Biology subjects

Obermayer, K.

Publications and source records attributed to Obermayer, K..

13 recordsLinked to original sources

bioRxiv2025

Polarity-dependent modulation of sleep oscillations and cortical excitability in aging

During non-rapid eye movement (NREM) sleep, cortical slow oscillation (SO; <1 Hz) and thalamic sleep spindle activity (12-15 Hz) interact through precise phase coupling to support memory consolidation. Slow oscillatory transcranial direct current stimulation (so-tDCS) can modulate these oscillations. Traditionally, anodal so-tDCS is used to depolarize the cortex during SO up-states, thereby promoting SO activity and SO-spindle coupling. However, intracranial findings suggest that SO down-states, characterized by cortical hyperpolarization, can trigger thalamic spindle bursts. This raises the hypothesis that cathodal so-tDCS, by promoting hyperpolarization, could selectively enhance down-states and more effectively improve SO-spindle coupling. We tested this hypothesis in twenty-two healthy older adults, a population known to exhibit diminished NREM oscillatory activity. Each participant received cathodal, anodal, and sham so-tDCS in separate nap sleep sessions. We quantified SO and spindle characteristics, their temporal coupling, and cortical excitation/inhibition (E/I) balance using EEG spectral slope. We also assessed individual circadian preference (chronotype) as a potential moderator. We found that anodal so-tDCS improved SO-spindle synchrony, and increased spindle power over sham in participants with intermediate or evening chronotypes, while cathodal so-tDCS did not enhance these oscillatory measures compared to sham, despite prolonging SO down-states. Anodal stimulation also elevated E/I balance, indicating increased cortical excitability, whereas cathodal stimulation did not produce the anticipated opposite shift. In summary, anodal, but not cathodal so-tDCS, effectively enhanced thalamocortical interactions underlying memory consolidation. Furthermore, these findings highlight the importance of individual factors such as chronotype in brain stimulation responsiveness.

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bioRxiv2023

Objects Guide Human Gaze Behavior in Dynamic Real-World Scenes

The complexity of natural scenes makes it challenging to experimentally study the mechanisms behind human gaze behavior when viewing dynamic environments. Historically, eye movements were believed to be driven primarily by space-based attention towards locations with salient features. Increasing evidence suggests, however, that visual attention does not select locations with high saliency but operates on attentional units given by the objects in the scene. We present a new computational framework to investigate the importance of objects for attentional guidance. This framework is designed to simulate realistic scanpaths for dynamic real-world scenes, including saccade timing and smooth pursuit behavior. Individual model components are based on psychophysically uncovered mechanisms of visual attention and saccadic decision-making. All mechanisms are implemented in a modular fashion with a small number of well-interpretable parameters. To systematically analyze the importance of objects in guiding gaze behavior, we implemented five different models within this framework: two purely spatial models, where one is based on low-level saliency and one on high-level saliency, two object-based models, with one incorporating low-level saliency for each object and the other one not using any saliency information, and a mixed model with object-based attention and selection but space-based inhibition of return. We optimized each models parameters to reproduce the saccade amplitude and fixation duration distributions of human scanpaths using evolutionary algorithms. We compared model performance with respect to spatial and temporal fixation behavior, including the proportion of fixations exploring the background, as well as detecting, inspecting, and returning to objects. A model with object-based attention and inhibition, which uses saliency information to prioritize between objects for saccadic selection, leads to scanpath statistics with the highest similarity to the human data. This demonstrates that scanpath models benefit from object-based attention and selection, suggesting that object-level attentional units play an important role in guiding attentional processing. Author summaryThere has long been an interest in understanding how we decide when and where to move our eyes, and psychophysical experiments have uncovered many underlying mechanisms. Under controlled laboratory conditions, objects in the scene play an important role in guiding our attention. Due to the visual complexity of the world around us, however, it is hard to assess experimentally how objects influence eye movements when observing dynamic real-world scenes. Computational models have proved to be a powerful tool for investigating visual attention, but existing models are either only applicable to images or restricted to predicting where humans look on average. Here, we present a computational framework for simulating where and when humans decide to move their eyes when observing dynamic real-world scenes. Using our framework, we can assess the influence of objects on the model predictions. We find that including object-based attention in the modeling increases the resemblance of simulated eye movements to human gaze behavior, showing that objects play indeed an important role in guiding our gaze when exploring the world around us. We hope that the availability of this framework encourages more research on attention in dynamic real-world scenes.

