A Generalized Linear Model for Estimating Spectrotemporal Receptive Fields from Responses to Natural Sounds

Calabrese, Ana; Schumacher, Joseph W.; Schneider, David M.; Paninski, Liam; Woolley, Sarah M. N.

doi:10.1371/journal.pone.0016104

Cited by 114 publications

(136 citation statements)

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“…In the last step the output of the linear filter u(ti) is transformed into the predicted response using a static non-linearity g(). Generalized linear models can be used to estimate simultaneously the STRF and this non-linear output function for different noise distributions [52]. Figure 4.…”

Section: Resultsmentioning

confidence: 99%

“…For example, sound representations can include known non-linearities such as adaptive mechanisms [49,50] or probabilistic expectations [51]. Output non-linearities such as those produced by a spiking threshold can be very efficiently modeled using the generalized linear framework [52] even in combination with input-nonlinearities [53]. Finally, dynamical second order or higher-order non-linearities have been estimated with techniques that yield multi-component STRFs [47,[54][55][56][57].…”

Section: Methods For Estimating Strfs Using Natural Soundsmentioning

confidence: 99%

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Neural processing of natural sounds

2014

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Natural sounds have unique statistical structure that makes them perceptually salient. The auditory system is finely tuned to this natural structure. Selective neurons found at the higher levels of the auditory system respond selectively to vocalizations used in communication. On-line Summary1. Natural sounds include animal vocalizations, environmental sounds such as wind, water and fire noises and non-vocal sounds made by animals and humans for communication. These natural sounds have characteristic statistical properties that make them perceptually salient and that drive auditory neurons in optimal regimes for information transmission. 2.Recent advances in statistics and computer sciences have allowed neuro-physiologists to extract the stimulus-response function of complex auditory neurons from responses to natural sounds. These studies have shown a hierarchical processing that leads to the neural detection of progressively more complex natural sound features and have demonstrated the importance of the acoustical and behavioral contexts for the neural responses.3. High-level auditory neurons have shown to be exquisitely selective for conspecific calls.This fine selectivity could play an important role for species recognition, for vocal learning in songbirds and, in the case of the bats, for the processing of the sounds used in echolocation. Research that investigates how communication sounds are categorized into behaviorally meaningful groups (e.g. call types in animals, words in human speech) remains in its infancy. Animals and humans also excel at separating communication sounds from each otherand from background noise. Neurons that detect communication calls in noise have been found but the neural computations involved in sound source separation and natural auditory scene analysis remain overall poorly understood. Thus, future auditory research will have to focus not only on how natural sounds are processed by the auditory system but also on the computations that allow for this processing to occur in natural listening situations. 5.The complexity of the computations needed in the natural hearing task might require a high-dimensional representation provided by ensemble of neurons and the use of natural sounds might be the best solution for understanding the ensemble neural code.The final publication is available at Nature via http://www.nature.com/nrn/journal/v15/n6/full/nrn3731.html PrefaceWe might be forced to listen to a high frequency tone at our audiologist's office or we might enjoy falling asleep with a white-noise machine but the sounds that really matter to us are the voices of our companions or music from our favorite radio station; the auditory system has evolved to process behaviorally relevant natural sounds. Research has shown not only that our brain is optimized for natural hearing tasks but also that using natural sounds to probe the auditory system is the best way to understand the neural computations that allow us to comprehend speech or appreciate music.

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Section: Resultsmentioning

confidence: 99%

Section: Methods For Estimating Strfs Using Natural Soundsmentioning

confidence: 99%

Neural processing of natural sounds

2014

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“…Selectivity was computed as the fraction of different stimuli that did not elicit a significant response from a given neuron. Spectrotemporal receptive fields (STRFs) were computed by fitting a generalized linear model to the sound-evoked responses of each neuron (60). The response latency for each neuron was computed from the STRF, at the best excitatory frequency (61) (SI Materials and Methods).…”

Section: Methodsmentioning

confidence: 99%

Coding principles of the canonical cortical microcircuit in the avian brain

Calabrese

Woolley

2015

Proc. Natl. Acad. Sci. U.S.A.

