From Receptive Field Dynamics to the Rate of ... - Semantic Scholar

Neuroembryol Aging 2004–05;3:230–238 DOI: 10.1159/000096800

Published online: November 3, 2006

From Receptive Field Dynamics to the Rate of Transmitted Information: Some Facets of the Thalamocortical Auditory System Chloé Huetz Jean-Marc Edeline Laboratoire de Neurobiologie de l’Apprentissage, de la Mémoire et de la Communication, Unité Mixte de Recherche, Centre National de la Recherche Scientifique, et Université Paris-Sud, Orsay, France

Key Words Information theory Learning-induced plasticity Sleep Spike timing precision Thalamocortical auditory system Vocalization

Abstract In this article, we first evaluate the literature describing reorganizations of auditory cortex topography after behavioral training. We then review the studies showing that receptive fields of auditory thalamocortical neurons express large dynamics in unanesthetized animals. During the time course of different behavioral training protocols, the frequency tuning curves of thalamocortical neurons can be selectively modified to code for the learned importance of acoustic stimuli. In other circumstances, when the vigilance state shifts from waking to sleep, the functional properties of thalamic and cortical neurons exhibit drastic modifications. Finally, we point out new lines of research. First, investigations describing the responses of neurons to communication signals (e.g. species-specific vocalizations) are important because they reveal how the thalamocortical auditory system processes biologically relevant sounds. Second, we suggest that the spike timing precision can largely increase the amount of information transmitted in the thalamocortical auditory system. This urges for more systematic studies in which the temporal organization of spike trains will be considered at presentation of natural stimuli. Copyright © 2005 S. Karger AG, Basel

© 2005 S. Karger AG, Basel 1661–3406/05/0034–0230$22.00/0 Fax +41 61 306 12 34 E-Mail [email protected] www.karger.com

Accessible online at: www.karger.com/nba

Introduction

In the auditory modality, as well as in others, the foundations of sensory physiology have been established in anesthetized preparations that have been judged more stable than the awake ones. It is out of the scope of this article to review the myriad of studies that have described the functional properties of auditory neurons under general anesthesia. This step is necessary and essential to unravel the fundamental principles that allow auditory neurons to extract information from the external word and to code this information for the subsequent stages of processing. From the initial description of response selectivity to pure tones [1] to the most recent descriptions of the functional properties of neurons [reviewed in ref. 2– 4], impressive progress has been made in our understanding of the thalamocortical auditory system. However, we should keep in mind that anesthetics are drugs. Therefore, it is absolutely necessary to examine to what extent the functional properties discovered under general anesthesia indeed operate in the waking brain. In the auditory modality, initial observations suggested that the responses of thalamocortical neurons can exhibit a large dynamic range in unanesthetized animals. To cite a few examples, Hubel et al. [5] described ‘attention units’ in the auditory cortex; Evans and Whitfield [6] noted that the response patterns can be profoundly modified from time to time. The effects produced by shifts in vigilance states were also reported [7] in the auditory cortex of un-

Jean-Marc Edeline NAMC, UMR 8620, Bat 446 Université Paris-Sud FR–91405 Orsay (France) Tel. +33 1 6915 4972, Fax +33 1 6915 7726, E-Mail [email protected]

anesthetized macaque monkeys ‘the most dramatic shifts in excitability occur when the animal drifts through periods of wakefulness and sleep’. It should be noted that being able to express large dynamics in the functional properties of auditory neurons does not mean that they exhibit unstable properties. As we will see below, reliable responses can be collected in all states of vigilance, and evoked responses are as reliable before than after a learning situation. Nonetheless, because each neuron probably receives a large diversity of inputs in the awake animal, the response dynamics observed in awake animals is incomparable with the one observed in anesthetized ones.

Topographic maps are often viewed as fundamental to sensory processing [8, 9]. For this reason, the modifications in topographic maps reported after behavioral training had a large impact on the field of sensory physiology. However, the very first studies describing map reorganization after behavioral training did not attract much attention, probably because maps were quantified with metabolic activity (2DG), an indirect method to evaluate neuronal activity (note: functional magnetic resonance imaging, which has been used extensively in humans, is also quite an indirect method). In a set of remarkable studies, Gonzalez-Lima and Scheich [10–12] described the consequences of repeated pairing between a frequency-modulated tone (used as conditioned stimulus, CS) and an aversive stimulation of the reticular formation (used as unconditoned stimulus, US). Importantly increased labeling confined to the frequency band of the CS was noted after pairing, whereas five control groups developed less pronounced labeling. These studies also pointed out that increased labeling occurred at the thalamic and inferior colliculus levels, suggesting an involvement of subthalamic nuclei to cortical plasticity. Undoubtedly, the findings which have the most profoundly modified our way to consider adult sensory systems are those reported by Jenkins et al. [13] and Recanzone et al. [14, 15]. In the mid 80s, these authors initiated a set of outstanding experiments which demonstrated that adult sensory cortices exhibit large-scale plasticity in varied situations, ranging from denervation, deafferentation, intracortical microstimulation, nursing behavior to behavioral training. In the studies describing the consequences of behavioral training, adult monkeys were submitted to an extensive training period (2–3 months, with several hundreds of trials/day), and the cortical map of