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bioRxiv2023

Empirically identifying and computationally modelling the brain-behaviour relationship for human scene categorization

Humans effortlessly make quick and accurate perceptual decisions about the nature of their immediate visual environment, such as the category of the scene they face. Previous research has revealed a rich set of cortical representations potentially underlying this feat. However, it remains unknown which of these representations are suitably formatted for decision-making. Here, we approached this question empirically and computationally, using neuroimaging and computational modelling. For the empirical part, we collected electroencephalography (EEG) data and reaction times from human participants during a scene categorization task (natural vs. man-made). We then related neural representations to behaviour using a multivariate extension of signal detection theory. We observed a correlation specifically between [~]100 ms and [~]200 ms after stimulus onset, suggesting that the neural scene representations in this time period are suitably formatted for decision-making. For the computational part, we evaluated a recurrent convolutional neural network (RCNN) as a model of brain and behaviour. Unifying our previous observations in an image-computable model, the RCNN predicted well the neural representations, the behavioural scene categorization data, as well as the relationship between them. Our results identify and computationally characterize the neural and behavioural correlates of scene categorization in humans. Significance statementCategorizing scene information is a ubiquitous and crucial task. Here we provide an empirical and computational account of scene categorization. Previous research has identified when scenes are represented in the visual processing hierarchy, but it remains unclear which of these representations are relevant for behaviour. We identified such representations between [~]100 ms and [~]200 ms after stimulus onset. We then showed that scene categorization in humans can be modelled via a recurrent convolutional neural network in a unified manner, i.e., in terms of neural and behavioural correlates, and their relationship. Together this reveals which representations underlie scene categorization behaviour and proposes a computational mechanism that implements such representations.

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bioRxiv2021

Synchronization through uncorrelated noise in excitatory-inhibitory networks

Gamma rhythms play a major role in many different processes in the brain, such as attention, working memory and sensory processing. While typically considered detrimental, counterintuitively noise can sometimes have beneficial effects on communication and information transfer. Recently, Meng and Riecke showed that synchronization of interacting networks of inhibitory neurons in the gamma band increases while synchronization within these networks decreases when neurons are subject to uncorrelated noise. However, experimental and modeling studies point towards an important role of the pyramidal-interneuronal network gamma (PING) mechanism in the cortex. Therefore, we investigated the effect of uncorrelated noise on the communication between excitatory-inhibitory networks producing gamma oscillations via a PING mechanism. Our results suggest that synaptic noise can have a supporting role in facilitating inter-regional communication and that noise-induced synchronization between networks is generated via a different mechanism than when synchronization is mediated by strong synaptic coupling. Noise-induced synchronization is achieved by lowering synchronization within networks which allows the respective other network to impose its own gamma rhythm resulting in synchronization between networks.

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bioRxiv2021

Towards optimization of oscillatory stimulation during sleep

BackgroundOscillatory rhythms during sleep such as slow oscillations (SO) and spindles, and most importantly their coupling, are thought to underlie processes of memory consolidation. External slow oscillatory transcranial direct current stimulation (so-tDCS) with a frequency of 0.75 Hz has been shown to improve this coupling and memory consolidation, however, effects varied quite markedly between individuals, studies, and species. Here, we aimed to determine how precisely the frequency of stimulation has to match the naturally occurring SO frequency in individuals to optimally improve SO-spindle coupling. Moreover, we systematically tested stimulation durations necessary to induce changes. MethodsWe addressed these questions by comparing so-tDCS with individualized frequency to standardized frequency of 0.75 Hz in a within-subject design with 28 older participants during napping while systematically varying stimulation train durations between 30 s, 2 min, and 5 min. ResultsStimulation trains as short as 30 s were sufficient to modulate the coupling between SOs and spindle activity. Contrary to our expectations, so-tDCS with standardized frequency indicated stronger aftereffects with regard to SO-spindle coupling compared to individualized frequency. Angle and variance of spindle maxima occurrence during the SO cycle were similarly modulated. ConclusionIn sum, short stimulation trains were sufficient to induce significant changes in sleep physiology allowing for more trains of stimulation, which provides methodological advantages and possibly even larger behavioral effects in future studies. With regard to individualized stimulation frequency, further options of optimization need to be investigated, such as closed-loop stimulation to calibrate stimulation frequency to the SO frequency at time of stimulation onset. Significance statementApplication of slow oscillatory transcranial direct current stimulation during sleep has been shown to enhance specific memory-relevant sleep parameters and memory performance after sleep, albeit with a high degree of variability. Here, we systematically explored two major stimulation parameters possibly accounting for this variability in humans: frequency and duration of stimulation. We found, contrary to our expectations, standardized frequency stimulation with 0.75 Hz being superior to individualized frequency stimulation in enhancing specific sleep parameters. Moreover short stimulation trains of 30 seconds were as effective as 5 min in modulating aftereffects. These are encouraging findings, implying methodological advantages as larger quantity of aftereffects data can be obtained within the same time window, which may also lead to enhanced behavioral stimulation effects.

