Noise changes receptive fields of auditory neurons in A1

David T. Blake; Michael M. Merzenich

doi:10.1121/1.4779180

Spectral-Temporal Receptive Fields of Nonlinear Auditory Neurons Obtained Using Natural Sounds

Journal of Neuroscience ◽

10.1523/jneurosci.20-06-02315.2000 ◽

2000 ◽

Vol 20 (6) ◽

pp. 2315-2331 ◽

Cited By ~ 312

Author(s):

Frédéric E. Theunissen ◽

Kamal Sen ◽

Allison J. Doupe

Keyword(s):

Receptive Fields ◽

Natural Sounds ◽

Auditory Neurons

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Representations of Conspecific Song by Starling Secondary Forebrain Auditory Neurons: Toward a Hierarchical Framework

Journal of Neurophysiology ◽

10.1152/jn.00464.2009 ◽

2010 ◽

Vol 103 (3) ◽

pp. 1195-1208 ◽

Cited By ~ 25

Author(s):

C. Daniel Meliza ◽

Zhiyi Chi ◽

Daniel Margoliash

Keyword(s):

Predictive Power ◽

Functional Organization ◽

Receptive Fields ◽

Spike Timing ◽

Auditory Neurons ◽

Linear Summation ◽

European Starlings ◽

Spike Timing Precision ◽

Spectrotemporal Receptive Field ◽

Spike Waveforms

The functional organization giving rise to stimulus selectivity in higher-order auditory neurons remains under active study. We explored the selectivity for motifs, spectrotemporally distinct perceptual units in starling song, recording the responses of 96 caudomedial mesopallium (CMM) neurons in European starlings ( Sturnus vulgaris) under awake-restrained and urethane-anesthetized conditions. A subset of neurons was highly selective between motifs. Selectivity was correlated with low spontaneous firing rates and high spike timing precision, and all but one of the selective neurons had similar spike waveforms. Neurons were further tested with stimuli in which the notes comprising the motifs were manipulated. Responses to most of the isolated notes were similar in amplitude, duration, and temporal pattern to the responses elicited by those notes in the context of the motif. For these neurons, we could accurately predict the responses to motifs from the sum of the responses to notes. Some notes were suppressed by the motif context, such that removing other notes from motifs unmasked additional excitation. Models of linear summation of note responses consistently outperformed spectrotemporal receptive field models in predicting responses to song stimuli. Tests with randomized sequences of notes confirmed the predictive power of these models. Whole notes gave better predictions than did note fragments. Thus in CMM, auditory objects (motifs) can be represented by a linear combination of excitation and suppression elicited by the note components of the object. We hypothesize that the receptive fields arise from selective convergence by inputs responding to specific spectrotemporal features of starling notes.

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Central auditory neurons have composite receptive fields

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1506903113 ◽

2016 ◽

Vol 113 (5) ◽

pp. 1441-1446 ◽

Cited By ~ 33

Author(s):

Andrei S. Kozlov ◽

Timothy Q. Gentner

Keyword(s):

Neural Circuits ◽

Receptive Fields ◽

Learning Rule ◽

Auditory Neurons ◽

Olfactory Neurons ◽

Unsupervised Neural Network ◽

Divisive Normalization ◽

Vocal Signals ◽

Central Auditory Neurons ◽

High Level

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 receptive-field 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.

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