Single auditory neurons rapidly discriminate conspecific communication signals

Machens, Christian K.; Schütze, Hartmut; Franz, A.; Kolesnikova, Olga; Stemmler, Martin B.; Ronacher, Bernhard; Herz, Andreas V. M.

doi:10.1038/nn1036

Cited by 107 publications

(93 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notice that the maximal mutual information and the percentage of correct classifications are not biunivocally related (Thomson and Kristan, 2005) and quantify different aspects of the representation of the stimuli in the responses. Machens et al (2003) indicated that the use of different supervised and unsupervised classifiers did not affect the results. However, other studies using the percentage of correct classifications to evaluate the discrimination do not report any comparison with other classifiers (Grewe et al, 2003;Narayan et al, 2006;Wohlgemuth and Ronacher, 2007;Wang et al, 2007;Billimoria et al, 2008).…”

Section: The Dependence On the Classifier And The Measurementioning

confidence: 99%

“…Furthermore, for the comparison of I * max (S P , S) and I * count (S P , S), both should be maximized separately. Alternatively, to avoid the parametric dependence on the exponent another classifier has been commonly used (e. g. Machens et al, 2003;Narayan et al, 2006). For each stimulus S i the spike train of one of the trials, s i,j T , is chosen as a template and each spike train s i ,j is assigned to the stimulus with the nearest template.…”

Section: The Dependence On the Classifier And The Measurementioning

confidence: 99%

“…In other cases various measures are used to verify if the results are independent of the measure (Machens et al, 2003;Narayan et al, 2006), or to compare their performance (Wang et al, 2007). In particular, the two different families of spike train distances based on spikes and intervals (Victor and Purpura, 1996), are usually considered together to examine the nature of the code (e. g. Victor and Purpura, 1996;Di Lorenzo and Victor, 2003;Samonds and Bonds, 2004).…”

Section: The Dependence On the Classifier And The Measurementioning

confidence: 99%

“…Discrimination analysis has mainly been applied in this context to discriminate among naturalistic sounds (Machens et al, 2001(Machens et al, , 2003Narayan et al, 2005Narayan et al, , 2006Huetz et al, 2006;Wohlgemuth and Ronacher, 2007;Huetz et al, 2009). In this context, the timedependent stimuli are regarded as a discrete set of different stimuli rather than simply different realizations from an underlying common probability distribution.…”

Section: The Application Of Discrimination Analysis To Experimental Dmentioning

confidence: 99%

“…The stimuli forming the set {S} are not distinguisable by one property, like the spatial phase, but they are behaviorally relevant complex entities, consisting of a sequence of features resulting in a particular timedependent profile of relevant features for each cell. In this case the mutual information between stimuli and predicted stimuli is not a lower bound of the mutual information between stimuli and responses, and should be considered simply as a measure of discrimination performance, similar to the percentage of correct classifications (Machens et al, 2003;Narayan et al, 2006). The percentage of correct classifications cannot be directly compared to the normalized mutual information.…”

Section: The Application Of Discrimination Analysis To Experimental Dmentioning

confidence: 99%

See 4 more Smart Citations

What can spike train distances tell us about the neural code?

Chicharro

Kreuz

Andrzejak

2011

Journal of Neuroscience Methods

View full text Add to dashboard Cite

Time scale parametric spike train distances like the Victor and the van Rossum distances are often applied to study the neural code based on neural stimuli discrimination. Different neural coding hypotheses, such as rate or coincidence coding, can be assessed by combining a time scale parametric spike train distance with a classifier in order to obtain the optimal discrimination performance. The time scale for which the responses to different stimuli are distinguished best is assumed to be the discriminative precision of the neural code. The relevance of temporal coding is evaluated by comparing the optimal discrimination performance with the one achieved when assuming a rate code.We here characterize the measures quantifying the discrimination performance, the discriminative precision, and the relevance of temporal coding. Furthermore, we evaluate the information these quantities provide about the neural code. We show that the discriminative precision is too unspecific to be interpreted in terms of the time scales relevant for encoding. Accordingly, the time scale parametric nature of the distances is mainly an advantage because it allows maximizing the discrimination performance across a whole set of measures with different sensitivities determined by the time scale parameter, but not due to the possibility to examine the temporal properties of the neural code.

