scholarly journals Speech encoding by coupled cortical theta and gamma oscillations

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Alexandre Hyafil ◽  
Lorenzo Fontolan ◽  
Claire Kabdebon ◽  
Boris Gutkin ◽  
Anne-Lise Giraud
Keyword(s):  
Parallel Processing ◽  
Auditory Cortex ◽  
Sensory Analysis ◽  
Gamma Oscillations ◽  
Theta Oscillations ◽  
Rhythmic Structure ◽  
Speech Encoding ◽  
Cortical Oscillations ◽  
Slow Speech ◽  
The Brain

Many environmental stimuli present a quasi-rhythmic structure at different timescales that the brain needs to decompose and integrate. Cortical oscillations have been proposed as instruments of sensory de-multiplexing, i.e., the parallel processing of different frequency streams in sensory signals. Yet their causal role in such a process has never been demonstrated. Here, we used a neural microcircuit model to address whether coupled theta–gamma oscillations, as observed in human auditory cortex, could underpin the multiscale sensory analysis of speech. We show that, in continuous speech, theta oscillations can flexibly track the syllabic rhythm and temporally organize the phoneme-level response of gamma neurons into a code that enables syllable identification. The tracking of slow speech fluctuations by theta oscillations, and its coupling to gamma-spiking activity both appeared as critical features for accurate speech encoding. These results demonstrate that cortical oscillations can be a key instrument of speech de-multiplexing, parsing, and encoding.

Central Auditory Processing

2021 ◽  
Author(s):  
Josef P. Rauschecker
Keyword(s):  
Auditory Cortex ◽  
Auditory Processing ◽  
Cognitive Abilities ◽  
Auditory Perception ◽  
Auditory Brainstem ◽  
Central Auditory Processing ◽  
Visible Part ◽  
Auditory Object ◽  
Mother’S Voice ◽  
The Brain

When one talks about hearing, some may first imagine the auricle (or external ear), which is the only visible part of the auditory system in humans and other mammals. Its shape and size vary among people, but it does not tell us much about a person’s abilities to hear (except perhaps their ability to localize sounds in space, where the shape of the auricle plays a certain role). Most of what is used for hearing is inside the head, particularly in the brain. The inner ear transforms mechanical vibrations into electrical signals; then the auditory nerve sends these signals into the brainstem, where intricate preprocessing occurs. Although auditory brainstem mechanisms are an important part of central auditory processing, it is the processing taking place in the cerebral cortex (with the thalamus as the mediator), which enables auditory perception and cognition. Human speech and the appreciation of music can hardly be imagined without a complex cortical network of specialized regions, each contributing different aspects of auditory cognitive abilities. During the evolution of these abilities in higher vertebrates, especially birds and mammals, the cortex played a crucial role, so a great deal of what is referred to as central auditory processing happens there. Whether it is the recognition of one’s mother’s voice, listening to Pavarotti singing or Yo-Yo Ma playing the cello, hearing or reading Shakespeare’s sonnets, it will evoke electrical vibrations in the auditory cortex, but it does not end there. Large parts of frontal and parietal cortex receive auditory signals originating in auditory cortex, forming processing streams for auditory object recognition and auditory-motor control, before being channeled into other parts of the brain for comprehension and enjoyment.

scholarly journals Learning to synchronize: Midfrontal theta dynamics during rule switching

2020 ◽  
Author(s):  
Pieter Verbeke ◽  
Kate Ergo ◽  
Esther De Loof ◽  
Tom Verguts
Keyword(s):  
Negative Feedback ◽  
Human Subjects ◽  
Learning Task ◽  
Theta Oscillations ◽  
Prediction Errors ◽  
Hierarchical Learning ◽  
Theta Power ◽  
Negative Prediction ◽  
Theta Phase ◽  
The Brain

