Biology subjects

Wibral, M.

Publications and source records attributed to Wibral, M..

15 recordsLinked to original sources

bioRxiv2026

Multivariate Transfer Entropy Quantifies Information Transfer Between Dyadic Partners During Real-Time Perceptual Decision-Making

1Many social behaviours unfold continuously in time, yet most studies of social influence rely on discrete, trial-based paradigms. To quantify how humans actually integrate sensory and social signals during ongoing perceptual decision-making we here apply an information-theoretic analysis to data obtained with the Continuous Perceptual Report (CPR) paradigm. In the CPR task, pairs of participants tracked a dynamic random-dot motion stimulus, continuously reporting perceived direction and their confidence. Crucially, each participant had access to their partners moment-by-moment direction and confidence reports embedded within the stimulus display. We quantified information flow between the stimulus and behavioural responses using multivariate transfer entropy, allowing us to detect whether participants acquired additional information from their partner once stimulus information had been received and integrated it into their behavioural response. We show that real-time social interactions predominantly occur within matching behavioural dimensions: partners perceptual choices influence participants own choices, and partners confidence influences participants confidence. Using both, more stimulus and social information contributed to improved task performance, and participants selectively acquired more information from better-performing partners. Crucially, information integration was flexibly modulated by stimulus reliability: as sensory evidence became noisier, participants relied more on social information, but only when interacting with a highly reliable (e.g. computer-controlled) partner. Latency analyses further revealed that participants responded faster to changes in their partners perceptual choices of direction than to changes in stimulus direction. They also responded more slowly to changes in their partners confidence than to changes in their partners choice of direction. Together, these findings demonstrate that humans dynamically and adaptively integrate social information in a time-continuous manner, and they establish multivariate information-theoretic analyses as a powerful framework for studying real-time social cognition.

animal behavior and cognition↗

bioRxiv2025

Correlations of neural predictability and information transfer in cortex and their relation to predictive coding

Predictive-coding like theories agree in describing top-down communication through the cortical hierarchy as a transmission of predictions generated by internal models of the inputs. With respect to the bottom-up connections, however, these theories differ in the neural processing strategies suggested for updating the internal model. Some theories suggest a coding strategy where unpredictable inputs, i.e., those not captured by the internal model, are passed on through the cortical hierarchy, whereas others claim that the predictable part of the inputs is passed on. Here, we addressed which neural coding strategy is employed in cortico-cortical connections using an information-theoretic approach. Our framework allows for quantifying two core aspects of both strategies, namely, predictability of inputs and information transfer, through local active information storage and local transfer entropy, respectively. A previous study on the neural processing of retinal ganglion cells connected to the lateral geniculate nucleus showed a coding for predictable information, captured by an increase in the information transfer with the predictability of inputs. Here, we further investigate predictive coding strategies at the cortical level. In particular, we analyzed LFP activity obtained from intracranial EEG recordings in humans and spike recordings from mouse cortex. We detected cortico-cortical connections with increasing information transfer with the predictability of inputs in recorded channels from frontal, parietal and temporal areas in human cortex. In the mouse visual system, we detected connections exhibiting both an increase and decrease in the information transfer with input predictability, although the former was predominant. Our evidence supports the presence of both predictive coding strategies at the cortical level, with a potential predominance of encoding for predictable information. SummaryThe ability of the brain to infer the hidden causes of sensory experiences has been conceptualized within the computational framework of predictive coding. This framework explains perceptual inference and learning as a process of constantly updating an internal model of the world. Predictive coding describes cortical activity as a communication of sensory evidence and predictions generated from prior expectations. While different views of predictive coding agree on the communication of prior expectations throughout cortex, they differ in how internal expectations are updated. One view states that, to update the internal model, the cortex propagates the mismatch between the expected neural activity and the actual neural response to sensory stimuli. In contrast, another view suggests that the cortex propagates the match between the expected neural activity and the actual neural response. In this work, we were able to tease apart these two views, both in human cortex and the mouse visual system, using information theory. We observed that the brain predominantly propagates expected information, i.e., the match between prior expectations and incoming sensory inputs.

