communications and human speech sounds (Rosen 1992; Joris et al. 2004). Measuring repetition-rate transfer functions or modulation transfer functions captures response characterization to assess information for a temporal range that corresponds to periodicities in communication sounds (Eggermont 2001; Joris et al. 2004). Two commonly used measures to characterize repetition-rate transfer functions are firing rate and vector strength (VS). Firing rate estimates overall response magnitude in a particular time window. Vector strength estimates spike-timing precision to a particular phase of repetition or modulation stimulus:
$$ VS=\frac{\sqrt{{\left(\Sigma \cos \theta \right)}^2+{\left(\Sigma \sin \theta \right)}^2}}{n} $$
$$ \theta =2\pi \frac{t}{T} $$
where n is the total number of spikes, t is time of spike occurrence, and T is the interstimulus interval (Goldberg and Brown 1969). Significance of synchronization to the stimulus is examined by a Rayleigh test, >13.8 (p < 0.001) (Mardia 1972). Because vector strength does not incorporate response strength, it may give rise to high vector strength values when the response strength is low. To overcome a shortcoming of the VS measure, phase-projected vector strength (VS pp ) is used (Yin et al. 2011; Niwa et al. 2012). VS pp compares the mean phase angle for each trial with the mean phase angle of all trials and penalizes single-trial vector strength values if they are not in phase with the global response. VS pp is computed on a trial-by-trial basis as follows:
$$ V{S}_{pp}=V{S}_t \cos \left({\phi}_t-{\phi}_c\right) $$
where VS pp is the phase-projected vector strength per trial, VS t is the vector strength per trial, and ϕ t and ϕ c are the trial-by-trial and mean phase angle in radians, calculated for each stimulus condition, and
$$ \phi = arctan2\frac{{\displaystyle {\sum}_{i=1}^n \sin \theta i}}{{\displaystyle {\sum}_{i=1}^n \cos \theta i}} $$
where n is the number of spikes per trial (for ϕ t ) or across all trials (for ϕ c ) and arctan2 is a modified version of the arctangent that determines the correct quadrant of the output based on the signs of the sine and cosine inputs. Whereas vector strength value ranges from 1 (all spikes occur at the same stimulus phase) to 0 (spikes occur randomly), phase-projected vector strength value ranges from 1 (all spikes in phase with population mean phase) to −1 (all spikes 180° out of phase with population mean phase) with 0 corresponding to randomly occurring spikes (Yin et al. 2011; Niwa et al. 2012). Another way to overcome a shortcoming of vector strength is the product of two measures (vector strength and firing rate), i.e., phase-locked rate or synchronized rate (Eggermont 1998b; Joris et al. 2004; Imaizumi et al. 2011). The synchronized rate measure incorporates both timing and response strength measures.

Two coding schemes for temporal receptive fields have been proposed: precise spike timing estimated by vector strength codes slow repetition rates (or modulation frequencies), while firing rate codes faster repetition rates (De Ribaupierre et al. 1972; Bieser and Muller-Preuss 1996; Schulze and Langner 1997; Lu and Wang 2000; Lu et al. 2001; Joris et al. 2004). However, more recent studies have proposed different coding schemes, as will be described later.

A problem describing temporal receptive fields in the thalamocortical transformation is also the availability of data utilizing the same stimulus (e.g., nature of stimulus: click or white noise; modulation carrier: pure tone or noise; modulation depth; and modulation shape: rectangular or sinusoidal), stimulus presentation method (free field or sealed earphones), and recording method (single- or multiunit extracellular recording) under similar recording conditions (e.g., anesthesia: isoflurane, ketamine, or pentobarbital; depth of anesthesia; awake states: restricted, freely moving, or engaged behavior) in the same animal species. In many cases, the analytical criteria are also different. These create difficulty for direct comparisons and some discrepancy. Thus, the readers should treat the following section with caution.

