Sparse Codes for Speech Predict Spectrotemporal Receptive Fields in the Inferior Colliculus

We have developed a sparse mathematical representation of speech that minimizes the number of active model neurons needed to represent typical speech sounds. The model learns several well-known acoustic features of speech such as harmonic stacks, formants, onsets and terminations, but we also find more exotic structures in the spectrogram representation of sound such as localized checkerboard patterns and frequency-modulated excitatory subregions flanked by suppressive sidebands. Moreover, several of these novel features resemble neuronal receptive fields reported in the Inferior Colliculus (IC), as well as auditory thalamus and cortex, and our model neurons exhibit the same tradeoff in spectrotemporal resolution as has been observed in IC. To our knowledge, this is the first demonstration that receptive fields of neurons in the ascending mammalian auditory pathway beyond the auditory nerve can be predicted based on coding principles and the statistical properties of recorded sounds.Comment: For Supporting Information, see PLoS website: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.100259

Efficient coding of spectrotemporal binaural sounds leads to emergence of the auditory space representation

To date a number of studies have shown that receptive field shapes of early sensory neurons can be reproduced by optimizing coding efficiency of natural stimulus ensembles. A still unresolved question is whether the efficient coding hypothesis explains formation of neurons which explicitly represent environmental features of different functional importance. This paper proposes that the spatial selectivity of higher auditory neurons emerges as a direct consequence of learning efficient codes for natural binaural sounds. Firstly, it is demonstrated that a linear efficient coding transform - Independent Component Analysis (ICA) trained on spectrograms of naturalistic simulated binaural sounds extracts spatial information present in the signal. A simple hierarchical ICA extension allowing for decoding of sound position is proposed. Furthermore, it is shown that units revealing spatial selectivity can be learned from a binaural recording of a natural auditory scene. In both cases a relatively small subpopulation of learned spectrogram features suffices to perform accurate sound localization. Representation of the auditory space is therefore learned in a purely unsupervised way by maximizing the coding efficiency and without any task-specific constraints. This results imply that efficient coding is a useful strategy for learning structures which allow for making behaviorally vital inferences about the environment.Comment: 22 pages, 9 figure

Emergence of Tuning to Natural Stimulus Statistics along the Central Auditory Pathway

We have previously shown that neurons in primary auditory cortex (A1) of anaesthetized (ketamine/medetomidine) ferrets respond more strongly and reliably to dynamic stimuli whose statistics follow "natural" 1/f dynamics than to stimuli exhibiting pitch and amplitude modulations that are faster (1/f(0.5)) or slower (1/f(2)) than 1/f. To investigate where along the central auditory pathway this 1/f-modulation tuning arises, we have now characterized responses of neurons in the central nucleus of the inferior colliculus (ICC) and the ventral division of the mediate geniculate nucleus of the thalamus (MGV) to 1/f(gamma) distributed stimuli with gamma varying between 0.5 and 2.8. We found that, while the great majority of neurons recorded from the ICC showed a strong preference for the most rapidly varying (1/f(0.5) distributed) stimuli, responses from MGV neurons did not exhibit marked or systematic preferences for any particular gamma exponent. Only in A1 did a majority of neurons respond with higher firing rates to stimuli in which gamma takes values near 1. These results indicate that 1/f tuning emerges at forebrain levels of the ascending auditory pathway

Neurons with stereotyped and rapid responses provide a reference frame for relative temporal coding in primate auditory cortex

The precise timing of spikes of cortical neurons relative to stimulus onset carries substantial sensory information. To access this information the sensory systems would need to maintain an internal temporal reference that reflects the precise stimulus timing. Whether and how sensory systems implement such reference frames to decode time-dependent responses, however, remains debated. Studying the encoding of naturalistic sounds in primate (Macaca mulatta) auditory cortex we here investigate potential intrinsic references for decoding temporally precise information. Within the population of recorded neurons, we found one subset responding with stereotyped fast latencies that varied little across trials or stimuli, while the remaining neurons had stimulus-modulated responses with longer and variable latencies. Computational analysis demonstrated that the neurons with stereotyped short latencies constitute an effective temporal reference for relative coding. Using the response onset of a simultaneously recorded stereotyped neuron allowed decoding most of the stimulus information carried by onset latencies and the full spike train of stimulus-modulated neurons. Computational modeling showed that few tens of such stereotyped reference neurons suffice to recover nearly all information that would be available when decoding the same responses relative to the actual stimulus onset. These findings reveal an explicit neural signature of an intrinsic reference for decoding temporal response patterns in the auditory cortex of alert animals. Furthermore, they highlight a role for apparently unselective neurons as an early saliency signal that provides a temporal reference for extracting stimulus information from other neurons