neuroscience↗

bioRxiv2024

Inferring Effective Networks of Spiking Neurons Using a Continuous-Time Estimator of Transfer Entropy

When analysing high-dimensional time-series datasets, the inference of effective networks has proven to be a valuable modelling technique. This technique produces networks where each target node is associated with a set of source nodes that are capable of providing explanatory power for its dynamics. Multivariate Transfer Entropy (TE) has proven to be a popular and effective tool for inferring these networks. Recently, a continuous-time estimator of TE for event-based data such as spike trains has been developed which, in more efficiently representing event data in terms of inter-event intervals, is significantly more capable of measuring multivariate interactions. The new estimator thus presents an opportunity to more effectively use TE for the inference of effective networks from spike trains, and we demonstrate in this paper for the first time its efficacy at this task. Using data generated from models of spiking neurons -- for which the ground-truth connectivity is known -- we demonstrate the accuracy of this approach in various dynamical regimes. We further show that it exhibits far superior inference performance to a pairwise TE-based approach as well as a recently-proposed convolutional neural network approach. Moreover, comparison with Generalised Linear Models (GLMs), which are commonly applied to spike-train data, showed clear benefits, particularly in cases of high synchrony. Finally, we demonstrate its utility in revealing the patterns by which effective connections develop from recordings of developing neural cell cultures.

neuroscience↗

bioRxiv2024

Impaired Visuospatial Working Memory but Preserved Attentional Control in Bipolar Disorder

BACKGROUNDPersistent deficits in working memory (WM) and attention have considerable clinical and functional impact in people with bipolar disorder (PBD). Understanding the neurocognitive underpinnings of these interacting cognitive constructs might facilitate the discovery of more effective pro-cognitive interventions. Therefore, we employed a paradigm designed for jointly studying attentional control and WM encoding. METHODSWe used a visuospatial change-detection task using four Gabor Patches with differing orientations in 63 euthymic PBD and 76 healthy controls (HCS), which investigated attentional competition during WM encoding. To manipulate bottom-up attention using stimulus salience, two Gabor patches flickered, which were designated as either targets or distractors. To manipulate top-down attention, the Gabor patches were preceded by either a predictive or a non-predictive cue for the target locations. RESULTSAcross all task conditions, PBD stored significantly less information in visual WM than HCS (significant effect of group). However, we observed no significant group by salience or group by cue interactions. This indicates that impaired WM was not caused by deficits in attentional control. CONCLUSIONSOur results imply that while WM is disturbed in PBD, attentional prioritization of salient targets and distractors as well as the utilization of external top-down cues were not compromised. Consequently, the control of attentional selection appears be intact. These findings provide important constraints for models of WM dysfunction in PBD by indicating that later stages of WM encoding are likely primarily impaired. We also demonstrate that selective attention is not among the main sources of cognitive impairment in PBD.

neuroscience↗

bioRxiv2023

Understanding the neural mechanisms of emotion-cognition interaction via high resolution mapping in space, time, frequency, and information transfer