primary sensory cortices was tested thereafter under general anesthesia. Both in the auditory and in the somatosensory cortex, enlarged map representations were reported after behavioral training. In the somatosensory system, two experiments using slightly different behavioral training revealed the same effect: when the training situation required that the animal actively and intensively used a small cutaneous portion of its hand, enlarged cortical representations were observed in favor of the trained skin surface [13, 14]. In the auditory cortex, an enlargement of the tonotopic map organization was found after a 3-month training in a perceptive discrimination task [15]. In this task, adult monkeys had to distinguish between pairs of stimuli made of two identical tone frequencies (the S1 stimulus) and pairs made of two slightly different frequencies (the S2 stimulus). Correct detection of the S2 stimulus allowed the animal to obtain a food reward, but this S2 stimulus was modified from session to session, being harder to detect each time the animal reached a detection level of 70%. The number of cortical sites responding to the frequency of the S1 stimulus was greater in the trained animals, leading to an increase in the cortical area corresponding to the trained frequency. Also, it was noted that the Q10dB (an index of the sharpness of tuning) was greater in trained than in control animals. The minimum latency of the evoked responses was increased in the trained animals, which might indicate that a recruitment of connections underlies the effects observed on the cortical map organization. More recently, two attempts to extend these findings led to opposite results. Rutkowski and Weinberger [16] used a particularly elegant strategy to obtain different levels of performance in rats trained to barpress for water delivery at presentation of a 6-kHz tone used as CS. During associative learning, the tone level of behavioral importance was controlled by the amount of supplemental water that the animals received in their home cage, so the asymptotic level of performance across subjects was correct in 60 to 190%. Maps of AI showed a large expanded representation for the frequency band centered on 6 kHz (the 4- to 8-kHz band), and there was a significant correlation between the final level of performance and selective expansion of the frequency band corresponding to the CS. In addition, the authors showed that other sounds associated with reward delivery also produced map reorganizations which, in that case, were not correlated with behavioral performance. In contrast, the study by Brown et al. [17] failed to detect map reorganization in AI after extensive perceptual learning, using a task that, in principle, was similar to that employed by Recanzone et al.

Dynamics of Auditory Receptive Fields

Neuroembryol Aging 2004–05;3:230–238

Plasticity of Cortical Maps after Behavioral Training

231

[15]. Cats trained on a 8-kHz frequency discrimination task showed improvements in performance that reflected changes in discriminative capacity. However, quantification of the AI tonotopic map indicated that the frequency organization in trained cats did not differ from that in controls. Nonetheless, quantitative measures of the response characteristics indicated that neurons with a characteristic frequency (CF) immediately above 8 kHz had slightly broader tuning in the trained cats and had significantly shorter response latency. The authors concluded that substantial changes in perceptual discriminative capacity can occur without change in primary cortical topography and with only modest changes in neuronal response characteristics. It is obviously difficult to reconcile these results, mostly because many factors can influence the changes in map organization: (i) the final differential threshold achieved during the last training sessions, (ii) the perceptual abilities acquired when procedural learning is at the asymptotic level, and (iii) the degree of motivation involved when the animal performed the behavioral task can potentially influence the map reorganizations. At the present time, data are too sparse to evaluate the impact of each of these factors. Comparisons with results on humans obtained with functional magnetic resonance imaging are inappropriate because maps in animals are collected under general anesthesia, whereas human data are obtained in awake subjects offering the possibility for multiple cascades of top-down processing.

Receptive Field Dynamics in Awake Animals

During Behavioral Training The determination of topographic maps is a very powerful technique to reveal large-scale plasticity in sensory cortices. However, with rare exceptions [18], maps derived in animals are usually determined under general anesthesia, partially because they require presentation of large sets of stimuli that need to be precisely controlled. Therefore, they provide somewhat static pictures of the thalamocortical system at a particular time point (for example after behavioral training). In contrast, single-unit recordings of behavior in animals allow to track the dynamics of sensory processing while the animal is submitted to a behavioral training [for a recent review see ref. 19]. The first evidence that functional properties of auditory cortical cells can be selectively affected by a brief learning experience came from the works of Diamond and Weinberger [20, 21] who demonstrated that the fre232


