Auditory abstraction from spectro-temporal features to coding auditory entities

Abstract

The auditory system extracts behaviorally relevant information from acoustic stimuli. The average activity in auditory cortex is known to be sensitive to spectro-temporal patterns in sounds. However, it is not known whether the auditory cortex also processes more abstract features of sounds, which may be more behaviorally relevant than spectro-temporal patterns. Using recordings from three stations of the auditory pathway, the inferior colliculus (IC), the ventral division of the medial geniculate body (MGB) of the thalamus, and the primary auditory cortex (A1) of the cat in response to natural sounds, we compared the amount of information that spikes contained about two aspects of the stimuli: spectro-temporal patterns, and abstract entities present in the same stimuli such as a bird chirp, its echoes, and the ambient noise. IC spikes conveyed on average approximately the same amount of information about spectro-temporal patterns as they conveyed about abstract auditory entities, but A1 and the MGB neurons conveyed on average three times more information about abstract auditory entities than about spectro-temporal patterns. Thus, the majority of neurons in auditory thalamus and cortex coded well the presence of abstract entities in the sounds without containing much information about their spectro-temporal structure, suggesting that they are sensitive to abstract features in these sounds.

Fig. 1.

A schematic diagram of the studied sounds and their representations. The original stimuli are processed in two ways. On the left, stimuli are transformed into a ST representation, segmented and grouped into clusters. On the right, stimuli are grouped by the presence of abstract auditory entities.

Fig. 4.

Coding of ST patterns vs. coding auditory entities. (A) Distribution of mutual information values across the population of neurons in the IC for coding ST clusters and auditory entities. Horizontal bars are centered on the mean of the distributions, and their length is twice the SEM (one SE on each side of the mean). The two distributions have similar means (t = −0.94, df = 52, P = 0.34). (B) Same as A, for A1 neurons. The two distributions have different means (t = −2.9, df = 88, P < 4 × 10⁻³). (C) Each circle denotes the normalized MI for one A1 neuron. The MI between ST patterns and responses was estimated by using 4-ms spike patterns; the MI about entities was computed by using spike patterns of variable length (optimized for each neuron separately). The rightmost point was drawn out of scale for clarity and marked by a diamond to emphasize that point. The black rectangle denotes the mean of the distribution. The numbers of neurons below and above the equality line are displayed.

Fig. 2.

A quantization approach to study coding ST patterns. To compute the joint distribution of spike trains (represented as a binary pattern) and the stimulus (a high dimensional continuous variable), the stimuli were first transformed into a series of discrete symbols, each corresponding to a “typical” ST pattern. (A) Fifty-millisecond overlapping segments were collected from all stimuli. (B) Segments were grouped into 32 classes by using k-means clustering (45). The means of four of these classes are displayed as examples. All class means are displayed in Fig. S2. (C) An illustration of how the joint count matrix of stimuli and responses was computed. Every stimulus was represented as a series of symbols by matching each segment to its nearest class mean. This process generated a symbol once every millisecond. The corresponding response of a neuron was represented as a series of short binary patterns, corresponding to the occurrence of spike patterns that occurred just after the occurrence of the pattern. The joint count matrix tallied the occurrences of every response (spiking pattern) after a stimulus (segment class). In this example, spike patterns are of length 4.

Fig. 3.

Information about auditory entities. Five stimuli were mapped to three classes according to the entities they contain (A and B). The responses (C) are represented as binary patterns for computing the information.

Fig. 5.

Responses of three entity-sensitive neurons. Raster plots showing spike times across 20 repetitions of the stimulus, and the corresponding stimulus.