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bioRxiv2021

Interrelations between delta waves, spindles and slow oscillations in human NREM sleep and their functional role in memory

Certain neurophysiological characteristics of sleep, in particular slow oscillations (SO), sleep spindles, and their temporal coupling, have been well characterized and associated with human memory formation. Delta waves, which are somewhat higher in frequency and lower in amplitude compared to SO, have only recently been found to play a critical role in memory processing of rodents, through a competitive interplay between SO-spindle and delta-spindle coupling. However, human studies that comprehensively address delta waves, their interactions with spindles and SOs as well as their functional role for memory are still lacking. Electroencephalographic data were acquired across three naps of 33 healthy older human participants (17 female) to investigate delta-spindle coupling and the interplay between delta and SO-related activity. Additionally, we determined intra-individual stability of coupling measures and their potential link to the ability to form novel memories. Our results revealed weaker delta-spindle compared to SO-spindle coupling. Contrary to our initial hypothesis, we found that increased delta activity was accompanied by stronger SO-spindle coupling. Moreover, we identified the ratio between SO- and delta-nested spindles as the sleep parameter that predicted ability to form novel memories best. Our study suggests that SOs, delta waves and sleep spindles should be jointly considered when aiming to link sleep physiology and memory formation in aging. Significance statementInterrelations between delta waves, slow oscillations and sleep spindles have recently been causally linked to the balance between consolidation and forgetting in rats using optogenetics. In humans, SO-spindle coupling has been intensively investigated, but delta waves and their interaction with spindles were only studied jointly as SWA. Here we delineate the coupling of delta waves to spindles, investigate interactions of delta- and SO-related activity and investigate their role for the ability to form novel memories in healthy older individuals. Our results show differences as well as dependencies between SO- and delta-related activities including spindle coupling. Further, our results indicate that the ratio of SO- to delta-nested spindles might be the most informative sleep parameter for memory formation of older adults.

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bioRxiv2021

Cross-frequency slow oscillation-spindle coupling in a biophysically realistic thalamocortical neural mass model

Sleep manifests itself by the spontaneous emergence of characteristic oscillatory rhythms, which often timelock and are implicated in the memory formation. Here, we analyze a neural mass model of the thalamocortical loop of which the cortical node can generate slow oscillations (approx. 1 Hz) while its thalamic component can generate fast sleep spindles of{sigma} -band activity (12-15 Hz). We study the dynamics for different coupling strengths between the thalamic and cortical nodes, for different conductance values of the thalamic nodes potassium leak and anomalous rectifying currents, and for different parameter regimes of the cortical node. The latter are: (1) a low activity (DOWN) state with noise-induced, transient excursions into a high activity (UP) state, (2) an adaptation induced slow oscillation limit cycle with alternating UP and DOWN states, and (3) a high activity (UP) state with noise-induced, transient excursions into the low activity (DOWN) state. During UP states, thalamic spindling is abolished or reduced. During DOWN states, the thalamic node generates sleep spindles, which in turn can cause DOWN to UP transitions in the cortical node. Consequently, this leads to spindle-induced UP state transitions in parameter regime (1), thalamic spindles induced in some but not all DOWN states in regime (2), and thalamic spindles following UP to DOWN transitions in regime (3). The spindle-induced{sigma} -band activity in the cortical node, however, is typically strongest during the UP state, which follows a DOWN state "window of opportunity" for spindling. When the cortical node is parametrized in regime (3), the model well explains the interactions between slow oscillations and sleep spindles observed experimentally during Non-Rapid Eye Movement sleep. The model is computationally efficient and can be integrated into large-scale modeling frameworks to study spatial aspects like sleep wave propagation.