Interactions between emotional and cognitive processing play a critical role in determining human behavior. The ability to efficiently control these interactions is essential for goal-directed behavior. Previous research on emotion-cognition interaction in humans has primarily focused on its neural topography using techniques with either low temporal resolution (fMRI) or low spatial resolution (ERP), leaving the underlying neuronal mechanisms and their behavioral implications poorly understood. Here, we used EEG recordings from a large cohort of human participants (N=121) and employed source-reconstruction with finite-element head modeling during an emotional Flanker task. Our findings reveal a neural interaction between emotional interference and cognitive control within the pars triangularis of the right inferior frontal gyrus (rIFG). These results align spatially and conceptually with meta-analytic evidence from fMRI studies on interference inhibition processes, supporting the idea that the rIFG is crucial for interference inhibition, regardless of the interfering information type. Notably, the neural interaction between emotion and cognition in the rIFG was transient, peaking during the transition from emotional to cognitive processing, and was most pronounced in the beta-frequency band. Furthermore, conditional Granger-causality analysis of information flow indicated that the pars triangularis of the rIFG communicated with other functional subdivisions of the rIFG and parieto-occipital areas (such as the Precuneus and V2) in a frequency-specific manner. This information flow predicted individual emotional and cognitive interference effects on behavior. Overall, our findings highlight the functional and temporal segregation and integration of emotion, cognition, and their interaction in the rIFG. Furthermore, rIFGs top-down modulation of visuo-attentional areas, ultimately shaping behavioral performance, underscores the importance of stimulus interference inhibition for emotion-cognition interaction. Author summaryUnderstanding the interplay between emotion and cognition is crucial for understanding human behavior. Our study investigates the role of transient oscillatory activity using source-localized EEG in a large cohort. We identify a spatial overlap of the interaction of emotion and cognition processing in the rIFGs pars triangularis, aligning closely with localizations indicated by meta-analytic fMRI evidence. This interaction, primarily in the beta-frequency (around 20Hz) band, is transient, peaking during the shift from emotional to cognitive processing. Granger-causality analysis reveals frequency-specific communication patterns within the rIFG and parieto-occipital areas, predicting individual emotional and cognitive interference effects. Our findings elucidate the functional and temporal dynamics of emotion-cognition interaction within the rIFG, providing insights into the nuanced neural processes shaping human behavior.

neuroscience↗

bioRxiv2022

Information theoretic evidence for layer- and frequency-specific changes in cortical information processing under anesthesia

Nature relies on highly distributed computation for the processing of information in nervous systems across the entire animal kingdom. Such distributed computation can be more easily understood if decomposed into the three elementary components of information processing, i.e. storage, transfer and modification, and rigorous information theoretic measures for these components exist. However, the distributed computation is often also linked to neural dynamics exhibiting distinct rhythms. Thus, it would be beneficial to associate the above components of information processing with distinct rhythmic processes where possible. Here we focus on the storage of information in neural dynamics and introduce a novel spectrally-resolved measure of active information storage (AIS). Drawing on intracortical recordings of neural activity under anesthesia before and after loss of consciousness (LOC) we show that anesthesia-related modulation of AIS is highly specific to different frequency bands and that these frequency-specific effects differ across cortical layers and brain regions. We found that in the high/low gamma band the effects of anesthesia result in AIS modulation only in the supergranular layers, while in the alpha/beta band the strongest decrease in AIS can be seen at infragranular layers. Finally, we show that the increase of spectral power at multiple frequencies, in particular at alpha and delta bands in frontal areas, that is often observed during LOC ( anteriorization) also impacts local information processing - but in a frequency specific way: Increases in isoflurane concentration induced a decrease in AIS in the alpha frequencies, while they increased AIS in the delta frequency range < 2Hz. Thus, the analysis of spectrally-resolved AIS provides valuable additional insights into changes in cortical information processing under anaesthesia. Author SummaryWhile describing information processing in digital computers is somewhat straightforward and accessible (e.g. how much information is stored in a hard disk or which modification of information a CPU is executing), quantifying the widely distributed information processing in a biological neural system is much more challenging. In neural systems separating the components of distributed information processing - information transfer, storage and modification - helps with this task, but requires accurate mathematical definitions of these components of information processing. These definitions of distributed information processing quantities have become available only very recently. Of the three component processes mentioned above information storage, in particular, has been used with great success to analyze information processing in swarms, and to evolve, and optimize artificial information processing systems. The analysis of information storage has also already proven to be useful for the analysis of biological neural systems. Since in such systems, information processing seems to be often carried out by rhythmic neural activity with different frequencies, a measure of the frequency-specific components of the active information storage is needed. Here introduce such a measure and study how isoflurane anesthesia affects the local information processing in the ferret prefrontal and primary visual areas around loss of consciousness. We found that the modulation of active information storage by isoflurane is specific to frequency, layers and area, and that the analysis of frequency-specific active information storage provides insights not captured by more traditional descriptions of neural activity.