quency tuning curves of cortical neurons display selective modifications at the frequency of the tone associated with an aversive US. In these pioneering studies, tuning curves collected in secondary auditory areas (AII/VE) after 40–70 conditioning trials displayed either selective increases or selective decreases at the CS frequency. In contrast, several subsequent studies performed in primary auditory cortex (AI) revealed only selective increases at the CS frequency [22–24]. This ‘retuning’ of auditory cortex neurons is also prominent after a two-tone discrimination [25] and after instrumental training [26]. A striking property is the rapidity of occurrence of this plasticity: selective changes in the neuron tuning curves were detected after only 5–10 trials (10–20 min of training). Although cortical plasticity is usually emphasized, we have to keep in mind that the frequency tuning curves of thalamic cells can also display selective modifications after a brief learning experience [27–29]. The pitfall of all these initial studies was that the dynamics of sensory processing was tested ‘off line’: the animals were trained in a brief behavioral session and the tuning curves were tested before and after training while the animals were not actively using the tones for their current behavior. This problem was circumvented in the studies performed by two research groups. In their experimental design, Ohl and Scheich [30, 31] engaged gerbils in a multiple-frequency discrimination task involving a single CS+ (followed by an aversive US) and 11 CS– (not followed by the US). In that study, selective tuning curve alterations were found but they involved decreases, or no changes, at the CS+ frequency and increases at frequencies adjacent to the CS+, which, according to the authors, enhanced locally the gradient of responses versus sound frequency. This effect was interpreted as an enhancement of sensitivity for frequency changes in the spectral neighborhood of the training tone. Recently, an enhancement of contrast spectral sensitivity has also been reported in an appetitive task involving discrimination between a repeated standard tone (S–) and a deviant tone of higher frequency (S+). Over weeks, when cats decreased their frequency discrimination threshold, the response strength to the training frequencies gradually located in a local minimum compared to adjacent frequencies [32]. A quite different strategy was employed by Fritz et al. [33]: they trained ferrets in instrumental tasks during which complex acoustic stimuli allowed quantitative measurement of the spectrotemporal receptive fields (STRFs). More precisely, at each trial a set of stimuli (named TORC) were presented, first when the animal Huetz /Edeline

was passively listening, second when it was actively looking for the presence of a pure tone within the set of TORC, and third when it was again passively listening. Thus, the animal was trained to detect a pure tone in a background of TORCs, and the STRFs obtained during the detection task were compared with those obtained before and after the task. For a large majority of cells (79%, 31/39), an enhancement in the excitatory field (or a reduction of the inhibitory sideband) appeared during the detection task in favor of the frequency to be detected [34]. Importantly, only a third of the facilitated effects reverted back to their original shape after completion of the task; in the other cases the STRF changes tended to persist after the behavior (in some cases, they were maintained during more than 5 h). Recently, similar effects were also obtained using a discrimination task: during the behavioral session, TORCs were accompanied by a reference tone, and the animal had to respond to the TORC accompanied by the target tone (differed in frequency from the reference tone). In this situation, most of the STRFs exhibiting significant changes expressed decreased response to the reference tone and facilitated responses to the target tone [35]. The advantage of this strategy is clear: the functional properties of the neurons can be assessed while the animal is actively using the stimuli to perform the behavioral task. As the target tones (or the reference tones and the target ones) are embedded in TORC stimuli, this technique provides snapshot moment-to-moment images of functional changes related to behavior. In both the detection task and the discrimination task, significant STRF changes can be detected very rapidly (in 2–3 min, the maximum temporal resolution of this technique). Although it is difficult to reconcile all these results, some hypotheses have recently been proposed. Reviewing the literature on learning-induced plasticity in the auditory cortex, Ohl and Scheich [36] put forward that the type of plasticity that emerged in the auditory cortex is task dependent. In other words, depending on the constraint of the task in which subjects are trained, different types of plasticity are expressed. However, this appealing possibility should be called only when it is clear that other factors, such as the selectivity of behavioral performance, can be ruled out. In fact, studies using similar tasks gave quite different results: in a discrimination task between a fixed target frequency and a deviant frequency, Fritz et al. [35] reported increased responses at the deviant frequency whereas Witte and Kipke [32] reported decreased responses. Conversely, studies using quite different behavioral tasks led to similar results [30, 32].