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bioRxiv2021

neurolib: a simulation framework for whole-brain neural mass modeling

neurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e. the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is possible using a parameter exploration module, which allows one to characterize the models behavior given a set of changing parameters. An optimization module can fit a model to multimodal empirical data using an evolutionary algorithm. neurolib is designed to be extendable such that custom neural mass models can be implemented easily, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure-function relationship of brain networks, and for performing in-silico optimization of whole-brain models.

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bioRxiv2020

The intersection between the representation of the stimuli and the choice by neural ensembles in the primary visual cortex of the macaque.

Animals frequently make decisions based on sensory cues. In such a setting, the overlap in the information on the stimulus and on the choice is crucial for the formation of informed behavioral decisions. Yet, how the information on the stimulus and on the choice interact in the brain is poorly understood. Here, we study the representation of a binary decision variable in the primary visual cortex (V1) while macaque monkeys perform delayed match-to-sample task on naturalistic visual stimuli close to psychophysical threshold. Using population vectors, we demonstrate the overlap in decoding spaces on binary stimulus classes "match/non-match" and binary choices "same /different" of the animal. Leveraging this overlap, we use learning from the invariant information across the two classification problems to predict the choice of the animal as a time-dependent population signal. We show the importance of the across-neuron organization and the temporal structure of spike trains for the decision signal and suggest how noise correlations between neurons with similar decoding selectivity are helpful for the accumulation of the decision signal. Finally, we show that decision signal is primarily carried by bursting neurons in the superficial layers of the cortex. Author summaryV1 is necessary for normal visual processing and is known to process features of visual stimuli such as orientation, but whether V1 also encodes behavioral decisions is an unresolved issue, with conflicting evidence. Here, we demonstrate that V1 encodes a mixed variable that contains the information about the stimulus as well as about the choice. We learn the structure of population responses in trials pertaining to the variable "stimulus+choice", and apply the resulting population vectors to trials that differ only about the choice of the animal, but not about the stimulus class. Moreover, we learn structure of population responses on time-averaged data and then apply it on time-dependent (spiking) data. During the late phase of the trial, this procedure allows to predict the upcoming choice of the animal with a time-dependent population signal. The spiking signal of small neural population is sparse, and we hypothesize that positive correlations between neurons in the same decoding pool help the transmission of the decision-related information downstream. We find that noise correlations in the same decoding pool are significantly stronger than across coding pools, which corroborates our hypothesis on the benefit of noise correlations for the read-out of a time-dependent population signal.

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bioRxiv2019

The structure of the population code in neural ensembles of V4 shapes pair-wise interactions on different time scales.

In visual areas of primates, neurons activate in parallel while the animal is engaged in a behavioral task. In this study, we examine the structure of the population code while the animal performs delayed match to sample task on complex natural images. The macaque monkeys visualized two consecutive stimuli that were either the same or different, while recorded with laminar arrays across the cortical depth in cortical areas V1 and V4. We decoded correct choice behavior from neural populations of simultaneously recorded units. Utilizing decoding weights, we divide neurons in most informative and less informative, and show that most informative neurons in V4, but not in V1, are more strongly synchronized, coupled and correlated than less informative neurons. As neurons are divided in two coding pools according to their coding preference, in V4, but not in V1, spiking synchrony, coupling and correlations within the coding pool are stronger than across coding pools. HighlightsO_LIIn a match-to-sample visual task, responses of neural populations in V1 and in V4 predict the stimulus class better than chance. C_LIO_LIIn V4, informative neurons are more strongly coupled, correlated and synchronized than less informative neurons. C_LIO_LIIn V4, neurons are more strongly coupled, correlated and synchronized within coding pools compared to across coding pools. C_LIO_LICorrelations within coding pools harm the performance of the classifier in both V1 and V4. C_LI

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bioRxiv2019

Reading-out task variables as a low-dimensional reconstruction of neural spike trains in single trials.

