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REPORT
Hearing Research Center, Department of Biomedical Engineering, Center for Biodynamics and Program in Mathematical and Computational Neuroscience, Boston University, Boston, Massachusetts
Submitted 10 February 2005; accepted in final form 18 May 2005
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ABSTRACT |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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; Rauschecker 1998). Recent studies have described the structure of neuronal receptive fields (RFs) in primary auditory cortex (ACx), employing natural as well as complex naturalistic sounds (Depireux et al. 2001; Elhilali et al. 2004; Linden et al. 2003; Machens et al. 2004; Miller et al. 2001, 2002). Although such studies have revealed new aspects of cortical representations for complex sounds, the functional roles of cortical neurons in processing natural sounds remain unclear.
The combination of well-understood vocal communication behaviors and identified neural circuits that mediate the perception, production, and learning of vocal communication sounds makes the songbird system attractive for studying the functional roles of neural substrates (Brainard and Doupe 2002; Konishi 1985). Here, we explore the role of neurons in field L, the ACx analog in songbirds, in a behaviorally important function: discriminating between conspecific songs (songs of the bird's own species).
Previous experiments suggest a link between discrimination of simple synthetic sounds, e.g., tones, and RFs (Edeline and Weinberger 1993; Fritz et al. 2003; Recanzone et al. 1993). However, how RF structure may influence the discrimination of natural sounds remains poorly understood. We take a computational approach to relate song discrimination to RF structure in field L. First, we modeled auditory neurons in field L based on previous experimental measurements of spectral temporal RFs (STRFs), a quantitative description of the receptive fields of auditory neurons (Sen et al. 2001; Theunissen et al. 2000). Here, we focused on delayed inhibitory STRFs, which contain a single excitatory and a single inhibitory region localized around a characteristic frequency. We chose these STRFs for three reasons. First, these STRFs are simple in structure, providing a good starting point for computational analysis. Second, these are a general class of RFs, found in field L, ACx of several species (Depireux et al. 2001; Elhilali et al. 2004; Linden et al. 2003; Machens et al. 2004; Miller et al. 2001, 2002; Sen et al. 2001; Theunissen et al. 2000), as well as other sensory modalities such as vision and somatosensation (DeAngelis et al. 1995; Ghazanfar and Nicolelis 2001). Thus the analysis of such RFs may provide general insights into cortical sensory processing of natural stimuli. Third, a candidate microcircuit underlying such RFs, involving delayed synaptic inhibition, has been identified by recent experiments (Cruikshank et al. 2002; Tan et al. 2004; Wehr and Zador 2003). We take advantage of this to explicitly model this circuit and compare the results with the STRF model. To characterize neural discrimination of songs, we used a recently proposed theoretical measure, the spike distance metric (SDM), to quantify the dynamics and temporal resolution of discrimination (Machens et al. 2003; van Rossum 2001). Finally, we generated model STRFs (Qiu et al. 2003) to assess the resulting changes in discrimination.
We used this approach to address several questions. How accurately do model neurons discriminate between natural sounds? How does the accuracy of discrimination evolve over time as the neural response accumulates? Can STRF structure be related to discrimination? Specifically, how does the delayed inhibitory region in the STRF affect the discrimination of songs? Finally, we modeled a cortical microcircuit, based on delayed synaptic inhibition, which may underlie such STRFs, and studied the effects of blocking inhibition in this model circuit on the discrimination of natural sounds.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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). The spectral and temporal profiles of the STRF were modeled using Gabor functions. The difference in our study is that we used the sum of two Gabor functions to model the temporal profile, which allowed us to alter the properties of the excitatory and inhibitory subregions of the STRF independently. The best modulation frequency (BMF) for the delayed inhibitory STRF we used was 40 Hz. Other BMFs in the range of 10–40 Hz were also tested with similar results. We also modeled narrow and broadband STRFs with identical temporal profiles but different spectral bandwidths (the half-maximal bandwidth ranged from 1 to 4 kHz).
We constructed a computational model of a cortical neuron, based on the STRF. As input to the model neurons, we used 20 zebra finch songs, which have previously been used to experimentally characterize the STRFs of neurons in field L (Sen et al. 2001; Theunissen et al. 2000). The distribution of song durations ranged from 0.8 to 4.4 s (mean = 2.0 s, median = 1.8 s, mode = 1.4 s). An estimate of the firing rate of the neuron was obtained by convolving the song spectrogram with the STRF (Sen et al. 2001; Theunissen et al. 2000). The firing rate was used to drive a stochastic spike train generator to obtain several trials of spike trains (Dayan and Abbott 2001). The stochastic model was an inhomogeneous Poisson process with an absolute and a relative refractory period, with values of 2 and 5 ms, respectively (Berry and Meister 1998).