In the Absence of Behavioral Training It is essential to take into account that multiple factors can influence the functional properties of thalamocortical neurons in unanesthetized animals. The most trivial one is the animal’s state of vigilance. Both at the cortical and at the thalamic level, neurons display profound receptive field modifications when the state of vigilance shifts from waking to slow-wave sleep and to paradoxical sleep. At the thalamic level, the majority (69/102) of the cells recorded during slow-wave sleep displayed smaller receptive fields at suprathreshold intensities, higher thresholds and as a consequence smaller frequency response areas. During paradoxical sleep, two populations of thalamic cells appeared: one which exhibited a more pronounced version of the changes detected in slow-wave sleep, and the other which tended to show receptive field properties similar to those in waking [37]. At the cortical level, many cells displayed alterations in their receptive field from waking to slow-wave and paradoxical sleep, but the heterogeneity of changes from one cell to the next finally led to a lack of global effect on the whole population [38]. It is important to note that when determined in a given stable state, the tuning curves are relatively stable. However, even during a well-defined state of vigilance, some changes can be detected when the neurons modify their mode of discharge. It is well documented that thalamic neurons can display two distinct modes of discharge: a ‘tonic’ mode where neurons emit single action potentials and a ‘burst’ mode where neurons emit clusters of actions potentials at high (1200 Hz) frequency [for review see ref. 39]. This dual mode of discharge has recently been described in auditory thalamus [40, 41], and some of its consequences have been reported; in a given behavioral state (e.g. waking), when a neuron switches from a tonic to a burst mode, its response latency and the variability in its response latency are decreased [42]. These changes can have important functional implications because it was shown that the timing of neuronal discharge carries more information than the spike count [43].



Responses to Natural Vocalizations

Obviously, neurons of the thalamocortical auditory system are not specialized to process pure tones or complex artificial stimuli such as dynamic ripples or TORCs. Pure tones are useful because they provide the easiest way to test the most accessible property which can be determined from the cochlea to the cortex: the frequency selectivity. The introduction of dynamic ripples by several 233

research groups [44–46] allowed considerable progress because the transfer function of neurons can be determined both in the temporal and in the spectral domain. However, it is not unreasonable to envision that the exquisite properties expressed by auditory neurons were progressively shaped by evolution to process natural sounds such as conspecific vocalizations. In many species, particularly in the primate, species-specific communication sounds are important for social interactions, reproductive success and survival. Initial studies performed several decades ago have evaluated how thalamocortical neurons process communication sounds and have discarded the concepts of call detectors because most of the neurons respond to multiple calls [47–49]. However, over the last years, an increasing number of studies evaluated the neuronal representation of species-specific vocalizations. For example, it was found that in the primary auditory cortex of marmosets, neurons responded much stronger to natural vocalizations than to synthetic variations that had the same spectral but different temporal characteristics [50]. Several recent studies aimed at determining whether the responses to vocalizations of auditory cortex neurons can be predicted from responses to artificial sounds. In the inferior colliculus, a good match was reported between the neuron CF and the response to the spectral content of vocalizations [51]. In other words, one can predict the responses of inferior colliculus neurons to vocalizations based on the frequency tuning of neurons. Results obtained in the auditory cortex are at variance with this simple view. On the basis of tuning curves, frequency response areas and responses obtained in two-tone paradigm, it was not always possible to predict the responses to natural sounds [52]. For example, some neurons respond to natural sounds with sparse responses to pure tones and no clearly definable frequency response areas [52, fig. 4]; other neurons do not respond to a natural sound even when the peak of its power spectrum coincides with their best frequency. In fact, recordings in the rat auditory cortex reveal that only 11% of the responses to natural stimuli can be inferred by the linear transformation from the sound spectrogram to responses of neurons (known as the STRF of neurons [53]). Therefore, whereas the STRF successfully predicts the responses to some of the natural stimuli, it fails completely to predict the responses to others. A similar picture seems to emerge at the thalamic level. In a recent study during which responses to guinea pig vocalizations were tested, no relationship was found between the CF of thalamic neurons and the responsiveness of neurons to vocalizations having different spectral con234


tent [54]. Preliminary results from our laboratory indicate that the STRFs of thalamic neurons can also fail in predicting the responses to natural stimuli (fig. 1). In contrast, in the inferior colliculus, the STRF model usually provides a good match between the predicted responses and the real responses to natural stimuli [55].