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113

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Mammalian neocortex is characterized by a layered architecture and a common or "canonical" microcircuit governing information flow among layers. This microcircuit is thought to underlie the computations required for complex behavior. Despite the absence of a six-layered cortex, birds are capable of complex cognition and behavior. In addition, the avian auditory pallium is composed of adjacent information-processing regions with genetically identified neuron types and projections among regions comparable with those found in the neocortex. Here, we show that the avian auditory pallium exhibits the same information-processing principles that define the canonical cortical microcircuit, long thought to have evolved only in mammals. These results suggest that the canonical cortical microcircuit evolved in a common ancestor of mammals and birds and provide a physiological explanation for the evolution of neural processes that give rise to complex behavior in the absence of cortical lamination.functional connectivity | cortex evolution | songbird | sensory coding T he cognitive abilities of birds suggest that the avian brain contains sophisticated information-processing circuitry. Recent studies have demonstrated that corvids, such as crows, rooks, and jays, exhibit innovative tool manufacture (1), referential gesturing (2), causal reasoning (3, 4), mirror self-recognition (5), and planning for future needs using recent experience (6). Other birds can perform complex pattern recognition (7), long-term recollection (8), and numerical discrimination (9), paralleling the performance of primates. Songbirds, such as zebra finches (Taeniopygia guttata, the species studied here), learn to produce and recognize complex vocalizations for social communication, with numerous parallels to speech learning and perception (10).Although cognitive skills in birds and nonhuman mammals are often comparable, avian and mammalian palliums exhibit very different anatomical organization. Although the organization of ascending sensory pathways and subpallial structures are similar in avian and mammalian brains (11), the avian pallium (referred to hereafter as the cortex) lacks the distinctive six-layered structure of the mammalian neocortex (12). Instead of a laminar structure, the avian cortex is composed of interconnected nuclei and bands of neurons that form distinct processing regions. The avian primary auditory cortex (avian A1) is organized into adjacent processing regions, collectively called the field L/CM (caudal mesopallium) complex, that are stacked in a dorsorostral to ventrocaudal orientation (13, 14) ( Fig. 1 and Fig. S1). Regions of avian A1 are delineated by differences in cytoarchitecture and connectivity (13, 15) (SI Materials and Methods) and are organized into superficial (field L1 and lateral caudal mesopallium, CML), intermediate (field L2a and -b), and deep (field L3) regions, similar to neocortical layers. A posterior region, the caudal nidopallium (NC) is referred to as "secondary" cortex here. Fig. 2A shows a schemati...

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“…3A). Our MTRF estimation was based on a recent technique called generalized linear model (GLM) (Calabrese et al 2011;Paninski 2004;Steinberg et al 2013) that allows the estimation of receptive fields when the input statistics (in this case the envelope at the output of the cochlear channel) is not white noise. Our GLM implementation is a single-channel monaural found through spike-triggered techniques.…”

Section: Methodsmentioning

confidence: 99%

Emergence of band-pass filtering through adaptive spiking in the owl's cochlear nucleus

Fontaine

MacLeod

Lubejko

et al. 2014

Journal of Neurophysiology

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In the visual, auditory, and electrosensory modalities, stimuli are defined by first-and second-order attributes. The fast time-pressure signal of a sound, a first-order attribute, is important, for instance, in sound localization and pitch perception, while its slow amplitude-modulated envelope, a second-order attribute, can be used for sound recognition. Ascending the auditory pathway from ear to midbrain, neurons increasingly show a preference for the envelope and are most sensitive to particular envelope modulation frequencies, a tuning considered important for encoding sound identity. The level at which this tuning property emerges along the pathway varies across species, and the mechanism of how this occurs is a matter of debate. In this paper, we target the transition between auditory nerve fibers and the cochlear nucleus angularis (NA). While the owl's auditory nerve fibers simultaneously encode the fast and slow attributes of a sound, one synapse further, NA neurons encode the envelope more efficiently than the auditory nerve. Using in vivo and in vitro electrophysiology and computational analysis, we show that a single-cell mechanism inducing spike threshold adaptation can explain the difference in neural filtering between the two areas. We show that spike threshold adaptation can explain the increased selectivity to modulation frequency, as input level increases in NA. These results demonstrate that a spike generation nonlinearity can modulate the tuning to second-order stimulus features, without invoking network or synaptic mechanisms.band-pass filtering; envelope encoding; cochlear nucleus; threshold adaptation SENSORY SYSTEMS HAVE EVOLVED to efficiently represent the statistics of natural stimuli (Barlow 1961). In the auditory system, the cochlea decomposes sound into narrow frequency channels. The output of every cochlear channel can be characterized by its first-order attribute, the time-dependent pressure wave or fine-structure, and second-order attribute, the instantaneous amplitude or envelope (Attias and Schreiner

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A Generalized Linear Model for Estimating Spectrotemporal Receptive Fields from Responses to Natural Sounds

Cited by 114 publications

References 56 publications

Neural processing of natural sounds

Neural processing of natural sounds

Coding principles of the canonical cortical microcircuit in the avian brain

Emergence of band-pass filtering through adaptive spiking in the owl's cochlear nucleus

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