Amazon Nights II: Electric Boogaloo-Neural Adaptations for Communication in Three Species of Weakly Electric FIsh

Sensory systems have to extract useful information from environments awash in noise and confounding input. Studying how salient signals are encoded and filtered from these natural backgrounds is a key problem in neuroscience. Communication is a particularly tractable tool for studying this problem, as it is a ubiquitous task that all organisms must accomplish, easily compared across species, and is of significant ethological relevance. In this chapter I describe the current knowledge of what is both known and still unknown about how sensory systems are adapted for the challenges of encoding conspecific signals, particularly in environments complicated by conspecific-generated noise. The second half of this chapter describes why weakly electric fish are particularly suited to investigating how communication can shape the nervous system to accomplish this task

Cortical And Subcortical Mechanisms For Sound Processing

The auditory cortex is essential for encoding complex and behaviorally relevant sounds. Many questions remain concerning whether and how distinct cortical neuronal subtypes shape and encode both simple and complex sound properties. In chapter 2, we tested how neurons in the auditory cortex encode water-like sounds perceived as natural by human listeners, but that we could precisely parametrize. The stimuli exhibit scale-invariant statistics, specifically temporal modulation within spectral bands scaled with the center frequency of the band. We used chronically implanted tetrodes to record neuronal spiking in rat primary auditory cortex during exposure to our custom stimuli at different rates and cycle-decay constants. We found that, although neurons exhibited selectivity for subsets of stimuli with specific statistics, over the population responses were stable. These results contribute to our understanding of how auditory cortex processes natural sound statistics. In chapter 3, we review studies examining the role of different cortical inhibitory interneurons in shaping sound responses in auditory cortex. We identify the findings that support each other and the mechanisms that remain unexplored. In chapter 4, we tested how direct feedback from auditory cortex to the inferior colliculus modulated sound responses in the inferior colliculus. We optogenetically activated or suppressed cortico-collicular feedback while recording neuronal spiking in the mouse inferior colliculus in response to pure tones and dynamic random chords. We found that feedback modulated sound responses by reducing sound selectivity by decreasing responsiveness to preferred frequencies and increasing responsiveness to less preferred frequencies. Furthermore, we tested the effects of perturbing intra-cortical inhibitory-excitatory networks on sound responses in the inferior colliculus. We optogenetically activated or suppressed parvalbumin-positive (PV) and somatostatin-positive (SOM) interneurons while recording neuronal spiking in mouse auditory cortex and inferior colliculus. We found that modulation of neither PV- nor SOM-interneurons affected sound-evoked responses in the inferior colliculus, despite significant modulation of cortical responses. Our findings imply that cortico-collicular feedback can modulate responses to simple and complex auditory stimuli independently of cortical inhibitory interneurons. These experiments elucidate the role of descending auditory feedback in shaping sound responses. Together these results implicate the importance of the auditory cortex in sound processing

Representation of acoustic communication signals by insect auditory receptor neurons

Despite their simple auditory systems, some insect species recognize certain temporal aspects of acoustic stimuli with an acuity equal to that of vertebrates; however, the underlying neural mechanisms and coding schemes are only partially understood. In this study, we analyze the response characteristics of the peripheral auditory system of grasshoppers with special emphasis on the representation of species-specific communication signals. We use both natural calling songs and artificial random stimuli designed to focus on two low-order statistical properties of the songs: their typical time scales and the distribution of their modulation amplitudes. Based on stimulus reconstruction techniques and quantified within an information-theoretic framework, our data show that artificial stimuli with typical time scales of >40 msec can be read from single spike trains with high accuracy. Faster stimulus variations can be reconstructed only for behaviorally relevant amplitude distributions. The highest rates of information transmission (180 bits/sec) and the highest coding efficiencies (40%) are obtained for stimuli that capture both the time scales and amplitude distributions of natural songs. Use of multiple spike trains significantly improves the reconstruction of stimuli that vary on time scales <40 msec or feature amplitude distributions as occur when several grasshopper songs overlap. Signal-to-noise ratios obtained from the reconstructions of natural songs do not exceed those obtained from artificial stimuli with the same low-order statistical properties. We conclude that auditory receptor neurons are optimized to extract both the time scales and the amplitude distribution of natural songs. They are not optimized, however, to extract higher-order statistical properties of the song-specific rhythmic patterns