Physiological studies in vocal animals such as songbirds indicate that vocalizations drive auditory neurons particularly well. But the neural mechanisms whereby vocalizations are encoded differently from other sounds in the auditory system are unknown. We used spectrotemporal receptive fields (STRFs) to study the neural encoding of song versus the encoding of a generic sound, modulationlimited noise, by single neurons and the neuronal population in the zebra finch auditory midbrain. The noise was designed to match song in frequency, spectrotemporal modulation boundaries, and power. STRF calculations were balanced between the two stimulus types by forcing a common stimulus subspace. We found that 91% of midbrain neurons showed significant differences in spectral and temporal tuning properties when birds heard song and when birds heard modulation-limited noise. During the processing of noise, spectrotemporal tuning was highly variable across cells. During song processing, the tuning of individual cells became more similar; frequency tuning bandwidth increased, best temporal modulation frequency increased, and spike timing became more precise. The outcome was a population response to song that encoded rapidly changing sounds with power and precision, resulting in a faithful neural representation of the temporal pattern of a song. Modeling responses to song using the tuning to modulation-limited noise showed that the population response would not encode song as precisely or robustly. We conclude that stimulus-dependent changes in auditory tuning during song processing facilitate the high-fidelity encoding of the temporal pattern of a song.

Auditory perception depends on the coding and organization of the information-bearing acoustic features of sounds by auditory neurons. We report here that auditory neurons can be classified into functional groups, each of which plays a specific role in extracting distinct complex sound features. We recorded the electrophysiological responses of single auditory neurons in the songbird midbrain and forebrain to conspecific song, measured their tuning by calculating spectrotemporal receptive fields (STRFs), and classified them using multiple cluster analysis methods. Based on STRF shape, cells clustered into functional groups that divided the space of acoustical features into regions that represent cues for the fundamental acoustic percepts of pitch, timbre, and rhythm. Four major groups were found in the midbrain, and five major groups were found in the forebrain. Comparing STRFs in midbrain and forebrain neurons suggested that both inheritance and emergence of tuning properties occur as information ascends the auditory processing stream.

tion is one of the fundamental components of both human and nonhuman animal behavior. Auditory communication signals (i.e., vocalizations) are especially important in the socioecology of several species of nonhuman primates such as rhesus monkeys. In rhesus, the ventrolateral prefrontal cortex (vPFC) is thought to be part of a circuit involved in representing vocalizations and other auditory objects. To further our understanding of the role of the vPFC in processing vocalizations, we characterized the spectrotemporal features of rhesus vocalizations, compared these features with other classes of natural stimuli, and then related the rhesus-vocalization acoustic features to neural activity. We found that the range of these spectrotemporal features was similar to that found in other ensembles of natural stimuli, including human speech, and identified the subspace of these features that would be particularly informative to discriminate between different vocalizations. In a first neural study, however, we found that the tuning properties of vPFC neurons did not emphasize these particularly informative spectrotemporal features. In a second neural study, we found that a first-order linear model (the spectrotemporal receptive field) is not a good predictor of vPFC activity. The results of these two neural studies are consistent with the hypothesis that the vPFC is not involved in coding the first-order acoustic properties of a stimulus but is involved in processing the higher-order information needed to form representations of auditory objects. I N T R O D U C T I O NCommunication is one of the fundamental components of both human and nonhuman animal behavior (Hauser 1997). Although the benefits and importance of language in human evolution are obvious (Carruthers 2002;Hauser 1997;Lieberman 2002), other nonhuman communication systems are also important. These communication systems are important because for most, if not all, species they are critical to the species' survival (Andersson 1996;Bennett et al. 1997;Greenfield 2002;Hauser 1997;Lau et al. 2000;Mech and Boitani 2003).For example, auditory communication signals (i.e., speciesspecific vocalizations) are especially important in the socioecology of several species of nonhuman primates (Cheney and Seyfarth 1985;Eimas 1994;Eimas et al. 1971;Hauser 1997;Jusczyk 1997;Jusczyk et al. 1983; Miller and Eimas 1995), such as rhesus monkeys (Macaca mulatta). Vocalizations convey information about the identity and the age of the caller and often provide information about sex and emotional or motivational state (Cheney and Seyfarth 1990; Hauser 1997). Some vocalizations transmit information about objects and events in the environment (Gifford 3rd et al. 2003;Hauser 1998;Seyfarth and Cheney 2003).In rhesus monkeys, the ventrolateral prefrontal cortex (vPFC) plays an important role in processing vocalizations (Hackett et al. 1999;Romanski and Goldman-Rakic 2002;Romanski et al. 1999 Romanski et al. , 2005. The vPFC is thought to be part of a circuit involved in representing au...

Several recent studies have shown that hippocampal neurons fire during the delay period in between trials and that these firing patterns differ when different behaviors are required, suggesting that the neuronal responses may be involved in maintaining the memories needed for the upcoming trial. In particular, one study found that hippocampal neurons reliably fired at particular times, referred to as 'episode fields' (EFs), during the delay period of a spatial alternation task (Pastalkova et al, 2008, Science 321:1322. The firing of these neurons resulted in distinct sequential firing patterns on left and right turn trials, and these firing patterns could be used to predict the upcoming behavioral response. In the present study, we examined neuronal firing during the delay period of a hippocampal dependent plus maze task which involved learning to approach two different reward locations (east and west) and we examined the development of these firing patterns with learning. As in the previous study, hippocampal neurons exhibited discrete periods of elevated firing during the delay (EFs) and the firing patterns were distinct on the east and west trials. Moreover, these firing patterns emerged and began to differentiate the east and west conditions during the first training session and continued to develop as the rats learned the task. The finding of similar firing patterns in different tasks suggests that the EFs are a robust phenomenon, which may occur whenever subjects must maintain distinct memory representations during a delay period. Additionally, in the previous study (Pastalkova et al, 2008), the distinct firing patterns could have been due to the differing goal locations, behavioral responses (left or right turns) or trajectories. In the present study, neuronal firing varied with the goal location regardless of the trajectories or responses, suggesting that the firing patterns encode the behavioral context rather than specific behaviors.

The spectro-temporal receptive field (STRF) of an auditory neuron describes the linear relationship between the sound stimulus in a time-frequency representation and the neural response. Time-frequency representations of a sound in turn require a nonlinear operation on the sound pressure waveform and many different forms for this non-linear transformation are possible. Here, we systematically investigated the effects of four factors in the non-linear step in the STRF model: the choice of logarithmic or linear filter frequency spacing, the time-frequency scale, stimulus amplitude compression and adaptive gain control. We quantified the goodness of fit of these different STRF models on data obtained from auditory neurons in the songbird midbrain and forebrain. We found that adaptive gain control and the correct stimulus amplitude compression scheme are paramount to correctly modelling neurons. The time-frequency scale and frequency spacing also affected the goodness of fit of the model but to a lesser extent and the optimal values were stimulus dependent.