We propose a new model of the read-out of spike trains that exploits the multivariate structure of responses of neural ensembles. Assuming the point of view of a read-out neuron that receives synaptic inputs from a population of projecting neurons, synaptic inputs are weighted with a heterogeneous set of weights. We propose that synaptic weights reflect the role of each neuron within the population for the computational task that the network has to solve. In our case, the computational task is discrimination of binary classes of stimuli, and weights are such as to maximize the discrimination capacity of the network. We compute synaptic weights as the feature weights of an optimal linear classifier. Once weights have been learned, they weight spike trains and allow to compute the post-synaptic current that modulates the spiking probability of the read-out unit in real time. We apply the model on parallel spike trains from V1 and V4 areas in the behaving monkey macaca mulatta, while the animal is engaged in a visual discrimination task with binary classes of stimuli. The read-out of spike trains with our model allows to discriminate the two classes of stimuli, while population PSTH entirely fails to do so. Splitting neurons in two subpopulations according to the sign of the weight, we show that population signals of the two functional subnetworks are negatively correlated. Disentangling the superficial, the middle and the deep layer of the cortex, we show that in both V1 and V4, superficial layers are the most important in discriminating binary classes of stimuli.

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bioRxiv2018

Weak electric fields promote resonance in neuronal spiking activity: analytical results from two-compartment cell and network models

Transcranial brain stimulation and evidence of ephaptic coupling have sparked strong interests in understanding the effects of weak electric fields on the dynamics of neuronal populations. While their influence on the subthreshold membrane voltage can be biophysically well explained using spatially extended neuron models, mechanistic analyses of neuronal spiking and network activity have remained a methodological challenge. More generally, this challenge applies to phenomena for which single-compartment (point) neuron models are oversimplified. Here we employ a pyramidal neuron model that comprises two compartments, allowing to distinguish basal-somatic from apical dendritic inputs and accounting for an extracellular field in a biophysically minimalistic way. Using an analytical approach we fit its parameters to reproduce the response properties of a canonical, spatial model neuron and dissect the stochastic spiking dynamics of single cells and large networks. We show that oscillatory weak fields effectively mimic anti-correlated inputs at the soma and dendrite and strongly modulate neuronal spiking activity in a rather narrow frequency band. This effect carries over to coupled populations of pyramidal cells and inhibitory interneurons, boosting network-induced resonance in the beta and gamma frequency bands. Our work contributes a useful theoretical framework for mechanistic analyses of population dynamics going beyond point neuron models, and provides insights on modulation effects of extracellular fields due to the morphology of pyramidal cells.\n\nAuthor SummaryThe elongated spatial structure of pyramidal neurons, which possess large apical dendrites, plays an important role for the integration of synaptic inputs and mediates sensitivity to weak extracellular electric fields. Modeling studies at the population level greatly contribute to our mechanistic understanding but face a methodological challenge because morphologically detailed neuron models are too complex for use in noisy, in-vivo like conditions and large networks in particular. Here we present an analytical approach based on a two-compartment spiking neuron model that can distinguish synaptic inputs at the apical dendrite from those at the somatic region and accounts for an extracellular field in a biophysically minimalistic way. We devised efficient methods to approximate the responses of a spatially more detailed pyramidal neuron model, and to study the spiking dynamics of single neurons and sparsely coupled large networks in the presence of fluctuating inputs. Using these methods we focused on how responses are affected by oscillatory weak fields. Our results suggest that ephaptic coupling may play a mechanistic role for oscillations of population activity and indicate the potential to entrain networks by weak electric stimulation.

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bioRxiv2017

Differential polarization of cortical pyramidal neuron dendrites through weak extracellular fields

The rise of transcranial current stimulation (tCS) techniques have sparked an increasing interest in the effects of weak extracellular electric fields on neural activity. These fields modulate ongoing neural activity through polarization of the neuronal membrane. While the somatic polarization has been investigated experimentally, the frequency-dependent polarization of the dendritic trees in the presence of alternating (AC) fields has received little attention yet. Using a biophysically detailed model with experimentally constrained active conductances, we analyze the subthreshold response of cortical pyramidal cells to weak AC fields, as induced during tCS. We observe a strong frequency resonance around 10-20 Hz in the apical dendrites sensitivity to polarize in response to electric fields but not in the basal dendrites nor the soma. To disentangle the relative roles of the cell morphology and active and passive membrane properties in this resonance, we perform a thorough analysis using simplified models, e.g. a passive pyramidal neuron model, simple passive cables and reconstructed cell model with simplified ion channels. We attribute the origin of the resonance in the apical dendrites to (i) a locally increased sensitivity due to the morphology and to (ii) the high density of h-type channels. Our systematic study provides an improved understanding of the subthreshold response of cortical cells to weak electric fields and, importantly, allows for an improved design of tCS stimuli.

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