The vast majority of studies regarding temporal receptive fields have focused on the primary auditory cortex. However, functional magnetic resonance imaging or positron emission tomography in humans and macaques suggested that the superior temporal plane is specific to human speech or macaque species-specific calls over nonspecific calls or other sounds (Belin et al. 2000; Poremba et al. 2004; Petkov et al. 2008). These fields are located anterior to the primary core fields and may correspond to the rostral field in primates. Reversible lesion experiments in cats also show that the anterior auditory field is specific to temporal pattern discrimination (Lomber and Malhotra 2008). Thus, the rostral field in primates and the anterior auditory field in carnivores (and rodents) may be more important for temporal pattern processing.

Rodent Auditory Cortex and Thalamus

Under anesthesia of pentobarbital, neurons in the rat primary auditory cortex, in general, show a low-pass filter property of repetition-rate transfer functions by rate coding (firing rate) or band-pass filter property by temporal coding (vector strength) (Kilgard and Merzenich 1999; Chang et al. 2005). Ter-Mikaelian et al. (2007) examined the effects of anesthesia (a combination of pentobarbital and ketamine) and awake (head-fixed) states in the gerbil primary auditory cortex. While anesthesia certainly affects modulation transfer functions in individual neurons, overall, the population response seems to be similar between anesthesia and awake states (Ter-Mikaelian et al. 2007). Clear evidence of an effect of anesthesia is available in the rat primary auditory cortex (Rennaker et al. 2007). Ketamine anesthesia significantly decreased cutoff repetition rates (higher border of repetition-rate transfer function) to <20 Hz from 80 to 90 Hz in the awake state. A more recent study using awake state (head-fixed) rats examined repetition-rate transfer function using click trains in the primary auditory cortex and the anterior auditory field (Ma et al. 2013). Both core fields show high best repetition rates (up to 32–64 Hz) and cutoff repetition rates (up to 256 Hz), although neurons in the anterior auditory field prefer significantly higher repetition rates than those in the primary auditory cortex. These best and cutoff repetition rates in the awake rat auditory cortex are, in general, higher than those in the anesthetized rat (Kilgard and Merzenich 1999; Chang et al. 2005). Attention or engaged behaviors also alter temporal receptive fields. By presenting at 15 Hz repetition rate (with various carrier frequencies) paired with electrical stimulation of the nucleus basalis for 20–25 days, neurons in the rat primary auditory cortex are capable of following higher repetition rates by rate coding than those in control (Kilgard and Merzenich 1998). Training in a sound maze in which rats use sound source location for food rewards based on auditory cues (noise repetition rates increased with decreasing distance between the rat and target location) also enhanced temporal receptive fields (Bao et al. 2004). Compared to studies in the rodent auditory cortex, a small number of studies are available in rodent thalamus. In awake guinea pigs, thalamic neurons show more robust responses to higher modulation frequencies than cortical neurons (Creutzfeldt et al. 1980).

Cat Auditory Cortex and Thalamus

The cat auditory cortex has been a focus of studies of temporal pattern processing. In particular, the primary auditory cortex and the anterior auditory field are readily identifiable based on their location relative to sulcal patterns (Knight 1977; Imaizumi et al. 2004; Lee et al. 2004) and are often compared in temporal pattern processing (Schreiner and Urbas 1988; Eggermont 2000; Joris et al. 2004). Under anesthesia, neurons in the anterior auditory field prefer higher modulation frequencies (and repetition rates) than those in the primary auditory cortex (Schreiner and Urbas 1988; Eggermont 1998b). Anesthesia also reduces temporal receptive fields by half in neurons of the cat primary auditory cortex (Goldstein et al. 1959). In the cat anterior auditory field under ketamine anesthesia, Imaizumi et al. (2010) proposed different coding schemes using a combination of an in vivo high-resolution cortical mapping technique with information theory. Because this study was made using multiunit recordings, they computed discriminability of six different low repetition rates (1–30 Hz) (Imaizumi et al. 2010). Unlike the traditional coding scheme (precise spike timing codes low repetition rates), inter-spike intervals can carry much more information than timing and rate codes: some multiunits carried an information value >2 bits that is close to a maximum of ~2.58 bits (=log2(6)). Furthermore, spatial distribution of normalized firing rate to six different repetition rates differs across the stimuli, which provides a potential coding scheme in the view of an ideal observer. These results suggest concurrent coding schemes of temporal pattern processing by inter-spike intervals, firing rate, and a map (Imaizumi et al. 2010). Using behaviorally trained cats, Dong et al. (2011) compared neurometric (neural responses to six repetition rates from 12.5 to 200 Hz by in vivo single-unit recordings) with psychometric (Go/No-Go behavioral responses to the same six repetition rates) functions. Their recordings were focused on the relatively low-frequency locations <16 kHz of the primary auditory cortex. Prevalence is found of more synchronized units in the behaviorally engaged primary auditory cortex than in an anesthetized one (Lu and Wang 2000; Dong et al. 2011). However, rate coding by non-synchronized units correlates well with psychometric functions.