We employed the SDM to quantify the dissimilarity between pairs of spike trains (van Rossum 2001). This metric is computed as the Euclidean distance between a pair of spike trains, which have been filtered using a decaying exponential kernel with time constant 
. The parameter can be varied to examine discrimination over different time scales of the neural response. We used a classification scheme based on the SDM to quantify the neural discrimination of songs (Machens et al. 2003). Ten trials of spike trains were obtained from the model for each of the 20 songs aligned at their onsets. A template spike train was chosen for each of the songs, and remaining spike trains were assigned to the song with the closest template based on the spike distance measure. This procedure was repeated 100 times for different templates. The percentage of correctly classified songs (% correct) was used as a measure of discrimination. The chance level for classification was 5%, because a spike train could be assigned to 1 of 20 songs. To compare discrimination between models with and without an inhibitory subregion, we constrained the average firing rate, over all songs in the two models to be equal. The average rate was 17 Hz, which is within the range of experimentally observed values in field L (Sen et al. 2001). To verify that our results were metric independent, we also employed an alternate cost-based distance metric (Victor and Purpura 1997).
We constructed a model cortical microcircuit, similar to a model recently described by Wehr and Zador 2003. The model consisted of an excitatory (E) input neuron and an inhibitory (I) input neuron, connected to an excitatory (E) output neuron (Fig. 3A). Spike trains of the E input neuron were generated using a purely excitatory STRF and the stochastic spike generator described earlier. The spike trains of the I neuron were generated by delaying and jittering the excitatory spike train. The delay was 2.5 ms, and the jitter was a random number drawn from a Gaussian distribution with a mean of 0 and an SD of 1 ms. The output E neuron was a standard integrate-and-fire neuron, with excitatory and inhibitory synaptic conductances. Synaptic inputs were modeled using 
functions (Dayan and Abbott 2001), with time constants of 1.5 ms for the excitatory synapse and 12 ms for the inhibitory synapse. The strengths of the excitatory and inhibitory synapses were controlled using two synaptic weights, we and wi, with wi set to 0 for the circuit without inhibition. The average firing rate in both models was constrained to be equal at 17 Hz by scaling the firing rate of the input E neuron.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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We quantified discrimination of songs by a model neuron with a delayed inhibitory STRF (Fig. 1A, inset), using a classification method based on the SDM (see METHODS). Figure 1A shows the accuracy of song discrimination (% correct) as a function of spike train duration for = 10 ms. Song discrimination increases rapidly between 0 and 500 ms of song duration, reaching an accuracy level of 
70%, after which it improves more gradually and reaches a maximal accuracy of 97%. We obtained similar results with an alternate cost-based distance metric (Victor and Purpura 1997).
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To compare discrimination at different time scales of the neural response, we varied the time constant in the SDM (see METHODS). At small time scales, the SDM acts like a "coincidence detector," with small differences in spike timing contributing to the distance, whereas at long time scales, the metric acts like a "rate difference counter," where average firing rates contribute to the distance (van Rossum 2001). Figure 1B shows the discrimination performance for the model neuron with the delayed inhibitory STRF at different time scales. At a very long time scale of 1,000 ms, discrimination accuracy is poor (20%). As the time scale is decreased, the temporal structure of the neural response is considered at increasingly finer resolutions and discrimination improves significantly, reaching a maximal value of 92% at 10 ms. The discrimination accuracy is >80% for time scales ranging from 4 to 25 ms and drops sharply for values outside this range.
Delayed inhibitory subregion improves discrimination
Next we generated STRFs without an inhibitory subregion and compared the discrimination curves for model neurons with and without an inhibitory subregion in the STRF (Fig. 2, insets). The discrimination curve for the STRF with the inhibitory region rises more rapidly and reaches a higher final level of accuracy compared with the STRF without the inhibitory region (Fig. 2). Both curves plateau at about 1,300 ms, when the inhibitory STRF model achieves an accuracy level of 97%, whereas the curve without inhibition results in an accuracy level of only 67%.
