High-level neurons processing complex, behaviorally relevant signals are sensitive to conjunctions of features. Characterizing the receptive fields of such neurons is difficult with standard statistical tools, however, and the principles governing their organization remain poorly understood. Here, we demonstrate multiple distinct receptivefield features in individual high-level auditory neurons in a songbird, European starling, in response to natural vocal signals (songs). We then show that receptive fields with similar characteristics can be reproduced by an unsupervised neural network trained to represent starling songs with a single learning rule that enforces sparseness and divisive normalization. We conclude that central auditory neurons have composite receptive fields that can arise through a combination of sparseness and normalization in neural circuits. Our results, along with descriptions of random, discontinuous receptive fields in the central olfactory neurons in mammals and insects, suggest general principles of neural computation across sensory systems and animal classes. auditory system | neural networks | receptive fields | sparseness | unsupervised learning H ow neurons efficiently represent multidimensional stimuli is an important question in sensory neuroscience. Dimensionality reduction involves extracting a hierarchy of features to obtain a selective and invariant (categorical) representation useful for behavior. To understand better the principles underlying this process in the central auditory system, we characterized receptive fields of neurons in the caudo-medial nidopallium (NCM) of the European starling (Sturnus vulgaris), a songbird with an acoustically rich vocal repertoire (1). The NCM, a secondary auditory cortexlike region in songbirds, receives convergent inputs from the primary thalamorecipient region, Field L, and other secondary auditory regions (2) and contains neurons selectively tuned to birdsong, a behaviorally relevant natural stimulus (3-5). We recorded action potentials extracellularly from individual well-isolated NCM neurons during the playback of starling songs and estimated the structure of the neurons' receptive fields using the Maximum Noise Entropy (MNE) method (6). Statistical inference methods in this class (7, 8) maximize the noise entropy of the conditional response distribution to produce models that are constrained by a given set of stimulus-response correlations but that are otherwise as random, and therefore as unbiased, as possible. Unlike the spike-triggered covariance (STC) method (9), MNE works well with natural stimuli; in contrast to the maximally informative dimensions (MID) method (10), MNE can identify any number of relevant receptive-field features. Results Song. We recorded neuronal responses to six different 1-minutelong songs, each repeated 30 times. These songs were recorded from three male starlings, and together they contained over 200 motifs, brief segments of starling song that are perceived as distinct auditory objects (11). An NCM neuron usually responds to a variety of motifs (4, 12) (Fig. 1) , and NCM neurons display rapid stimulusspecific adaptation (13), suggesting that an individual neuron can be sensitive to a variety of different stimulus features. To examine whether single NCM neurons respond to multiple distinct features of starling song, we obtained significant eigenvectors of the second-order MNE model's matrix J for each neuron that together with the first-order term (Methods) define its receptive field (Fig. 2) . On average, NCM neurons' receptive fields (n = 37 neurons) contained six excitatory features, or negative eigenvalues of the matrix J (6.43 ± 2.06, range 2-11, interquartile range 5-8), and six suppressive features, or positive eigenvalues of the matrix J (6.35 ± 2.41, range 3-12, interquartile range 5-7). As a control, we also determined the number of features using all possible five-song subsets of the six-song set. The distributions of significant features obtained using five or six songs were not statistically different (Kolmogorov-Smirnov test, P = 0.3 and P = 0.2 for the negative and positive eigenvalues, respectively). Using many more songs, however, did reveal additional features (see the next section). The features of each neuron's receptive field were spectrotemporally diverse: the neurons typically combined broadband features resembling clicks and narrowband features resembling tones, or harmonic stacks. The spectral and temporal statistics of the ensemble of features were captured using the modulation power spectrum (14) (Fig. 3) . Thus, we found multiple, distinct receptive-field features in individual highlevel auditory neurons in response to natural stimuli. To reflect their multifeature composition, we call these receptive fields composite. To verify the model's ability to predict responses to new stimuli, we estimated its parameters for each neuron using all possible fivesong subsets and generated a prediction of the probability of a spike Significance How neurons are selective for complex natural stimuli remains poorly understood, in part because standard statistical tools only identify one or two features of stimuli but not complete sets. Here, using a statistical method that overcomes these difficulties, we demonstrate that a set of multiple distinct acoustical features exists in individual auditory neurons in songbirds. We then use birdsongs to train an unsupervised neural network constrained by two common properties of biological neural circuits. The network rediscovers the same stimulus features observed in vivo. These results demonstrate that individual highlevel auditory neurons respond not to single, but to multiple features of natural stimuli. This enables a robust, statistically optimal, representation of complex, real-world signals such as birdsong, speech, or music.
Another recent avian forebrain study REF used a maximum noise entropy (MNE) approach to uncover multiple receptive fields sensitive to second-order aspects of the stimulus.
Central auditory neurons have composite receptive fields
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Computer Science Medicine