Compared to the studies in the cat auditory cortex, a small number of studies are available in the cat thalamus. In nitrous oxide-anesthetized cats, approximately half of the thalamic units showed precisely time-locked responses to click trains (Rouiller et al. 1981). Half of these units had cutoff repetition rates (higher border of repetition-rate transfer function) up to 100 Hz. Local field potentials in the auditory cortex record subthreshold responses potentially from thalamocortical fibers (and corticocortical fibers), thus indicating thalamic responses. Both best modulation frequencies and cutoff modulation frequencies are generally higher in local field potentials than unit recordings in the primary auditory cortex and anterior auditory field (Eggermont 1998b). Under ketamine anesthesia in the primary auditory cortex and the ventral division of the medial geniculate body of cats, Miller et al. (2002) conducted in vivo simultaneous single-unit recordings from thalamus and cortical neurons. They found that temporal modulation transfer functions are significantly deteriorated through thalamocortical projections.

Primate Auditory Cortex and Thalamus

A majority of studies of temporal receptive fields in primates are conducted in the awake state. In the awake squirrel monkey, neurons in the primary auditory cortex and the rostral field show both temporal and rate codes to amplitude modulation frequencies up to 64 or 128 Hz (Bieser and Muller-Preuss 1996). Neurons in the primary auditory cortex showed higher best modulation frequencies than those in the rostral field. In the awake macaque, neurons in the primary auditory cortex have higher best modulation frequencies by both temporal (means are 13 and 4.8 Hz) and rate (means are 45 and 19 Hz) codes and higher vector strength than those in the rostral field (Malone et al. 2010; Scott et al. 2011). These examples from the awake squirrel monkey and macaque have suggested the opposite trend of temporal receptive fields in the core auditory fields to rodents and cats (Schreiner and Urbas 1988; Eggermont 1998b; Joris et al. 2004) despite the similar cortical locations (relative to the primary auditory cortex) of the anterior auditory field in rodents and cats and the rostral field in the primates. In the awake primate auditory cortex, neurons may carry both temporal and rate codes for lower and higher repetition rates (or modulation frequencies) (Bieser and Muller-Preuss 1996; Lu et al. 2001; Liang et al. 2002; Malone et al. 2007; Yin et al. 2011). However, a proportion of synchronized (using vector strength or similar measures) and non-synchronized (using firing rate) neurons are different among the studies. These discrepancies may be caused by the stimulus, the range of repetition rates, and/or analytical criterion (Yin et al. 2011). There is an interesting proposal of low to mid range of repetition rates (10–45 Hz) corresponding to flutter perception by two different populations of neurons in the awake marmoset auditory cortex. One population of neurons increases firing rate with increasing repetition rates, while the other population decreases firing rate with increasing repetition rates (Bendor and Wang 2007). All examples reviewed above are based on studies of primates passively listening to stimuli in awake state. However, active engagement of behaviors (discriminating modulated sounds, 2.5–500 Hz, from unmodulated sounds) improves both temporal and rate codes in single neurons of the macaque primary auditory cortex (Niwa et al. 2012).