neuroscience↗

bioRxiv2021

Significant Subgraph Mining for Neural Network Inference with Multiple Comparisons Correction

We describe how the recently introduced method of significant subgraph mining can be employed as a useful tool in network comparison. It is applicable whenever the goal is to compare two sets of unweighted graphs and to determine differences in the processes that generate them. We provide an extension of the method to dependent graph generating processes as the occur for example in within-subject experimental designs. Furthermore, we present an extensive investigation of error-statistical properties of the method in simulation using Erd[o]s-Renyi models and in empirical data. In particular, we perform an empirical power analysis for transfer entropy networks inferred from resting state MEG data comparing autism spectrum patients with neurotypical controls. From this analysis one may estimate that the appropriate sample size for similar studies should be chosen in the order of n=60 per group or larger. Author summaryA key objective of neuroscientifc research is to determine how different parts of the brain are connected. The end result of such investigations is always a graph consisting of nodes corresponding to brain regions or nerve cells and edges between the nodes indicating if they are connected or not. The connections may be structural (an actual anatomical connection) but can also be functional - meaning that there is a statistical dependency between the activity in one part of the brain and the activity in another. A prime example of the latter type of connection would be the information flow between brain areas. Differences in the patterns of connectivity are likely to be responsible for and indicative of various neurological disorders such as autism spectrum disorders. It is therefore important that efficient methods to detect such differences are available. The key problem in developing methods for comparing patterns of connectivity is that there is generally a vast number of different patterns (it can easily exceed the number of stars in the milky way). In this paper we describe how the recently developed method of significant subgraph mining accounts for this problem and how it can be usefully employed in neuroscientifc research.

neuroscience↗

bioRxiv2021

Early lock-in of structured and specialised information flows during neural development

The brains of many organisms are capable of complicated distributed computation underpinned by a highly advanced information processing capacity. Although substantial progress has been made towards characterising the information flow component of this capacity in mature brains, there is a distinct lack of work characterising its emergence during neural development. This lack of progress has been largely driven by the lack of effective estimators of information processing operations for the spiking data available for developing neural networks. Here, we leverage recent advances in this estimation task in order to quantify the changes in information flow during development. We do so by studying the changes in the intrinsic dynamics of the spontaneous activity of developing dissociated neural cell cultures. We find that the quantity of information flowing across these networks undergoes a dramatic increase across development. Moreover, the spatial structure of these flows is locked-in during early development, after which there is a substantial temporal correlation in the information flows across recording days. We analyse the flow of information during the crucial periods of population bursts. We find that, during these bursts, nodes undertake specialised computational roles as either transmitters, mediators or receivers of information, with these roles tending to align with their spike ordering -- either early, mid or late in the bursts. Further, we find that the specialised computational roles occupied by nodes during bursts tend to be locked-in early. Finally, we briefly compare these results to information flows in a model network developing according to an STDP learning rule from a state of independent firing to synchronous bursting. The phenomena of large increases in information flow, early lock-in of information flow spatial structure and computational roles based on burst position were also observed in this model, hinting at the broader generality of these phenomena. AUTHOR SUMMARYThis paper studies the development of computation in biological systems by analysing changes in the flow of information in developing neural cell cultures. Although there have been a number of previous studies of information flows in neural cell cultures, this work represents the first study which compares information flows in the intrinsic dynamics across development time. Moreover, we make use of a recently proposed continuous-time transfer entropy estimator for spike trains, which, in comparison to the discrete-time estimator used previously, is able to capture important effects occurring on both small and large timescales simultaneously. We find that information flows begin to emerge after 5-10 days of activity, and crucially, the spatial structure of information flows remains significantly temporally correlated over the first month of recording. Furthermore, the magnitude of information flows across the culture are strongly related to burst position, and the roles of regions as information flow sources, sinks and mediators are found to remain consistent across development. Finally, we confirm that these early lock-ins also occur in a simple model network developing under an STDP update rule, suggesting a plausible mechanism undergirding this phenomenon.

