Representation of Acoustic Communication Signals by Insect Auditory Receptor Neurons

Machens, Christian K.; Stemmler, Martin B.; Prinz, P.; Krahe, Rüdiger; Ronacher, Bernhard; Herz, Andreas V. M.

doi:10.1523/jneurosci.21-09-03215.2001

Cited by 134 publications

(91 citation statements)

References 33 publications

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“…In insects, auditory receptors respond synchronously at the onsets of temporally distinct sounds, producing a neural population response that faithfully mimics the basic temporal pattern of the stimulus (Mason and Faure, 2004). Machens et al (2001) found that using the population response of grasshoppers' auditory receptors increased the accuracy of temporal encoding over single responses such that the neural sensitivity to temporal information approached behavioral levels. Similarly, our findings suggest that the auditory midbrain population response could be used to obtain a reliable representation of the amplitude envelope of a song, which is a highly informative stimulus parameter .…”

Section: Stimulus-dependent Tuning Population Coding and Ensemble Smentioning

confidence: 97%

“…But little experimental work has addressed this issue. Population coding of temporal patterns that characterize natural communication sounds has been demonstrated in mammals (Wang et al, 1995;Gehr et al, 2000), fish (Bodnar et al, 2001), birds , and grasshoppers (Machens et al, 2001). The direct relationship between the perception of communication sounds and how populations of neurons represent such sounds remains to be determined.…”

Section: Coding Temporal Information and Acoustic Communicationmentioning

confidence: 99%

“…Correspondingly, behavioral and physiological studies suggest a strong relationship between the accurate perception of communication sounds and temporal auditory processing (Tallal et al, 1993;Shannon et al, 1995;McAnally and Stein, 1996;Wright et al, 1997;Woolley and Rubel, 1999;Escabi et al, 2003;Marsat and Pollack, 2005;. Natural sounds such as vocalizations and/or sounds that are "natural-like" evoke more informative responses (e.g., higher information rates) from auditory neurons (Rieke et al, 1995;Machens et al, 2001;Escabi et al, 2003;Hsu et al, 2004). But how single neurons and neuronal populations code natural sounds differently from other sounds such as noise is not known.…”

Section: Introductionmentioning

confidence: 99%

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Stimulus-Dependent Auditory Tuning Results in Synchronous Population Coding of Vocalizations in the Songbird Midbrain

2006

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Physiological studies in vocal animals such as songbirds indicate that vocalizations drive auditory neurons particularly well. But the neural mechanisms whereby vocalizations are encoded differently from other sounds in the auditory system are unknown. We used spectrotemporal receptive fields (STRFs) to study the neural encoding of song versus the encoding of a generic sound, modulationlimited noise, by single neurons and the neuronal population in the zebra finch auditory midbrain. The noise was designed to match song in frequency, spectrotemporal modulation boundaries, and power. STRF calculations were balanced between the two stimulus types by forcing a common stimulus subspace. We found that 91% of midbrain neurons showed significant differences in spectral and temporal tuning properties when birds heard song and when birds heard modulation-limited noise. During the processing of noise, spectrotemporal tuning was highly variable across cells. During song processing, the tuning of individual cells became more similar; frequency tuning bandwidth increased, best temporal modulation frequency increased, and spike timing became more precise. The outcome was a population response to song that encoded rapidly changing sounds with power and precision, resulting in a faithful neural representation of the temporal pattern of a song. Modeling responses to song using the tuning to modulation-limited noise showed that the population response would not encode song as precisely or robustly. We conclude that stimulus-dependent changes in auditory tuning during song processing facilitate the high-fidelity encoding of the temporal pattern of a song.