Temporal Coding and Transmitted Information

In the past, the functional properties of thalamocortical cells have been evaluated based solely on the firing rate, i.e. on the number of action potentials emitted at presentation of natural or artificial auditory stimuli. This is quite surprising given that it has long been pointed out that the temporal organization of neuronal discharges can code for the sound characteristics (such as sound frequency or intensity) more reliably than rate code does [56]. It is out of the scope of this review to describe the various aspects of temporal organizations which can participate in the neural code [reviewed in ref. 57, 58]. Regarding large cell populations, induced neuronal oscillations (particularly in the range) are often viewed as a main actor in the construction of object representation both in the visual and in the auditory modality [reviewed in ref. 59]. Regarding small cell populations, temporal code can be expressed by the short time-scale coordination of neuronal discharges assessed by cross-correlograms. Auditory cortex neurons, for example, can display selective neuronal coordination for a stimulus location or a stimulus movement [60]. Furthermore, short time-scale interactions between neuronal discharges were found to be related with sound frequency independently of the firing rate: neurons can synchronize their firing rate without firing more action potentials [61]. At the single-cell level, temporal coding can involve the exact time of occurrence of the action potentials and/or the succession of the interspike intervals. In the auditory cortex, the critical parameter for the first-spike latency is the acceleration of peak pressure at tone onset rather than the rise time or the sound pressure level per se [62, 63]. Over a neuronal population, this property can be used to track properties of transients, and thus might contribute to the instantaneous coding of transients thought to underlie the categorical perception of speech and some nonlinguistic sounds [see discussion in ref. 63]. In addition, several studies provided evidence that auditory cortex neurons code stimulus location using the temporal structure of spike trains more than the rate of discharge of individual neurons [64, 65]. When an artificial neural netHuetz /Edeline

150 Response Prediction Spike rate (spikes/s)

100

50

0

–50 0

200

400

600

800

1,000

1,200

1,400

1,600

Time (ms)

Fig. 1. Predicted neuron poststimulus time histogram (PSTH) with original PSTH: lack of predictability of auditory thalamus responses based on STRF. Auditory thalamus neurons were tested with four guinea pig vocalizations presented 20 times in their natural and time-reversed version (total: 8 stimuli). Seven sets of stimulus responses were used to construct the STRF and the eighth stimulus was used to test the adequacy between the predic-

tion of STRF and the actual PSTH of the neuron. Construction of the STRF and prediction stage were done using the software STRFPAK [71]. The actual PSTH is represented by the black line and the prediction by the grey line. For this particular neuron (CF at 4 kHz, threshold 20 dB), there is no match between the actual responses and the responses predicted based on the STRF. Other neurons can provide good or very good predictions [72].

work is trained to recognize a stimulus location among 18 possible sources using neuron spike trains as inputs, it appears that, for most of the cells, azimuth coding by complete spike pattern is more accurate than by spike count, probably because of additional stimulus-related information contained in the timing of spikes. Based on single-unit recordings obtained in the auditory cortex of ferrets and cats, Nelken et al. [66] have demonstrated that for responses to natural and artificial sounds, spike count alone falls very short of computing the full information between stimuli and spikes trains. Essentially all the information can be grasped by adding another measure related to the timing of spikes in the calculation: the mean response time [66]. Recently, we started to investigate whether spike timing precision can reliably encode more information than firing rate at presentation of natural communication sounds. To address this question, we used the technique developed by Victor and Purpura [67, 68] to assess the precision of temporal coding from spike trains of visual cortex neurons. This metric-space method allows the estimation of information contained in spike trains and avoids the classical binning problem of the ‘direct’ method [69] when estimating this information [70]. Similar to

some other methods, the metric-space analysis computes distances between spike trains (as a measure of their dissimilarity), but it does not require binning of spike trains or embedding them in vector spaces, with dimensions dramatically increasing when one wants to keep a good time resolution (e.g. millisecond range) over several seconds of stimulus presentation. This technique also has the advantage to estimate the temporal resolution which maximizes the transmitted information. Analyzing the responses of auditory thalamus neurons to guinea pig vocalizations, we found that taking into account the temporal organization of neuronal discharges largely increases transmitted information, including cells that transmit little or no information based on spike count (fig. 2).



Conclusions

Over the last 2 decades, electrophysiological recordings performed in awake animals have revealed the dynamics of sensory processing. We have made considerable progress in our understanding of the thalamocortical auditory system: this system can no longer be considered as a passive analyzer of the external word, but should be 235

a

0

100

200

300

400

500

600

700

0

100

200

300

400

500

600

700

b

Time (ms)

Fig. 2. The rate of transmitted information

236

Transmitted information Chance level

1.0

Transmitted information

0.8

0.6

0.4

0.2

c


1

10 5

100 50

0 1,000 500

increases when spike timing is considered. a, b Raster plots from responses of an auditory thalamus neuron to 100 presentations of a guinea pig vocalization (a ‘purr’) in its natural version (a) and in its time-reversed version (b). Stimulus begins at time 0 and lasts for 700 ms. c Transmitted information (black line) and chance level (grey line) as a function of the spike timing precision computed with the metric-space analysis of Victor and Purpura [67, 68]. The curve indicates that there is almost no transmitted information based on the spike count (value of information at the origin, when spike timing precision tends to infinite). The maximum of transmitted information is reached for values of spike timing in the 10-ms range.