show abstract

Section: The Dependence On the Classifier And The Measurementioning

confidence: 99%

Section: The Dependence On the Classifier And The Measurementioning

confidence: 99%

Section: The Dependence On the Classifier And The Measurementioning

confidence: 99%

Section: The Application Of Discrimination Analysis To Experimental Dmentioning

confidence: 99%

Section: The Application Of Discrimination Analysis To Experimental Dmentioning

confidence: 99%

See 3 more Smart Citations

What can spike train distances tell us about the neural code?

Chicharro

Kreuz

Andrzejak

2011

Journal of Neuroscience Methods

View full text Add to dashboard Cite

show abstract

Frequency processing at consecutive levels in the auditory system of bush crickets (tettigoniidae)

Ostrowski

Stumpner

2010

J of Comparative Neurology

View full text Add to dashboard Cite

We asked how processing of male signals in the auditory pathway of the bush cricket Ancistrura nigrovittata (Phaneropterinae, Tettigoniidae) changes from the ear to the brain. From 37 sensory neurons in the crista acustica single elements (cells 8 or 9) have frequency tuning corresponding closely to the behavioral tuning of the females. Nevertheless, one-quarter of sensory neurons (approximately cells 9 to 18) excite the ascending neuron 1 (AN1), which is best tuned to the male's song carrier frequency. AN1 receives frequency-dependent inhibition, reducing sensitivity especially in the ultrasound. When recorded in the brain, AN1 shows slightly lower overall activity than when recorded in the prothoracic ganglion close to the spike-generating zone. This difference is significant in the ultrasonic range. The first identified local brain neuron in a bush cricket (LBN1) is described. Its dendrites overlap with some of AN1-terminations in the brain. Its frequency tuning and intensity dependence strongly suggest a direct postsynaptic connection to AN1. Spiking in LBN1 is only elicited after summation of excitatory postsynaptic potentials evoked by individual AN1-action potentials. This serves a filtering mechanism that reduces the sensitivity of LBN1 and also its responsiveness to ultrasound as compared to AN1. Consequently, spike latencies of LBN1 are long (>30 ms) despite its being a second-order interneuron. Additionally, LBN1 receives frequency-specific inhibition, most likely further reducing its responses to ultrasound. This demonstrates that frequency-specific inhibition is redundant in two directly connected interneurons on subsequent levels in the auditory system.

show abstract

The physiology of insect auditory afferents

Mason

Faure

2004

Microscopy Res & Technique

View full text Add to dashboard Cite

This review presents an overview of the physiology of primary receptors serving tympanal hearing in insects. Auditory receptor responses vary with frequency, intensity, and temporal characteristics of sound stimuli. Various insect species exploit each of these parameters to differing degrees in the neural coding of auditory information, depending on the nature of the relevant stimuli. Frequency analysis depends on selective tuning in individual auditory receptors. In those insect groups that have individually tuned receptors, differences in physiology are correlated with structural differences among receptors and with the anatomical arrangement of receptors within the ear. Intensity coding is through the rate-level characteristics of tonically active auditory receptors and through variation in the absolute sensitivities of individual receptors (range fractionation). Temporal features of acoustic stimuli may be copied directly in the timing of afferent responses. Salient signal characteristics may also be represented by variation in the timing of afferent responses on a finer temporal scale, or by the synchrony of responses across a population of receptors.

show abstract

Single auditory neurons rapidly discriminate conspecific communication signals

Cited by 107 publications

References 6 publications

What can spike train distances tell us about the neural code?

What can spike train distances tell us about the neural code?

Frequency processing at consecutive levels in the auditory system of bush crickets (tettigoniidae)

The physiology of insect auditory afferents

Contact Info

Product

Resources

About