AbstractIn recent years, several hierarchical extensions of well-known learning algorithms have been proposed. For example, when stimulus-action mappings vary across time or context, the brain may learn two or more stimulus-action mappings in separate modules, and additionally (at a hierarchically higher level) learn to appropriately switch between those modules. However, how the brain mechanistically coordinates neural communication to implement such hierarchical learning, remains unknown. Therefore, the current study tests a recent computational model that proposed how midfrontal theta oscillations implement such hierarchical learning via the principle of binding by synchrony (Sync model). More specifically, the Sync model employs bursts at theta frequency to flexibly bind appropriate task modules by synchrony. 64-channel EEG signal was recorded while 27 human subjects (Female: 21, Male: 6) performed a probabilistic reversal learning task. In line with the Sync model, post-feedback theta power showed a linear relationship with negative prediction errors, but not with positive prediction errors. This relationship was especially pronounced for subjects with better behavioral fit (measured via AIC) of the Sync model. Also consistent with Sync model simulations, theta phase-coupling between midfrontal electrodes and temporo-parietal electrodes was stronger after negative feedback. Our data suggest that the brain uses theta power and synchronization for flexibly switching between task rule modules, as is useful for example when multiple stimulus-action mappings must be retained and used.Significance StatementEveryday life requires flexibility in switching between several rules. A key question in understanding this ability is how the brain mechanistically coordinates such switches. The current study tests a recent computational framework (Sync model) that proposed how midfrontal theta oscillations coordinate activity in hierarchically lower task-related areas. In line with predictions of this Sync model, midfrontal theta power was stronger when rule switches were most likely (strong negative prediction error), especially in subjects who obtained a better model fit. Additionally, also theta phase connectivity between midfrontal and task-related areas was increased after negative feedback. Thus, the data provided support for the hypothesis that the brain uses theta power and synchronization for flexibly switching between rules.

scholarly journals Theta and gamma hippocampal-neocortical oscillations during the episodic-like memory test: impairment in epileptogenic rats

2021 ◽  
Author(s):  
Anton E Malkov ◽  
Ludmila Shevkova ◽  
Alexandra Latyshkova ◽  
Valentina Kitchigina
Keyword(s):  
Episodic Memory ◽  
Memory Task ◽  
Gamma Oscillations ◽  
High Amplitude ◽  
Memory Test ◽  
Interictal Spikes ◽  
Paroxysmal Activity ◽  
Cortical Oscillations ◽  
Gamma Rhythms ◽  
Hippocampal Theta

Cortical oscillations in different frequency bands have been shown to be intimately involved in exploration of environment and cognition. Here, the local field potentials in the hippocampus, the medial prefrontal cortex (mPFC), and the medial entorhinal cortex (mEC) were recorded simultaneously in rats during the execution of the episodic-like memory task. The power of hippocampal theta (~4-10 Hz), slow gamma (~25-50 Hz), and fast gamma oscillations (~55-100 Hz) was analyzed in all structures examined. Particular attention was paid to the theta coherence between three mentioned structures. The modulation of the power of gamma rhythms by the phase of theta cycle during the execution of the episodic-like memory test by rats was also closely studied. Healthy rats and rats one month after kainate-induced status epilepticus (SE) were examined. Paroxysmal activity in the hippocampus (high amplitude interictal spikes), excessive excitability of animals, and the death of hippocampal and dentate granular cells in rats with kainate-evoked SE were observed, which indicated the development of seizure focus in the hippocampus (epileptogenesis). One month after SE, the rats exhibited a specific impairment of episodic memory for the what-where-when triad: unlike healthy rats, epileptogenic SE animals did not identify the objects during the test. This impairment was associated with the changes in the characteristics of theta and gamma rhythms and specific violation of theta coherence and theta/gamma coupling in these structures in comparison with the healthy animals. We believe that these disturbances in the cortical areas play a role in episodic memory dysfunction in kainate-treated animals. These findings can shed light on the mechanisms of cognitive deficit during epileptogenesis.

Hippocampal-prefrontal theta coupling develops as mice become proficient in associative odorant discrimination learning

2021 ◽  
Author(s):  
Daniel Ramirez-Gordillo ◽  
Andrew A. Parra ◽  
K. Ulrich Bayer ◽  
Diego Restrepo
Keyword(s):  
Learning And Memory ◽  
Discrimination Learning ◽  
Gamma Oscillations ◽  
Oscillatory Activity ◽  
Dependent Protein Kinase ◽  
Dependent Protein ◽  
Theta Phase ◽  
Phase Amplitude ◽  
The Brain