Compared to the studies in primate auditory cortex, a small number of studies are available in primate thalamus. In the awake squirrel monkey, neurons in the thalamus show a similar tendency of temporal and rate coding to best modulation frequencies up to 128 Hz (Preuss and Muller-Preuss 1990). In the awake marmoset, neurons in the ventral and the anterodorsal divisions of the medial geniculate body show a mixture of temporal and rate coding, while neurons in the posterodorsal division show a dominant tendency of rate coding (Bartlett and Wang 2011). These examples suggest that separation of temporal and rate coding for low and high repetition rates may be created within the auditory cortex (Bartlett and Wang 2007). However, other data suggest that separation from temporal to rate coding for low to high repetition rates may be completed through thalamocortical transformation (Malone et al. 2007; Yin et al. 2011). Thalamic neurons are capable of following higher repetition rates by both temporal and rate coding than cortical neurons in the awake marmoset (Bartlett and Wang 2007).

Overall, temporal receptive fields change through the thalamocortical transformation. In general, thalamic neurons are more capable of following higher repetition rates (modulation frequencies) than cortical neurons. However, neurons in the auditory cortex may employ different coding schemes to follow different ranges of repetition rates (this is not restricted only to high repetition rates but also low to mid repetition rates) either through the thalamocortical transformation or at the cortical level.

Latency

First-spike latency (hereafter latency) is another important physiological parameter (Eggermont 2001). However, because only the experimenter knows the stimulus onset (the brain and neurons do not know it), relative latencies might be a good candidate for neural coding of temporal patterns (Eggermont 1998b; Schreiner and Raggio 1996; Lu and Wang 2000; Liang et al. 2002; Ter-Mikaelian et al. 2007; Imaizumi et al. 2011), vocalizations (Wang et al. 1995; Nagarajan et al. 2002), sound localization (Eggermont 1998a; Furukawa et al. 2000), and auditory scene (Dear et al. 1993). Latency, in general, decreases with increasing sound level (Heil 1997). However, neurons in the primary auditory cortex of the little brown bat show shorter latencies to lower sound level than higher sound level (Sullivan 1982b). Furthermore, when the two sounds (higher and lower sound levels) are presented by a behaviorally relevant delay between pulse and echo, latencies are facilitated and become shorter for the echolocating behavior (Sullivan 1982a). In general, neurons in the anterior auditory field have shorter latencies than those in the primary auditory cortex across many different species (Schreiner and Urbas 1986, 1988; Linden et al. 2003; Rutkowski et al. 2003; Imaizumi et al. 2004; Bizley et al. 2005). However, neurons in the primate primary auditory cortex show shorter latencies than those in the rostral field (Scott et al. 2011), which is related to the fact that neurons in the primate primary auditory cortex follow higher repetition rates than those in the rostral field (Bieser and Muller-Preuss 1996; Malone et al. 2010).

Neurons in the ventral division of the medial geniculate body show shorter latencies than those in other divisions (Rouiller et al. 1981; Calford 1983; Edeline et al. 1999). However, more recent studies in the mouse and guinea pig thalamus show that neurons in the medial division have shorter latencies than those in the ventral division of the medial geniculate body (Anderson et al. 2006; Anderson and Linden 2011). This evidence strongly supports the shorter latencies in neurons of the anterior auditory field than those in the primary auditory cortex (Schreiner and Urbas 1986, 1988; Linden et al. 2003; Rutkowski et al. 2003; Imaizumi et al. 2004; Bizley et al. 2005) because the anterior auditory field receives input not only from the ventral division but also from the medial division. Latency through thalamocortical transformation can be estimated by in vivo simultaneous single-unit recordings and cross-correlation analysis. A maximum peak in the correlogram is shifted to the expected travel and synaptic delay (e.g., a few milliseconds) (Miller et al. 2001). Thus, it is generally believed that latency in thalamocortical transformation is inherited from the thalamus to cortex. However, a recent study using in vivo whole-cell recordings combined with a pharmacological application (to silence cortical activity by a mixture of muscimol and SCH50911) in the rat primary auditory cortex unfolds a different story: difference in latency between the thalamus and cortex is generated by synaptic integration time by excitation and inhibition through corticocortical interactions (Zhou et al. 2012).

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Authors and Affiliations

  1. 1.Department of Comparative Biomedical Sciences, School of Veterinary MedicineLouisiana State UniversityBaton RougeUSA