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Finally, we studied discrimination in a model circuit, which has been proposed to underlie delayed inhibitory STRFs (Fig. 3A). This circuit consisted of an excitatory neuron receiving two synaptic inputs: a direct excitatory input and an indirect, i.e., disynaptic inhibitory, input. Figure 3B shows the discrimination curves for the output excitatory neuron, with and without inhibition (see METHODS). Discrimination performance with inhibition plateaus at about 1,400 ms achieved 97% accuracy, whereas performance without inhibition improves at a much slower rate, reaching 72% accuracy at 3,000 ms.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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In ACx, the timing of inhibitory conductance has been shown to be delayed relative to excitatory conductance (Tan et al. 2004; Wehr and Zador 2003). Previous studies have suggested that such a scheme may provide a mechanistic substrate for delayed inhibitory cortical STRFs (Miller et al. 2001). We constructed a simple model of such a cortical microcircuit with delayed inhibition and studied the discrimination of natural sounds with and without inhibition. We found that the presence of delayed inhibition in this circuit improved the discrimination of songs in a manner similar to that observed for the model with the delayed inhibitory STRF.
Delayed inhibition in field L seems to be mediated by a microcircuit similar to that in ACx, consisting of local GABAergic interneurons driven by feed-forward excitation from the thalamus (Muller and Scheich 1987, 1988). Recent experiments have revealed additional features of delayed inhibition in ACx. The delay between excitatory and inhibitory synaptic conductances has been measured directly using intracellular recordings (mean delay, 2–4 ms; Cruikshank et al. 2002; Tan et al. 2004; Wehr and Zador 2003). These studies suggest a GABAA receptor–mediated mechanism underlying delayed inhibition (Cruikshank et al. 2002; Tan et al. 2004), although a longer-lasting suppression, which may be mediated by synaptic depression, has also been reported (Wehr and Zador 2005). Similar intracellular measurements in field L will allow a more detailed comparison between the microcircuitry of avian and mammalian systems.
The importance of ACx in processing natural sounds such as human speech suggests specialized cortical mechanisms for efficiently processing such sounds. Intracortical inhibition may play an important role in the construction of specific spectral-temporal filters, e.g., delayed inhibitory STRFs. Consistent with this idea, recent studies in ACx have suggested that an important difference between thalamic and cortical STRFs is the structure of the inhibitory region (Miller et al. 2001).
Importance of temporal structure in complex sounds and neural responses
Clearly, the efficacy of particular classes of STRFs in processing natural sounds depends on the spectral and temporal cues present in the sounds. Temporal cues play a particularly important role in speech recognition (Shannon et al. 1995). Delayed inhibitory STRFs may provide a neural substrate for extracting temporal cues, e.g., onsets, from sounds with time-varying envelopes, e.g., vocal communication sounds, music, or environmental sounds. Similar cortical RFs in humans may allow better discrimination of sounds in speech and music, which differ in temporal attributes, e.g., voice onset time, formant transition duration, prosody, and rhythm.
Our study also indicates the potential importance of the fine temporal structure of neural responses in cortical discrimination. We found that for the delayed inhibitory STRFs, best discrimination was obtained at a relatively fine temporal resolution (10 ms). Previous studies have revealed precise timing in auditory cortical responses (DeWeese et al. 2003; Elhilali et al. 2004; Heil 1997; Lu et al. 2001; Wehr and Zador 2003). Our results show that information on a fine time scale is available for a behaviorally important function: discriminating the sounds of conspecifics. Several factors may shape the optimal temporal resolution for discrimination. These include extrinsic time scales present in the natural sounds [e.g., time scales associated with the sound onsets and amplitude modulations (AM)] and the time scales intrinsic to the neuron (e.g., time scales in the RF, time scales associated with refractoriness). An intrinsic time scale that might influence the optimal temporal resolution is the integration time scale, or the inverse of the BMF. Specifically, neurons with larger integration time scales may have larger optimal temporal resolution. We did not find such a direct relationship between the BMF and the optimal temporal resolution (data not shown, see METHODS for range of BMFs tested). Thus discrimination resolution may depend primarily on extrinsic time scales, e.g., time scales associated with onsets or other intrinsic time scales. Manipulating the statistics of natural sounds, e.g., onsets and AM, and examining the resulting changes in the optimal temporal resolution may clarify the relative contributions of extrinsic versus intrinsic time scales toward shaping the temporal resolution for discrimination.