Spike timing precision (ms)

Huetz /Edeline

viewed as part of the network contributing to cognitive representation. In the future, the studies in awake animals should probably benefit from the development of new recording techniques such as calcium imaging or cortical intrinsic signaling. However, the electrophysiological techniques will remain essential because, when associated with new analytical tools, they allow descriptions of the neural code focused on the temporal organization of neural discharges. As the neural code probably evolved over evolution to provide precise and reliable processing of sounds necessary for survival, a straightforward approach to understand this code is certainly to use natural communication sounds. Dissecting the neural code used by awake animals to extract information

from natural stimuli is an enormous and very exciting challenge.

Acknowledgments We thank Elizabeth Hennevin for helpful comments on an earlier draft of this paper. The preliminary analyses presented here were made with the help of Israel Nelken at the Advanced Course in Computational Neuroscience (Arcachon, August 2005). C.H. was supported by a fellowship from the French Ministère de la Recherche et de l’Enseignement Supérieur. The research described in this review was partially supported by a grant from the Action Concertée Incitative ‘Neurosciences Intégratives et Computationnelles’.

References 1 Galambos R: Microelectrode studies on medial geniculate body of cat. III. Response to pure tones. J Neurophysiol 1952; 15: 381– 400. 2 Griffiths TD, Warren JD, Scott SK, Nelken I, King AJ: Cortical processing of complex sound: a way forward? Trends Neurosci 2004;27:181–185. 3 Shamma S: On the role of space and time in auditory processing. Trends Cogn Sci 2001; 5:340–348. 4 Winer JA, Miller LM, Lee CC, Schreiner CE: Auditory thalamocortical transformation: structure and function. Trends Neurosci 2005;28:255–263. 5 Hubel DH, Henson CO, Rupert A, Galambos R: ‘Attention’ units in the auditory cortex. Science 1959;129:1279–1280. 6 Evans EF, Whitfield IC: Classification of unit responses in the auditory cortex of the unanesthetized and unrestrained cat. J Physiol 1964;171:476–493. 7 Brugge JF, Merzenich MM: Responses of neurons in auditory cortex of macaque monkey to monaural and binaural stimulation. J Neurophysiol 1973; 36:1130–1158. 8 Kaas JH: Topographic maps are fundamental to sensory processing. Brain Res Bull 1997;44:107–112. 9 Shamma SA: Topographic organization is essential for pitch perception. Proc Natl Acad Sci USA 2004;101:1114–1115. 10 Gonzalez-Lima F, Scheich H: Neural substrates for tone-conditioned bradycardia demonstrated with 2-deoxyglucose. I. Activation of auditory nuclei. Behav Brain Res 1984;14:213–233. 11 Gonzalez-Lima F, Scheich H: Classical conditioning enhances auditory 2-deoxyglucose patterns in the inferior colliculus. Neurosci Lett 1984;51:79–85.


12 Gonzalez-Lima F, Scheich H: Neural substrates for tone-conditioned bradycardia demonstrated with 2-deoxyglucose. II. Auditory cortex plasticity. Behav Brain Res 1986;20:281–293. 13 Jenkins WM, Merzenich M, Ochs MT, Allard T, Guic-Robles E: Functional reorganization of primary somatosensory cortex in adult owl monkeys after behaviorally controlled tactile stimulation. J Neurophysiol 1990;63:82–104. 14 Recanzone GH, Merzenich MM, Jenkins WM, Grajski KA, Dinse HA: Topographic reorganization of the hand representation in cortical area 3b of owl monkeys trained in a frequency discrimination task. J Neurophysiol 1992;67:1031–1056. 15 Recanzone GH, Schreiner CE, Merzenich MM: Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys. J Neurosci 1993;13:87–103. 16 Rutkowski RG, Weinberger NM: Encoding of learned importance of sound by magnitude of representational area in primary auditory cortex. Proc Natl Acad Sci USA 2005; 102:13664–13669. 17 Brown M, Irvine DR, Park VN: Perceptual learning on an auditory frequency discrimination task by cats: association with changes in primary auditory cortex. Cereb Cortex 2004;14:952–965. 18 Chen LM, Friedman RM, Roe AW: Optical imaging of SI topography in anesthetized and awake squirrel monkeys. J Neurosci 2005;25:7648–7659. 19 Weinberger NM: Specific long-term memory traces in primary auditory cortex. Nat Rev Neurosci 2004;5:279–290.