neuroscience↗

bioRxiv2021

Impaired and Intact Aspects of Attentional Competition and Prioritization during Visual Working Memory Encoding in Schizophrenia

BackgroundPeople with schizophrenia (PSZ) are impaired in attentional prioritization of non-salient but relevant stimuli over salient distractors during visual working memory (VWM) encoding. Conversely, guidance of top-down attention by external predictive cues is intact. Yet, it is unknown whether this preserved ability can help PSZ encode more information in the presence of salient distractors. MethodsWe employed a visuospatial change-detection task using four Gabor patches with differing orientations in 66 PSZ and 74 healthy controls (HCS). Two Gabor patches flickered which were designated either as targets or distractors and either a predictive or a non-predictive cue was displayed to manipulate top-down attention, resulting in four conditions. ResultsWe observed significant effects of group, salience and cue as well as significant interactions of salience by cue, group by salience and group by cue. Across all conditions, PSZ stored significantly less information in VWM than HCS. PSZ stored significantly less non-flickering than flickering information with a non-predictive cue. However, PSZ stored significantly more flickering and non-flickering information with a predictive cue. ConclusionsOur findings indicate that control of attentional selection is impaired in schizophrenia. We demonstrate that additional top-down information significantly improves performance in PSZ. The observed deficit in attentional control suggests a disturbance of GABAergic inhibition in early visual areas. Moreover, our findings are indicative of a mechanism for enhancing attentional control in PSZ, which could be utilized by pro-cognitive interventions. Thus, the current paradigm is suitable to reveal both preserved and compromised cognitive component processes in schizophrenia.

neuroscience↗

bioRxiv2020

Embedding optimization reveals long-lasting history dependence in neural spiking activity

Information processing can leave distinct footprints on the statistics of neural spiking. For example, efficient coding minimizes the statistical dependencies on the spiking history, while temporal integration of information may require the maintenance of information over different timescales. To investigate these footprints, we developed a novel approach to quantify history dependence within the spiking of a single neuron, using the mutual information between the entire past and current spiking. This measure captures how much past information is necessary to predict current spiking. In contrast, classical time-lagged measures of temporal dependence like the autocorrelation capture how long--potentially redundant--past information can still be read out. Strikingly, we find for model neurons that our method disentangles the strength and timescale of history dependence, whereas the two are mixed in classical approaches. When applying the method to experimental data, which are necessarily of limited size, a reliable estimation of mutual information is only possible for a coarse temporal binning of past spiking, a so called past embedding. To still account for the vastly different spiking statistics and potentially long history dependence of living neurons, we developed an embedding-optimization approach that does not only vary the number and size, but also an exponential stretching of past bins. For extra-cellular spike recordings, we found that the strength and timescale of history dependence indeed can vary independently across experimental preparations. While hippocampus indicated strong and long history dependence, in visual cortex it was weak and short, while in vitro the history dependence was strong but short. This work enables an information theoretic characterization of history dependence in recorded spike trains, which captures a footprint of information processing that is beyond time-lagged measures of temporal dependence. To facilitate the application of the method, we provide practical guidelines and a toolbox. Author summaryEven with exciting advances in recording techniques of neural spiking activity, experiments only provide a comparably short glimpse into the activity of only a tiny subset of all neurons. How can we learn from these experiments about the organization of information processing in the brain? To that end, we exploit that different properties of information processing leave distinct footprints on the firing statistics of individual spiking neurons. In our work, we focus on a particular statistical footprint: How much does a single neurons spiking depend on its own preceding activity, which we call history dependence. By quantifying history dependence in neural spike recordings, one can, in turn, infer some of the properties of information processing. Because recording lengths are limited in practice, a direct estimation of history dependence from experiments is challenging. The embedding optimization approach that we present in this paper aims at extracting a maximum of history dependence within the limits set by a reliable estimation. The approach is highly adaptive and thereby enables a meaningful comparison of history dependence between neurons with vastly different spiking statistics, which we exemplify on a diversity of spike recordings. In conjunction with recent, highly parallel spike recording techniques, the approach could yield valuable insights on how hierarchical processing is organized in the brain.

