Selectivity for spectral motion as a neural computation for encoding natural communication signals in bat inferior colliculus

Abstract

This study examines the neural computations performed by neurons in the auditory system to be selective for the direction and velocity of signals sweeping upward or downward in frequency, termed spectral motion. We show that neurons in the auditory midbrain of Mexican free-tailed bats encode multiple spectrotemporal features of natural communication sounds. These features to which each neuron is tuned are nonlinearly combined to produce selectivity for spectral motion cues present in their conspecific calls, such as direction and velocity. We find that the neural computations resulting in selectivity for spectral motion are analogous to models of motion selectivity studied in vision. Our analysis revealed that auditory neurons in the inferior colliculus (IC) are avoiding spectrotemporal modulations that are redundant across different bat communication signals and are specifically tuned for modulations that distinguish each call from another by their frequency-modulated direction and velocity, suggesting that spectral motion is the neural computation through which IC neurons are encoding specific features of conspecific vocalizations.

Figure 1.

Different levels of neural selectivity in the IC. The top row shows the spectrogram of two example communication calls used by Mexican free-tailed bats. The bottom rows (a–c) display the raster plots of the extracellular response to each call from three IC neurons with their spike waveforms (black) and its average (blue) shown to the right of each row. Each IC neuron displayed a different level of neural selectivity to these calls. Whereas some neurons responded to most syllables in each call, as in a, others showed a higher degree of selectivity and only a subset of these syllables evoked a neural response (b). Other neurons were even more selective, responding strongly to only a single syllable from these two calls, as in c. This shows that each IC neuron is encoding a different set of spectrotemporal features present in these natural social communication signals.

Figure 2.

Extracting the most informative features and their static nonlinearity. To extract the relevant features an IC neuron is encoding in its spiking output, each stimulus segment that preceded a spiking response is collected in the STE shown in a. Taking the average of the STE after correcting for spectrotemporal correlations, or “whitening” the stimuli, resulted in the STA displayed in b. Using both the STA and the STC, we searched for the set of spectrotemporal features that maximized the amount of information they preserved between the stimulus and the spiking response. The plot in c shows the amount of information gained as the number of features considered is increased. The dashed line in c indicates the level of significant information gain determined by nested bootstrap resampling (see Materials and Methods). The three most informative features are shown in the second row (d–f), in which the feature ranked as third resembled the STA. The static nonlinearity associated with each feature is displayed in the last row (g–i). Each nonlinearity shows how the spiking probability changes when the similarity between the stimulus and that feature varies.

Figure 3.

Predictions are improved when multiple stimulus features are considered. The two most informative features for an IC neuron are shown on the left, each with its 1D nonlinearity as well as their combined 2D nonlinearity. The 2D nonlinearity shows how the spiking probability varies when the similarity of the stimulus to both features changes. A spectrogram of a bat courtship song not used in deriving the features is displayed on the right with its evoked response (blue) and predicted response (red) shown in the bottom rows. Mutual information between the response of this neuron and projecting the vocalization through the first and second feature independently was 1.1 and 0.6 bits, respectively. The joint information using both features together increased to 2.2 bits. Therefore, information gain using both features together is greater than the sum of information calculated independently from each feature, resulting in a synergy index of 1.3. The middle row shows the predicted response using only the most informative feature, and its 1D nonlinearity, resulting in a CC of 0.4. Using both features and their combined 2D nonlinearity resulted in the most accurate prediction with a CC of 0.6. This shows that this IC neuron is tuned for multiple spectrotemporal features of natural signals.

Figure 4.

Properties of feature selectivity in the IC. a, The number of significant features each neuron is encoding shows that the majority of IC neurons in our sample were tuned for two or more spectrotemporal features of the stimulus, and only a minority of them (7%; 6 of 87) were tuned for a single feature that was equivalent to the STA. The remaining 81 neurons were significantly tuned for multiple features and their first feature was compared with the second in the subsequent panels. b, Most of these features showed strong inseparability since they were usually tilted either upward or downward indicating a preference for the direction of FM sweeps. The mean inseparability index for the first and second most informative features were both ∼0.6 (n = 81). c, d, Direction selectivity index for the first and second feature shows that they have different directional tuning. While the first feature is biased toward downward (negative) motion with a mean of −0.2, the second feature showed a bimodal distribution with a mean around zero. e, Comparing direction selectivity extracted from features of communication signals to selectivity for the direction of synthetic FM sweeps showed similarity to the selectivity observed in the first feature but not the second. Yet selectivity to sweeps had an even stronger bias for the downward direction with a mean of −0.3.

Figure 5.

Cooperative features for spectral motion selectivity. a, A set of downward and upward FM sweeps with varying velocities was presented to an IC neuron with its spiking response displayed below each sweep. b, Using the most informative features and their nonlinearity extracted from responses to natural stimuli we were able to predict the response of the neuron to only a single sweep velocity of around −150 octaves/s (oct/s) in the downward direction. c, f, Both features were tilted in the downward direction and had a BV of ∼150 oct/s. d, Both features also had a 1D symmetric nonlinearity and their combined 2D nonlinearity suggests their summation. e, Fitting a Gabor function to a smoothed cross-section perpendicular to the BV of the first (black) and second (blue) features revealed that they are offset in phase by 87°. This shows that spectral motion selectivity in this IC neuron could be described by a functional model similar to the energy model previously described in the processing of visual motion.

Figure 6.

Opponent features for spectral motion selectivity. a and b are as described in Figure 5. c, f, The most informative features extracted from responses to natural stimuli showed selectivity for opposing FM directions. d, While both features had a symmetric nonlinearity, the nonlinearity of the second feature was actually suppressive, reducing the response to the upward (nonpreferred) direction as shown in the full 2D nonlinearity. e, Decomposing each feature into its ripple components via a Fourier transform shows that each feature has power within a similar range of spectral and temporal modulations but in opposing quadrants. Furthermore, both features were tuned to the same velocity of 93 oct/s in opposing directions as indicated by the dashed lines.

Figure 7.

Spectral motion selectivity in the population of IC neurons. a, Two populations of IC neurons with symmetric nonlinearities were observed in this study. One with cooperative features tuned for the same direction (black dots; n = 39) and the other with opponent features tuned for opposite directions (gray dots; n = 37). The BV of the first feature is plotted against the BV of the second and shows that both the cooperative as well as the opponent features were tuned for velocities that were highly correlated. This indicates the importance of spectrotemporal asymmetry in two distinct computations of motion selectivity in the IC. b, Neurons that were tuned for cooperative features had a phase shift between them with a mean of 92° and a SD of 22°.

Figure 8.

Comparing spectrotemporal modulations of conspecific signals to neural tuning. a, A spectrogram of a single syllable taken from a courtship call. b, The Fourier transform of the syllable shows that most of the energy is concentrated around the origin with a tail that is oriented along a line that indicates the sweeping velocity of the syllable. c, The contour plot shows the modulation spectrum of all bat calls in our repertoire showing 1/f distribution that is typical of natural signals. The black and red dots designate the peak modulations present in the first and second most informative features of IC neurons, respectively. Note that peak tuning in the IC is organized to detect various FM velocities (dashed lines) while avoiding redundant energy found in most calls. d, f, Modulation spectrum of the most informative features shows that IC tuning is specifically aligned around the common modulations in the calls to be selective for modulations that represent motion cues found in their conspecific signals such as the extended tail in the modulation spectrum of the above syllable. e, Distribution of FM velocities found in the calls match the velocities of the most informative feature representing the velocity tuning of IC neurons. This suggests that IC neurons are tuned to detect spectral motion cues present in their conspecific social communication signals.