1,721,082 research outputs found
Testing Predictive Coding through an Information-Theoretic Analysis of Intracranial EEG Recordings
No full textThe predictive coding principle posits that the brain encodes a model of the world, whose predictions are compared against incoming sensory data. Predictive coding-like theories, i.e. those that rely on this principle, suggest two types of coding strategies: an error coding strategy, where mismatches between the predictions of our world model and incoming sensory data are propagated forward through the cortex, and a coding for reliable information, where matches are further propagated. Here, we investigated the presence of such predictive coding strategies at the cortical level and which type the brain unfolds. Specifically, we studied the information transfer from one cortical area (the source) to another one (the target), quantified with local transfer entropy. On the other hand, we computed the predictability of the activity in the source cortical area with local active information storage. We analyzed intracranial EEG recordings of nine epileptic patients during a face classification task. Across all patients we identified 16 connections with a positive Pearson correlation (p
< 10−6, Bonferroni correction) between the information transfer to the target brain area and the predictability of the source area. The correlations ranged between r=0.019 and r=0.093. We conclude that our framework is capable of detecting signatures of predictive processing strategies at the cortical level, given by the presence of these correlations. Furthermore, the positive sign of them provides evidence that our data reflect a coding strategy for reliable information
A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition
No full textBits and pieces: understanding information decomposition from part-whole relationships and formal logic
No full textPartial information decomposition (PID) seeks to decompose the multivariate mutual information that a set of source variables contains about a target variable into basic pieces, the so-called ‘atoms of information’. Each atom describes a distinct way in which the sources may contain information about the target. For instance, some information may be contained uniquely in a particular source, some information may be shared by multiple sources and some information may only become accessible synergistically if multiple sources are combined. In this paper, we show that the entire theory of PID can be derived, firstly, from considerations of part-whole relationships between information atoms and mutual information terms, and secondly, based on a hierarchy of logical constraints describing how a given information atom can be accessed. In this way, the idea of a PID is developed on the basis of two of the most elementary relationships in nature: the part-whole relationship and the relation of logical implication. This unifying perspective provides insights into pressing questions in the field such as the possibility of constructing a PID based on concepts other than redundant information in the general n-sources case. Additionally, it admits of a particularly accessible exposition of PID theory
Significant subgraph mining for neural network inference with multiple comparisons correction
Full text linkAbstractWe describe how the recently introduced method of significant subgraph mining can be employed as a useful tool in neural 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 they occur, for example, in within-subject experimental designs. Furthermore, we present an extensive investigation of the error-statistical properties of the method in simulation using Erdős-Rényi models and in empirical data in order to derive practical recommendations for the application of subgraph mining in neuroscience. 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. Finally, we provide a Python implementation as part of the openly available IDTxl toolbox
Information theoretic evidence for layer- and frequency-specific changes in cortical information processing under anesthesia
No full textNature 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 in ferrets 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 Hz. Thus, the analysis of spectrally-resolved AIS provides valuable additional insights into changes in cortical information processing under anaesthesia.Local field potential recordings in prefrontal and primary visual areas of the ferrets' cortex (at Supragranular, Granular and Infragranular layer) in the awake state and after administration of isoflurane in concentrations of 0.5%, 0.75% and 1.0 %.Data were recorded by K. Sellers from F. Fröhlich lab
Where is the error? Hierarchical predictive coding through dendritic error computation: (Trends in Neurosciences 46, 45–59; 2023)
No full textSubsampling effects in neuronal avalanche distributions recorded in vivo
No full textBackground Many systems in nature are characterized by complex behaviour where large cascades of events, or avalanches, unpredictably alternate with periods of little activity. Snow avalanches are an example. Often the size distribution f(s) of a system's avalanches follows a power law, and the branching parameter sigma, the average number of events triggered by a single preceding event, is unity. A power law for f(s), and sigma=1, are hallmark features of self-organized critical (SOC) systems, and both have been found for neuronal activity in vitro. Therefore, and since SOC systems and neuronal activity both show large variability, long-term stability and memory capabilities, SOC has been proposed to govern neuronal dynamics in vivo. Testing this hypothesis is difficult because neuronal activity is spatially or temporally subsampled, while theories of SOC systems assume full sampling. To close this gap, we investigated how subsampling affects f(s) and sigma by imposing subsampling on three different SOC models. We then compared f(s) and sigma of the subsampled models with those of multielectrode local field potential (LFP) activity recorded in three macaque monkeys performing a short term memory task. Results Neither the LFP nor the subsampled SOC models showed a power law for f(s). Both, f(s) and sigma, depended sensitively on the subsampling geometry and the dynamics of the model. Only one of the SOC models, the Abelian Sandpile Model, exhibited f(s) and sigma similar to those calculated from LFP activity. Conclusions Since subsampling can prevent the observation of the characteristic power law and sigma in SOC systems, misclassifications of critical systems as sub- or supercritical are possible. Nevertheless, the system specific scaling of f(s) and sigma under subsampling conditions may prove useful to select physiologically motivated models of brain function. Models that better reproduce f(s) and sigma calculated from the physiological recordings may be selected over alternatives
HIGH-FREQUENCY GAMMA-BAND OSCILLATIONS DURING PERCEPTUAL ORGANISATION IN CHRONIC AND FIRST-EPISODE SCHIZOPHRENIA PATIENTS
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