Scope and future directions
Delayed inhibitory RFs are found in field L, ACx as well as other sensory cortices (DeAngelis et al. 1995; Depireux et al. 2001; Elhilali et al. 2004; Ghazanfar and Nicolelis 2001; Linden et al. 2003; Machens et al. 2004; Miller et al. 2001, 2002; Sen et al. 2001; Theunissen et al. 2000). Thus our results suggest that delayed inhibition in RFs may provide a general cortical mechanism for efficient discrimination of dynamic natural stimuli across multiple modalities. However, such RFs are one of many classes of cortical RFs observed experimentally. By itself, a narrowband onset detector, such as the delayed inhibitory STRFs considered here, is unlikely to be sufficient for all discrimination tasks. For example, a problem arises when there is insufficient power in the limited frequency range to which the narrowband onset detector is sensitive. A population of delayed inhibitory STRFs centered at different frequencies or a STRF with broader spectral bandwidth, i.e., broadband onset detectors, may be more robust. Broadband onset detectors are more common in field L relative to narrowband onset detectors (Theunissen et al. 2004). Broadband onset detectors produced discrimination very similar to narrowband onset detectors (data not shown, see METHODS for parameters). Thus both broadband and narrowband onset detectors may contribute toward accurate discrimination of birdsongs. Discrimination tasks involving more complex spectral cues may also require spectral edge detectors, e.g., STRFs with an excitatory subregion and inhibitory sidebands, or complex STRFs with excitatory and inhibitory subregions at multiple frequencies. These different classes of STRFs have been experimentally observed in field L and ACx (Sen et al. 2001; Theunissen et al. 2004). In the future, it will be important to explore the discrimination capabilities of other RF classes and RF populations and to relate neural discrimination to behavior.
Understanding the cortical code: stimulus statistics versus behavior
A theoretical proposal that has been influential in the visual system is that the goal of sensory processing is to construct efficient representations for natural stimuli (Attneave 1954; Barlow 1961; Simoncelli and Olshausen 2001). Such a theory predicts a relationship between receptive field properties of visual neurons and the statistics of natural images. Experimental and theoretical studies in vision suggest that efficient representations for natural images can be found in the primary visual cortex (Olshausen and Field 1996; Vinje and Gallant 2000).
A similar line of inquiry has recently been initiated for the auditory system (Attias and Schreiner 1998; Lewicki 2002; Singh and Theunissen 2003). Interestingly, experimental and theoretical studies have found statistically efficient representations of natural sounds as early as the auditory nerve (Lewicki 2002; Rieke et al. 1995). This finding raises an intriguing question: if efficient sensory codes are established at the periphery, what are the computational roles of subsequent auditory processing? We propose that a goal of subsequent stages of processing may be to build on efficient sensory representations to create new representations that are well suited for behaviorally important tasks, e.g., discrimination and recognition of species-specific and other complex sounds.
Both intracortical and precortical auditory processing may contribute to the generation of efficient behavioral representations. In the avian auditory system, selectivity for conspecific songs relative to synthetic sounds gradually increases from the midbrain nucleus mesencephalicus lateralis, pars dorsalis (MLd), the avian analog of inferior colliculus, to field L, to the caudal mesopallium (CM), which is analogous to secondary auditory cortex (Grace et al. 2003; Hsu et al. 2004; Theunissen et al. 2004). This pattern suggests an intracortical contribution to increasing selectivity for conspecific sounds. Secondary cortical areas CM and the caudal nidopallium (NCM) respond to song familiarity, suggesting a role in individual vocal recognition (Gentner 2004; Gentner and Margoliash 2003; Mello et al. 1992, 2004). The process of recognition can be conceptualized in two stages: discrimination followed by association. Thus improvements in song discrimination in field L may assist in song recognition in secondary cortical areas. Some aspects of the cortical code may also be inherited from precortical processing. For example, onset detector type STRFs have recently been reported in MLd (Woolley and Theunissen 2005), raising the possibility that similar STRFs found in field L may be inherited from MLd. Localizing where efficient representations for behavior originate will require further comparative analysis of response properties along hierarchical stages of auditory processing. Regardless of intracortical or precortical origins, such representations are likely to transform the cortical code. If so, understanding the cortical code will require an integrative analysis of receptive fields, stimulus statistics as well as behavior.
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ACKNOWLEDGMENTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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FOOTNOTES |
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Address for reprint requests and other correspondence: K. Sen, Dept. of Biomedical Engineering, Boston Univ., 44 Cummington St., Boston, MA 02215 (E-mail: kamalsen{at}bu.edu)
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) and without inhibition in the circuit (
). Time scale 