neuroscience↗

bioRxiv2020

Measuring spectrally-resolved information transfer for sender- and receiver-specific frequencies

Information transfer, measured by transfer entropy, is a key component of distributed computation. It is therefore important to understand the pattern of information transfer in order to unravel the distributed computational algorithms of a system. Since in many natural systems distributed computation is thought to rely on rhythmic processes a frequency resolved measure of information transfer is highly desirable. Here, we present a novel algorithm, and its efficient implementation, to identify separately frequencies sending and receiving information in a network. Our approach relies on the invertible maximum overlap discrete wavelet transform (MODWT) for the creation of surrogate data in the computation of transfer entropy and entirely avoids filtering of the original signals. The approach thereby avoids well-known problems due to phase shifts or the ineffectiveness of filtering in the information theoretic setting. We also show that measuring frequency-resolved information transfer is a partial information decomposition problem that cannot be fully resolved to date and discuss the implications of this issue. Last, we evaluate the performance of our algorithm on simulated data and apply it to human magnetoencephalography (MEG) recordings and to local field potential recordings in the ferret. In human MEG we demonstrate top-down information flow in temporal cortex from very high frequencies (above 100Hz) to both similarly high frequencies and to frequencies around 20Hz, i.e. a complex spectral configuration of cortical information transmission that has not been described before. In the ferret we show that the prefrontal cortex sends information at low frequencies (4-8 Hz) to early visual cortex (V1), while V1 receives the information at high frequencies (> 125 Hz). Author SummarySystems in nature that perform computations typically consist of a large number of relatively simple but interacting parts. In human brains, for example, billions of neurons work together to enable our cognitive abilities. This well-orchestrated teamwork requires information to be exchanged very frequently. In many cases this exchange happens rhythmically and, therefore, it seems beneficial for our understanding of physical systems if we could link the information exchange to specific rhythms. We here present a method to determine which rhythms send, and which rhythms receive information. Since many rhythms can interact at both sender and receiver side, we show that the interpretation of results always needs to consider that the above problem is tightly linked to partial information decomposition - an intriguing problem from information theory only solved recently, and only partly. We applied our novel method to information transfer in the human inferior temporal cortex, a brain region relevant for object perception, and unexpectedly found information transfer originating at very high frequencies at 100Hz and then forking to be received at both similarly high but also much lower frequencies around 20Hz. These results overturn the current standard assumption that low frequencies send information to high frequencies.

neuroscience↗

bioRxiv2020

Cortical network mechanisms of response inhibition

Both the right inferior frontal gyrus (rIFG) and the pre-supplementary motor area (pre-SMA) are crucial for successful response inhibition. However, the particular functional roles of those two regions have been controversially debated for more than a decade now. It is unclear whether the rIFG directly initiates stopping or serves an attentional function, whereas the stopping is triggered by the pre-SMA. The current multimodal MEG/fMRI study sought to clarify the role and temporal activation order of both regions in response inhibition using a selective stopping task. This task dissociates inhibitory from attentional processes. Our results reliably reveal a temporal precedence of rIFG over pre-SMA. Moreover, connectivity during response inhibition is directed from rIFG to pre-SMA and predicts stopping performance. Response inhibition is implemented via beta-band oscillations. Our findings support the hypothesis that response inhibition is initiated by the rIFG as a form of attention-independent top-down control.

neuroscience↗

bioRxiv2018

Perceptual phenotypes: Perceptual gains and losses in synesthesia and schizophrenia