20 Diamond DM, Weinberger NM: Classical conditioning rapidly induces specific changes in frequency receptive fields of single neurons in secondary and ventral ectosylvian auditory cortical fields. Brain Res 1986; 372: 357–360. 21 Diamond DM, Weinberger NM: Role of context in the expression of learning-induced plasticity of single neurons in auditory cortex. Behav Neurosci 1989;103:471–494. 22 Bakin JS, Weinberger NM: Classical conditioning induces CS-specific receptive field plasticity in the auditory cortex of the guinea pig. Brain Res 1990;536:271–286. 23 Weinberger NM, Javid R, Lepan B: Longterm retention of learning-induced receptive-field plasticity in the auditory cortex. Proc Natl Acad Sci USA 1993; 90: 2394– 2398. 24 Edeline JM, Pham P, Weinberger NM: Rapid development of learning-induced receptive field plasticity in the auditory cortex. Behav Neurosci 1993;107:539–551. 25 Edeline JM, Weinberger NM: Receptive field plasticity in the auditory cortex during frequency discrimination training: selective retuning independent of task difficulty. Behav Neurosci 1993;107:82–103. 26 Bakin JS, South DA, Weinberger NM: Induction of receptive field plasticity in the auditory cortex of the guinea pig during instrumental avoidance conditioning. Behav Neurosci 1996;110:905–913. 27 Edeline JM, Weinberger NM: Subcortical adaptive filtering in the auditory system: associative receptive field plasticity in the dorsal medial geniculate body. Behav Neurosci 1991;105:154–175. 28 Edeline JM, Weinberger NM: Thalamic short-term plasticity in the auditory system: associative retuning of receptive fields in the ventral medial geniculate body. Behav Neurosci 1991;105:618–639.


237

29 Edeline JM, Weinberger NM: Associative retuning in the thalamic source of input to the amygdala and auditory cortex: receptive field plasticity in the medial division of the medial geniculate body. Behav Neurosci 1992;106:81–105. 30 Ohl FW, Scheich H: Differential frequency conditioning enhances spectral contrast sensitivity of units in auditory cortex (field Al) of the alert Mongolian gerbil. Eur J Neurosci 1996;8:1001–1017. 31 Ohl FW, Scheich H: Learning-induced dynamic receptive field changes in primary auditory cortex of the unanaesthetized Mongolian gerbil. J Comp Physiol [A] 1997; 181: 685–696. 32 Witte RS, Kipke DR: Enhanced contrast sensitivity in auditory cortex as cats learn to discriminate sound frequencies. Cogn Brain Res 2005;23:171–184. 33 Fritz J, Elhilali M, Shamma S: Active listening: task-dependent plasticity of spectrotemporal receptive fields in primary auditory cortex. Hear Res 2005;206:159–176. 34 Fritz J, Shamma S, Elhilali M, Klein D: Rapid task-related plasticity of spectrotemporal receptive fields in primary auditory cortex. Nat Neurosci 2003;6:1216–1223. 35 Fritz JB, Elhilali M, Shamma SA: Differential dynamic plasticity of A1 receptive fields during multiple spectral tasks. J Neurosci 2005; 25:7623–7635. 36 Ohl FW, Scheich H: Learning-induced plasticity in animal and human auditory cortex. Curr Opin Neurobiol 2005;15:470–477. 37 Edeline JM, Manunta Y, Hennevin E: Auditory thalamus neurons during sleep: changes in frequency selectivity, threshold and receptive field size. J Neurophysiol 2000; 84: 934–953. 38 Edeline JM, Dutrieux G, Manunta Y, Hennevin E: Diversity of receptive field changes in auditory cortex during natural sleep. Eur J Neurosci 2001;14:1865–1880. 39 Sherman SM: Tonic and burst firing: dual modes of thalamocortical relay. Trends Neurosci 2001;24:122–126. 40 He J, Hu B: Differential distribution of burst and single-spike responses in auditory thalamus. J Neurophysiol 2002; 88:2152–2156. 41 Massaux AE, Edeline JM: Bursts in the medial geniculate body: a comparison between anesthetized and unanesthetized states in guinea-pig. Exp Brain Res 2003; 153: 573– 578. 42 Massaux A, Dutrieux G, Cotillon-Williams N, Manunta Y, Edeline JM: Auditory thalamus bursts in anesthetized and non-anesthetized states: contribution to functional properties. J Neurophysiol 2004; 91: 2117– 2134.