Learning and memory requires coordinated activity between different regions of the brain. Here we studied the interaction between medial prefrontal cortex (mPFC) and hippocampal dorsal CA1 during associative odorant discrimination learning in the mouse. We found that as the animal learns to discriminate odorants in a go-no go task the coupling of high frequency neural oscillations to the phase of theta oscillations (phase-amplitude coupling or PAC) changes in a manner that results in divergence between rewarded and unrewarded odorant-elicited changes in the theta-phase referenced power (tPRP) for beta and gamma oscillations. In addition, in the proficient animal there was a decrease in the coordinated oscillatory activity between CA1 and mPFC in the presence of the unrewarded odorant. Furthermore, the changes in PAC resulted in a marked increase in the accuracy for decoding odorant identity from tPRP when the animal became proficient. Finally, we studied the role of Ca2+/calmodulin-dependent protein kinase II α (CaMKIIα), a protein involved in learning and memory, in oscillatory neural processing in this task. We find that the accuracy for decoding the odorant identity from tPRP decreases in CaMKIIα knockout mice and that this accuracy correlates with behavioral performance. These results implicate a role for PAC and CaMKIIα in olfactory go-no go associative learning in the hippocampal-prefrontal circuit.

Hippocampus Retains the Periodicity of Gamma Stimulation In Vivo

2002 ◽  
Vol 88 (5) ◽  
pp. 2349-2354 ◽  
Author(s):  
J. E. Mikkonen ◽  
T. Grönfors ◽  
J. J. Chrobak ◽  
M. Penttonen
Keyword(s):  
Information Acquisition ◽  
Pyramidal Cells ◽  
Gamma Oscillations ◽  
Short Term ◽  
Ca1 Pyramidal Cells ◽  
State Dependent ◽  
Hippocampal Ca1 ◽  
Oscillatory Rhythms ◽  
The Brain

Several behavioral state dependent oscillatory rhythms have been identified in the brain. Of these neuronal rhythms, gamma (20–70 Hz) oscillations are prominent in the activated brain and are associated with various behavioral functions ranging from sensory binding to memory. Hippocampal gamma oscillations represent a widely studied band of frequencies co-occurring with information acquisition. However, induction of specific gamma frequencies within the hippocampal neuronal network has not been satisfactorily established. Using both in vivo intracellular and extracellular recordings from anesthetized rats, we show that hippocampal CA1 pyramidal cells can discharge at frequencies determined by the preceding gamma stimulation, provided that the gamma is introduced in theta cycles, as occurs in vivo. The dynamic short-term alterations in the oscillatory discharge described in this paper may serve as a coding mechanism in cortical neuronal networks.

scholarly journals Altered GABAA,slow Inhibition and Network Oscillations in Mice Lacking the GABAA Receptor β3 Subunit

2009 ◽  
Vol 102 (6) ◽  
pp. 3643-3655 ◽  
Author(s):  
Harald Hentschke ◽  
Claudia Benkwitz ◽  
Matthew I. Banks ◽  
Mark G. Perkins ◽  
Gregg E. Homanics ◽  
...  
Keyword(s):  
Pyramidal Neurons ◽  
Epileptiform Activity ◽  
Gamma Oscillations ◽  
Theta Oscillations ◽  
Network Activity ◽  
Inhibitory Synapses ◽  
Gabaergic Inhibition ◽  
Wild Type ◽  
Hippocampal Network

Phasic GABAergic inhibition in hippocampus and neocortex falls into two kinetically distinct categories, GABAA,fast and GABAA,slow. In hippocampal area CA1, GABAA,fast is generally believed to underlie gamma oscillations, whereas the contribution of GABAA,slow to hippocampal rhythms has been speculative. Hypothesizing that GABAA receptors containing the β3 subunit contribute to GABAA,slow inhibition and that slow inhibitory synapses control excitability as well as contribute to network rhythms, we investigated the consequences of this subunit's absence on synaptic inhibition and network function. In pyramidal neurons of GABAA receptor β3 subunit-deficient (β3−/−) mice, spontaneous GABAA,slow inhibitory postsynaptic currents (IPSCs) were much less frequent, and evoked GABAA,slow currents were much smaller than in wild-type mice. Fittingly, long-lasting recurrent inhibition of population spikes was less powerful in the mutant, indicating that receptors containing β3 subunits contribute substantially to GABAA,slow currents in pyramidal neurons. By contrast, slow inhibitory control of GABAA,fast-producing interneurons was unaffected in β3−/− mice. In vivo hippocampal network activity was markedly different in the two genotypes. In β3−/− mice, epileptiform activity was observed, and theta oscillations were weaker, slower, less regular and less well coordinated across laminae compared with wild-type mice, whereas gamma oscillations were weaker and faster. The amplitude modulation of gamma oscillations at theta frequency (“nesting”) was preserved but was less well coordinated with theta oscillations. With the caveat that seizure-induced changes in inhibitory circuits might have contributed to the changes observed in the mutant animals, our results point to a strong contribution of β3 subunits to slow GABAergic inhibition onto pyramidal neurons but not onto GABAA,fast -producing interneurons and support different roles for these slow inhibitory synapses in the generation and coordination of hippocampal network rhythms.

