Low-Frequency Spike-Field Coherence Is a Fingerprint of Periodicity Coding in the Auditory Cortex

García‐Rosales, Francisco; Martin, Lisa M.; Beetz, M. Jerome; Cabral‐Calderín, Yuranny; Kössl, Manfred; Hechavarría, Julio C.

doi:10.1016/j.isci.2018.10.009

Cited by 21 publications

(19 citation statements)

References 71 publications

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“… 28 , and Discussion), but rather to the syllabic rate which entrains both the LFPs and the spiking of ST units. High-frequency LFP-stimulus entrainment has been previously reported in this species 3 , 36 , and was quantified here using the stimulus–field coherence metric (a frequency-dependent index similar to the SFC; see Supplementary Methods ), depicted in Supplementary Figure 5 . Overall, the coherence patterns suggest distinct cortical processes underlying the response type of each neuronal subpopulation, particularly implicating low frequency oscillations in the organization of BT-like responses.…”

Section: Resultsmentioning

confidence: 99%

Neuronal coding of multiscale temporal features in communication sequences within the bat auditory cortex

et al. 2018

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Experimental evidence supports that cortical oscillations represent multiscale temporal modulations existent in natural stimuli, yet little is known about the processing of these multiple timescales at a neuronal level. Here, using extracellular recordings from the auditory cortex (AC) of awake bats (Carollia perspicillata), we show the existence of three neuronal types which represent different levels of the temporal structure of conspecific vocalizations, and therefore constitute direct evidence of multiscale temporal processing of naturalistic stimuli by neurons in the AC. These neuronal subpopulations synchronize differently to local-field potentials, particularly in theta- and high frequency bands, and are informative to a different degree in terms of their spike rate. Interestingly, we also observed that both low and high frequency cortical oscillations can be highly informative about the listened calls. Our results suggest that multiscale neuronal processing allows for the precise and non-redundant representation of natural vocalizations in the AC.

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Section: Resultsmentioning

confidence: 99%

Neuronal coding of multiscale temporal features in communication sequences within the bat auditory cortex

et al. 2018

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“…Considering all these things, rhythm analysis can be used to tackle many questions. Not only can we further investigate couplings of biological processes such as motor rhythms [50,51], but it can be used to find possible guiding neural processes [5,7] and can give valuable information for studies on the perception of temporal structures [52]. Especially in echolocating animals such as whales and bats, rhythm analysis yields a good background for studies on rhythm perception.…”

Section: Discussionmentioning

confidence: 99%

Comparison of methods for rhythm analysis of complex animals’ acoustic signals

Burchardt

Knörnschild

2020

PLoS Comput Biol

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Analyzing the rhythm of animals' acoustic signals is of interest to a growing number of researchers: evolutionary biologists want to disentangle how these structures evolved and what patterns can be found, and ecologists and conservation biologists aim to discriminate cryptic species on the basis of parameters of acoustic signals such as temporal structures. Temporal structures are also relevant for research on vocal production learning, a part of which is for the animal to learn a temporal structure. These structures, in other words, these rhythms, are the topic of this paper. How can they be investigated in a meaningful, comparable and universal way? Several approaches exist. Here we used five methods to compare their suitability and interpretability for different questions and datasets and test how they support the reproducibility of results and bypass biases. Three very different datasets with regards to recording situation, length and context were analyzed: two social vocalizations of Neotropical bats (multisyllabic, medium long isolation calls of Saccopteryx bilineata, and monosyllabic, very short isolation calls of Carollia perspicillata) and click trains of sperm whales, Physeter macrocephalus. Techniques to be compared included Fourier analysis with a newly developed goodness-of-fit value, a generate-and-test approach where data was overlaid with varying artificial beats, and the analysis of inter-onset-intervals and calculations of a normalized Pairwise Variability Index (nPVI). We discuss the advantages and disadvantages of the methods and we also show suggestions on how to best visualize rhythm analysis results. Furthermore, we developed a decision tree that will enable researchers to select a suitable and comparable method on the basis of their data. Author summaryIn the analysis of animal communication more and more interest is shown in rhythm of animal communication and what information this might convey. In this paper, we establish a workflow to analyze the temporal structure-namely the rhythm-of any particular animals'acoustic signal with methods that are applicable for a wide range of signals and results that are easily comparable and interpretable. This workflow will enhance the understanding of rhythmicality in animals' acoustic signals as well as facilitate comparison between species. Methods we conducted ranged from simple distributional and visual PLOS COMPUTATIONAL BIOLOGY PLOS Computational Biology | https://doi.

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“…The time period after vocal production must be examined carefully, as the calls produced could differ in their acoustic attributes (i.e., frequency composition and duration, among others), which could lead to differences in call-evoked neural responses. In different sensory cortices, low frequencies such as theta and alpha are known to modulate sensory processing and enable sensory selection [29,73,74]. Unlike sensory cortices, low-frequency oscillations in frontal areas are less understood in terms of sensory processing.…”

Section: Theta Oscillations For Inter-areal Coupling In the Fronto-stmentioning

confidence: 99%

“…The latter facilitated stable recording conditions by preventing head movements. The implantation protocol was modified from the procedure used in previous studies [73,74,[81][82][83][84].…”

Section: Surgical Proceduresmentioning

confidence: 99%

Neural oscillations in the fronto-striatal network predict vocal output in bats

2020

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The ability to vocalize is ubiquitous in vertebrates, but neural networks underlying vocal control remain poorly understood. Here, we performed simultaneous neuronal recordings in the frontal cortex and dorsal striatum (caudate nucleus, CN) during the production of echolocation pulses and communication calls in bats. This approach allowed us to assess the general aspects underlying vocal production in mammals and the unique evolutionary adaptations of bat echolocation. Our data indicate that before vocalization, a distinctive change in highgamma and beta oscillations (50-80 Hz and 12-30 Hz, respectively) takes place in the bat frontal cortex and dorsal striatum. Such precise fine-tuning of neural oscillations could allow animals to selectively activate motor programs required for the production of either echolocation or communication vocalizations. Moreover, the functional coupling between frontal and striatal areas, occurring in the theta oscillatory band (4-8 Hz), differs markedly at the millisecond level, depending on whether the animals are in a navigational mode (that is, emitting echolocation pulses) or in a social communication mode (emitting communication calls). Overall, this study indicates that fronto-striatal oscillations could provide a neural correlate for vocal control in bats.

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Low-Frequency Spike-Field Coherence Is a Fingerprint of Periodicity Coding in the Auditory Cortex

Cited by 21 publications

References 71 publications

Neuronal coding of multiscale temporal features in communication sequences within the bat auditory cortex

Neuronal coding of multiscale temporal features in communication sequences within the bat auditory cortex

Comparison of methods for rhythm analysis of complex animals’ acoustic signals

Neural oscillations in the fronto-striatal network predict vocal output in bats

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