Rhythmic interactions between the mediodorsal thalamus and prefrontal cortex precede human visual perception

Griffiths, Benjamin; Zaehle, Tino; Repplinger, Stefan; Schmitt, Friedhelm C.; Voges, Jürgen; Hanslmayr, Simon; Staudigl, Tobias

doi:10.1038/s41467-022-31407-z

Cited by 16 publications

(14 citation statements)

References 75 publications

(73 reference statements)

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“…Empirically, intracranial recordings in patients revealed a decrease in NC alpha, which preceded fast gamma oscillations in MTL networks during memory encoding ( Griffiths et al, 2019 ). This complements former evidence that reduced EEG alpha power facilitated information processes when preceding relevant sensory input (e.g., Romei et al, 2008 ; Busch et al, 2009 ; see VanRullen, 2016 , for a review; Griffiths et al, 2022 ). An inverse relation between local field alpha desynchronization and neural firing rates in sensory cortical regions has been found in monkeys ( Haegens et al, 2011 ), and the alpha rhythm has been shown to exert top-down influences in the visual cortex of macaques ( Bastos et al, 2015 ; Richter et al, 2017 ).…”

Section: Neural Rhythms – Temporal Dynamics Of Neural Computationsupporting

confidence: 84%

Rhythms of human attention and memory: An embedded process perspective

Köster

Gruber

2022

Front. Hum. Neurosci.

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It remains a dogma in cognitive neuroscience to separate human attention and memory into distinct modules and processes. Here we propose that brain rhythms reflect the embedded nature of these processes in the human brain, as evident from their shared neural signatures: gamma oscillations (30–90 Hz) reflect sensory information processing and activated neural representations (memory items). The theta rhythm (3–8 Hz) is a pacemaker of explicit control processes (central executive), structuring neural information processing, bit by bit, as reflected in the theta-gamma code. By representing memory items in a sequential and time-compressed manner the theta-gamma code is hypothesized to solve key problems of neural computation: (1) attentional sampling (integrating and segregating information processing), (2) mnemonic updating (implementing Hebbian learning), and (3) predictive coding (advancing information processing ahead of the real time to guide behavior). In this framework, reduced alpha oscillations (8–14 Hz) reflect activated semantic networks, involved in both explicit and implicit mnemonic processes. Linking recent theoretical accounts and empirical insights on neural rhythms to the embedded-process model advances our understanding of the integrated nature of attention and memory – as the bedrock of human cognition.

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Section: Neural Rhythms – Temporal Dynamics Of Neural Computationsupporting

confidence: 84%

Rhythms of human attention and memory: An embedded process perspective

Köster

Gruber

2022

Front. Hum. Neurosci.

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“…In this study, we theorize that P1 and P2 were triggered by the sensory input, which suggests the existence of direct pathways that bypass intermediate hierarchical levels. One possible route is the thalamocortical pathways, which converge with corticocortical pathways to enable higher auditory and visual perception 59 , 60 .…”

Section: Discussionmentioning

confidence: 99%

A quantitative model reveals a frequency ordering of prediction and prediction-error signals in the human brain

Chao

Huang

2022

Commun Biol

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The human brain is proposed to harbor a hierarchical predictive coding neuronal network underlying perception, cognition, and action. In support of this theory, feedforward signals for prediction error have been reported. However, the identification of feedback prediction signals has been elusive due to their causal entanglement with prediction-error signals. Here, we use a quantitative model to decompose these signals in electroencephalography during an auditory task, and identify their spatio-spectral-temporal signatures across two functional hierarchies. Two prediction signals are identified in the period prior to the sensory input: a low-level signal representing the tone-to-tone transition in the high beta frequency band, and a high-level signal for the multi-tone sequence structure in the low beta band. Subsequently, prediction-error signals dependent on the prior predictions are found in the gamma band. Our findings reveal a frequency ordering of prediction signals and their hierarchical interactions with prediction-error signals supporting predictive coding theory.

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“…The second prediction of our model is that a stable temporal code emerges from approximately synchronous oscillations in the consecutive network layers. Recent work using concurrent iEEG and MEG recordings has suggested that interactions between alpha oscillations in prefrontal cortex and mediodorsal thalamus mediate visual performance [41]. In light of the literature on travelling alpha waves [6, 4, 5, 9, 27, 65, 124] this begs the question of whether neuronal processing along the visual hierarchy is controlled by one driving force as suggested by our simulations, e.g.…”

Section: Discussionmentioning

confidence: 65%

Oscillations in an Artificial Neural Network Convert Competing Inputs into a Temporal Code

Duecker,

Idiart,

van Gerven

et al. 2023

Preprint

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Deep convolutional neural networks (CNNs) resemble the hierarchically organised neural representations in the primate visual ventral stream. However, these models typically disregard the temporal dynamics experimentally observed in these areas. For instance, alpha oscillations dominate the dynamics of the human visual cortex, yet the computational relevance of oscillations is rarely considered in artificial neural networks (ANNs). We propose an ANN that embraces oscillatory dynamics with the computational purpose of converting simultaneous inputs, presented at two different locations, into a temporal code. The network was trained to classify three individually presented letters. Post-training, we added semi-realistic temporal dynamics to the hidden layer, introducing relaxation dynamics in the hidden units as well as pulsed inhibition mimicking neuronal alpha oscillations. Without these dynamics, the trained network correctly classified individual letters but produced a mixed output when presented with two letters simultaneously, elucidating a bottleneck problem. When introducing refraction and oscillatory inhibition, the output nodes corresponding to the two stimuli activated sequentially, ordered along the phase of the inhibitory oscillations. Our model provides a novel approach for implementing multiplexing in ANNs. It further produces experimentally testable predictions of how the primate visual system handles competing stimuli.

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Rhythmic interactions between the mediodorsal thalamus and prefrontal cortex precede human visual perception

Cited by 16 publications

References 75 publications

Rhythms of human attention and memory: An embedded process perspective

Rhythms of human attention and memory: An embedded process perspective

A quantitative model reveals a frequency ordering of prediction and prediction-error signals in the human brain

Oscillations in an Artificial Neural Network Convert Competing Inputs into a Temporal Code

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