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Section: Stimulus-dependent Tuning Population Coding and Ensemble Smentioning

confidence: 97%

Section: Coding Temporal Information and Acoustic Communicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stimulus-Dependent Auditory Tuning Results in Synchronous Population Coding of Vocalizations in the Songbird Midbrain

2006

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“…Another common, but less direct, measure that has been proposed to assess the role of correlations is the synergy/redundancy measure, denoted ⌬I synergy (Brenner et al, 2000;Machens et al, 2001;Schneidman et al, 2003). It is defined to be…”

Section: Synergy and Redundancymentioning

confidence: 99%

Synergy, Redundancy, and Independence in Population Codes, Revisited

Latham¹,

Nirenberg²

2005

J. Neurosci.

200

239

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Decoding the activity of a population of neurons is a fundamental problem in neuroscience. A key aspect of this problem is determining whether correlations in the activity, i.e., noise correlations, are important. If they are important, then the decoding problem is high dimensional: decoding algorithms must take the correlational structure in the activity into account. If they are not important, or if they play a minor role, then the decoding problem can be reduced to lower dimension and thus made more tractable. The issue of whether correlations are important has been a subject of heated debate. The debate centers around the validity of the measures used to address it. Here, we evaluate three of the most commonly used ones: synergy, ⌬I shuffled , and ⌬I. We show that synergy and ⌬I shuffled are confounded measures: they can be zero when correlations are clearly important for decoding and positive when they are not. In contrast, ⌬I is not confounded. It is zero only when correlations are not important for decoding and positive only when they are; that is, it is zero only when one can decode exactly as well using a decoder that ignores correlations as one can using a decoder that does not, and it is positive only when one cannot decode as well. Finally, we show that ⌬I has an information theoretic interpretation; it is an upper bound on the information lost when correlations are ignored.

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“…Natural stimuli for grasshoppers, such as their courtship songs, usually contain broad carrier-frequency bands (von Helversen and von Helversen, 1994;Stumpner and von Helversen, 2001). The coding properties of grasshopper auditory receptors have been shown to be particularly adapted to specific aspects of these stimuli (Meyer and Elsner, 1996;Machens et al, 2001) and to allow discrimination between slightly different songs (Machens et al, 2003). Investigating input-driven adaptation using these more complex signals may thus shed light on how this adaptation component affects neural coding.…”

Section: Possible Functions Of Input-driven Adaptationmentioning

confidence: 99%

Input-Driven Components of Spike-Frequency Adaptation Can Be UnmaskedIn Vivo

Gollisch¹,

Herz²

2004

J. Neurosci.

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Spike-frequency adaptation affects the response characteristics of many sensory neurons, and different biophysical processes contribute to this phenomenon. Many cellular mechanisms underlying adaptation are triggered by the spike output of the neuron in a feedback manner (e.g., specific potassium currents that are primarily activated by the spiking activity). In contrast, other components of adaptation may be caused by, in a feedforward way, the sensory or synaptic input, which the neuron receives. Examples include viscoelasticity of mechanoreceptors, transducer adaptation in hair cells, and short-term synaptic depression. For a functional characterization of spike-frequency adaptation, it is essential to understand the dependence of adaptation on the input and output of the neuron. Here, we demonstrate how an input-driven component of adaptation can be uncovered in vivo from recordings of spike trains in an insect auditory receptor neuron, even if the total adaptation is dominated by output-driven components. Our method is based on the identification of different inputs that yield the same output and sudden switches between these inputs. In particular, we determined for different sound frequencies those intensities that are required to yield a predefined steady-state firing rate of the neuron. We then found that switching between these sound frequencies causes transient deviations of the firing rate. These firing-rate deflections are evidence of input-driven adaptation and can be used to quantify how this adaptation component affects the neural activity. Based on previous knowledge of the processes in auditory transduction, we conclude that for the investigated auditory receptor neurons, this adaptation phenomenon is of mechanical origin.

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Representation of Acoustic Communication Signals by Insect Auditory Receptor Neurons

Cited by 134 publications

References 33 publications

Stimulus-Dependent Auditory Tuning Results in Synchronous Population Coding of Vocalizations in the Songbird Midbrain

Stimulus-Dependent Auditory Tuning Results in Synchronous Population Coding of Vocalizations in the Songbird Midbrain

Synergy, Redundancy, and Independence in Population Codes, Revisited

Input-Driven Components of Spike-Frequency Adaptation Can Be UnmaskedIn Vivo

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