238

43 Mickey BJ, Middlebrooks JC: Representation of auditory space by cortical neurons in awake cats. J Neurosci 2003;23:8649–8663. 44 Eggermont JJ: Wiener and Volterra analyses applied to the auditory system. Hear Res 1993;66:177–201. 45 Kowalski N, Depireux DA, Shamma SA: Analysis of dynamic spectra in ferret primary auditory cortex. I. Characteristics of single-unit responses to moving ripple spectra. J Neurophysiol 1996; 76:3503–3523. 46 Escabi MA, Schreiner CE: Nonlinear spectrotemporal sound analysis by neurons in the auditory midbrain. J Neurosci 2002; 22: 4114–4131. 47 Newman JD, Wollberg Z: Multiple coding of species-specific vocalizations in the auditory cortex of squirrel monkeys. Brain Res 1973;54:287–304. 48 Glass I, Wollberg Z: Auditory cortex responses to sequences of normal and reversed squirrel monkey vocalizations. Brain Behav Evol 1983;22:13–21. 49 Glass I, Wollberg Z: Responses of cells in the auditory cortex of awake squirrel monkeys to normal and reversed species-specific vocalizations. Hear Res 1983;9:27–33. 50 Wang X, Merzenich MM, Beitel R, Schreiner CE: Representation of a species-specific vocalization in the primary auditory cortex of the common marmoset: temporal and spectral characteristics. J Neurophysiol 1995; 74: 2685–2706. 51 Suta D, Kvasnak E, Popelar J, Syka J: Representation of species-specific vocalizations in the inferior colliculus of the guinea pig. J Neurophysiol 2003; 90:3794–3808. 52 Rotman Y, Bar-Yosef O, Nelken I: Relating cluster and population responses to natural sounds and tonal stimuli in cat primary auditory cortex. Hear Res 2001;152:110–127. 53 Machens CK, Wehr MS, Zador AM: Linearity of cortical receptive fields measured with natural sounds. J Neurosci 2004; 204: 1089– 1100. 54 Philibert B, Laudanski J, Edeline JM: Auditory thalamus responses to species-specific guinea-pig vocalizations: a comparison between rat and guinea-pig. Hear Res 2005; 209:97–103 55 Woolley SM, Gill PR, Theunissen FE: Stimulus-dependent auditory tuning results in synchronous population coding of vocalizations in the songbird midbrain. J Neurosci 2006;26:2499–2512. 56 Hind JE, Goldberg JM, Greenwood DD, Rose JE: Some discharge characteristics of single neurons in the inferior colliculus of the cat. II. Timing of discharges and observations on binaural stimulation. J Neurophysiol 1963; 26:321–341. 57 Eggermont JJ: Is there a neural code? Neurosci Biobehav Rev 1998;22:355–370.


58 Eggermont JJ: Between sound and perception: reviewing the search for a neural code. Hear Res 2001;157:1–42. 59 Tallon-Baudry C, Bertrand O: Oscillatory gamma activity in humans and its role in object representation. Trends Cogn Sci 1999; 3: 151–162. 60 Ahissar M, Ahissar E, Bergman H, Vaadia E: Encoding of sound-source location and movement: activity of single neurons and interactions between adjacent neurons in the monkey auditory cortex. J Neurophysiol 1992;67:203–215. 61 deCharms RC, Merzenich MM: Primary cortical representation of sounds by the coordination of action-potential timing. Nature 1996;381:610–613. 62 Heil P, Irvine DR: On determinants of firstspike latency in auditory cortex. Neuroreport 1996;7:3073–3076. 63 Heil P: Auditory cortical onset responses revisited. I. First-spike timing. J Neurophysiol 1997;77:2616–2641. 64 Middlebrooks JC, Clock AE, Xu L, Green DM: A panoramic code for sound location by cortical neurons. Science 1994; 264: 842– 844. 65 Middlebrooks JC, Xu L, Clock AE, Green DM: Codes for sound-source location in nontonotopic auditory cortex. J Neurophysiol 1998;80:863–868. 66 Nelken I, Chechik G, Mrsic-Flogel TD, King AJ, Schnupp JW: Encoding stimulus information by spike numbers and mean response time in primary auditory cortex. J Comput Neurosci 2005;19:199–221. 67 Victor JD, Purpura KP: Nature and precision of temporal coding in visual cortex: a metricspace analysis. J Neurophysiol 1996; 76: 1310–1326. 68 Victor JD, Purpura KP: Metric-space analysis of spike trains: theory algorithms and application. Network 1997;8:127–164. 69 Strong SP, Koberle R, Ruyter van Steveninck R, Bialek W: Entropy and information in neural spike trains. Phys Rev Lett 1998; 80: 197–200. 70 Victor JD: How the brain uses time to represent and process visual information. Brain Res 2000;886:33–46. 71 Theunissen FE, David SV, Singh NC, Hsu A, Vinje WE, Gallant JL: Estimating spatiotemporal receptive fields of auditory and visual neurons from their responses to natural stimuli. Network 2001;12:289–316. 72 Laudanski J, Huetz C, Edeline JM: Spectrotemporal receptive field of guinea-pig auditory cortex neurons: differences between dynamic noise and natural communication sounds. Nottingham, Auditory Cortex: The Listening Brain, Sept 17–21, 2006.

Huetz /Edeline