Individual differences in perception are widespread. Considering inter-individual variability, synesthetes experience stable additional sensations; schizophrenia patients suffer perceptual deficits in e.g. perceptual organization (alongside hallucinations and delusions). Is there a unifying principle explaining inter-individual variability in perception? There is good reason to believe perceptual experience results from inferential processes whereby sensory evidence is weighted by prior knowledge about the world. Different perceptual phenotypes may result from different precision weighting of sensory evidence and prior knowledge. We tested this hypothesis by comparing visibility thresholds in a perceptual hysteresis task across medicated schizophrenia patients, synesthetes, and controls. Participants rated the subjective visibility of stimuli embedded in noise while we parametrically manipulated the availability of sensory evidence. Additionally, precise long-term priors in synesthetes were leveraged by presenting either synesthesia-inducing or neutral stimuli. Schizophrenia patients showed increased visibility thresholds, consistent with overreliance on sensory evidence. In contrast, synesthetes exhibited lowered thresholds exclusively for synesthesia-inducing stimuli suggesting high-precision long-term priors. Additionally, in both synesthetes and schizophrenia patients explicit, short-term priors - introduced during the hysteresis experiment - lowered thresholds but did not normalize perception. Our results imply that distinct perceptual phenotypes might result from differences in the precision afforded to prior beliefs and sensory evidence, respectively.

neuroscience↗

bioRxiv2017

Predictive coding over the lifespan: Increased reliance on perceptual priors in older adults -- a magnetoencephalography and dynamic causal modelling study

Aging is accompanied by unisensory decline; but to compensate for this, two complementary strategies are potentially relied upon increasingly: first, older adults integrate more information from different sensory organs. Second, according to predictive coding (PC) we form templates (internal models or priors) of the environment through our experiences. It is through increased life experience that older adults may rely more on these templates compared to younger adults. Multisensory integration and predictive coding would be effective strategies for the perception of near-threshold stimuli, but they come at the cost of integrating irrelevant information. Their role can be studied in multisensory illusions because these require the integration of different sensory information, as well as an internal model of the world that can take precedence over sensory input. Here, we elicited a classic multisensory illusion, the sound-induced flash illusion, in younger (mean: 27 yrs) and older (mean: 67 yrs) adult participants while recording the magnetoencephalogram. Older adults perceived more illusions than younger adults. Older adults had increased pre-stimulus beta({beta})-band activity compared to younger adults as predicted by microcircuit theories of predictive coding, which suggest priors and predictions are linked to {beta}-band activity. In line with our hypothesis, transfer entropy analysis and dynamic causal models of pre-stimulus MEG data revealed a stronger illusion-related modulation of cross-modal connectivity from auditory to visual cortices in older compared to younger adults. We interpret this as the neural correlate of increased reliance on a cross-modal predictive template in older adults that is leading to the illusory percept.

neuroscience↗

bioRxiv2016

Activating task relevant prior knowledge increases active information storage in content specific brain areas

Predictive coding suggests that the brain infers the causes of its sensations by combining sensory evidence with internal predictions based on available prior knowledge. However, the neurophysiological correlates of (pre-)activated prior knowledge serving these predictions are still unknown. Based on the idea that such pre-activated prior knowledge must be maintained until needed we measured the amount of maintained information in neural signals via the active information storage (AIS) measure. AIS was calculated on whole-brain beamformer-reconstructed source time-courses from magnetoencephalography (MEG) recordings of 52 human subjects during the baseline of a Mooney face/house detection task. Pre-activation of prior knowledge for faces showed as alpha- and beta-band related AIS increases in content specific areas; these AIS increases were behaviourally relevant in brain area FFA. Further, AIS allowed decoding of the cued category on a trial-by-trial basis. Moreover, top-down transfer of predictions estimated by transfer entropy was associated with beta frequencies. Our results support accounts that activated prior knowledge and the corresponding predictions are signalled in low-frequency activity (<30 Hz).\n\nSignificance statementOur perception is not only determined by the information our eyes/retina and other sensory organs receive from the outside world, but strongly depends also on information already present in our brains like prior knowledge about specific situations or objects. A currently popular theory in neuroscience, predictive coding theory, suggests that this prior knowledge is used by the brain to form internal predictions about upcoming sensory information. However, neurophysiological evidence for this hypothesis is rare - mostly because this kind of evidence requires making strong a-priori assumptions about the specific predictions the brain makes and the brain areas involved. Using a novel, assumption-free approach we find that face-related prior knowledge and the derived predictions are represented and transferred in low-frequency brain activity.

neuroscience↗