scholarly journals Echoes of the spoken past: how auditory cortex hears context during speech perception

2014 ◽  
Vol 369 (1651) ◽  
pp. 20130297 ◽  
Author(s):  
Jeremy I. Skipper
Keyword(s):  
Speech Perception ◽  
Auditory Cortex ◽  
Speech Production ◽  
Real World ◽  
Similar Pattern ◽  
Meta Analyses ◽  
The Brain ◽  
Auditory Objects

What do we hear when someone speaks and what does auditory cortex (AC) do with that sound? Given how meaningful speech is, it might be hypothesized that AC is most active when other people talk so that their productions get decoded. Here, neuroimaging meta-analyses show the opposite: AC is least active and sometimes deactivated when participants listened to meaningful speech compared to less meaningful sounds. Results are explained by an active hypothesis-and-test mechanism where speech production (SP) regions are neurally re-used to predict auditory objects associated with available context. By this model, more AC activity for less meaningful sounds occurs because predictions are less successful from context, requiring further hypotheses be tested. This also explains the large overlap of AC co-activity for less meaningful sounds with meta-analyses of SP. An experiment showed a similar pattern of results for non-verbal context. Specifically, words produced less activity in AC and SP regions when preceded by co-speech gestures that visually described those words compared to those words without gestures. Results collectively suggest that what we ‘hear’ during real-world speech perception may come more from the brain than our ears and that the function of AC is to confirm or deny internal predictions about the identity of sounds.

Dynamics of Changes in the Fatty Acid Composition of the Auditory Cortex of the Brain in Rats after Single Audiogenic Convulsions

2016 ◽  
Vol 47 (2) ◽  
pp. 168-172
Author(s):  
T. P. Kulagina ◽  
A. V. Aripovskii ◽  
T. A. Savina ◽  
T. G. Shchipakina ◽  
O. V. Godukhin
Keyword(s):  
Fatty Acid Composition ◽  
Fatty Acid ◽  
Auditory Cortex ◽  
Acid Composition ◽  
Audiogenic Convulsions ◽  
The Brain

scholarly journals Computational Modeling of Information Propagation during the Sleep–Waking Cycle

Biology ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 945
Author(s):  
Farhad Razi ◽  
Rubén Moreno Bote ◽  
Belén Sancristóbal
Keyword(s):  
Auditory Cortex ◽  
Nrem Sleep ◽  
Neural Mass Model ◽  
Excitatory Synapses ◽  
Information Propagation ◽  
Mass Model ◽  
Cortical Columns ◽  
Auditory Nerves ◽  
Cortical Responses ◽  
The Brain

Non-threatening familiar sounds can go unnoticed during sleep despite the fact that they enter our brain by exciting the auditory nerves. Extracellular cortical recordings in the primary auditory cortex of rodents show that an increase in firing rate in response to pure tones during deep phases of sleep is comparable to those evoked during wakefulness. This result challenges the hypothesis that during sleep cortical responses are weakened through thalamic gating. An alternative explanation comes from the observation that the spatiotemporal spread of the evoked activity by transcranial magnetic stimulation in humans is reduced during non-rapid eye movement (NREM) sleep as compared to the wider propagation to other cortical regions during wakefulness. Thus, cortical responses during NREM sleep remain local and the stimulus only reaches nearby neuronal populations. We aim at understanding how this behavior emerges in the brain as it spontaneously shifts between NREM sleep and wakefulness. To do so, we have used a computational neural-mass model to reproduce the dynamics of the sensory auditory cortex and corresponding local field potentials in these two brain states. Following the synaptic homeostasis hypothesis, an increase in a single parameter, namely the excitatory conductance g¯AMPA, allows us to place the model from NREM sleep into wakefulness. In agreement with the experimental results, the endogenous dynamics during NREM sleep produces a comparable, even higher, response to excitatory inputs to the ones during wakefulness. We have extended the model to two bidirectionally connected cortical columns and have quantified the propagation of an excitatory input as a function of their coupling. We have found that the general increase in all conductances of the cortical excitatory synapses that drive the system from NREM sleep to wakefulness does not boost the effective connectivity between cortical columns. Instead, it is the inter-/intra-conductance ratio of cortical excitatory synapses that should raise to facilitate information propagation across the brain.

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