Publications

Abstract

Biologically inspired spiking networks are an important tool to study the nature of computation and cognition in neural systems. In this work, we investigate the representational capacity of spiking networks engaged in an identity mapping task. We compare two schemes for encoding symbolic input, one in which input is injected as a direct current and one where input is delivered as a spatio-temporal spike pattern. We test the ability of networks to discriminate their input as a function of the number of distinct input symbols. We also compare performance using either membrane potentials or filtered spike trains as state variable. Furthermore, we investigate how the circuit behavior depends on the balance between excitation and inhibition, and the degree of synchrony and regularity in its internal dynamics. Finally, we compare different linear methods of decoding population activity onto desired target labels. Overall, our results suggest that even this simple mapping task is strongly influenced by design choices on input encoding, state-variables, circuit characteristics and decoding methods, and these factors can interact in complex ways. This work highlights the importance of constraining computational network models of behavior by available neurobiological evidence.

Abstract

The cause of developmental dyslexia is still unknown despite decades of intense research. Many causal explanations have been proposed, based on the range of impairments displayed by affected individuals. Here we draw attention to the fact that many of these impairments are also shown by illiterate individuals who have not received any or very little reading instruction. We suggest that this fact may not be coincidental and that the performance differences of both illiterates and individuals with dyslexia compared to literate controls are, to a substantial extent, secondary consequences of either reduced or suboptimal reading experience or a combination of both. The search for the primary causes of reading impairments will make progress if the consequences of quantitative and qualitative differences in reading experience are better taken into account and not mistaken for the causes of reading disorders. We close by providing four recommendations for future research.

Abstract

This study investigates the implicit sequence learning abilities of dyslexic children using an artificial grammar learning task with an extended exposure period. Twenty children with developmental dyslexia participated in the study and were matched with two control groups—one matched for age and other for reading skills. During 3 days, all participants performed an acquisition task, where they were exposed to colored geometrical forms sequences with an underlying grammatical structure. On the last day, after the acquisition task, participants were tested in a grammaticality classification task. Implicit sequence learning was present in dyslexic children, as well as in both control groups, and no differences between groups were observed. These results suggest that implicit learning deficits per se cannot explain the characteristic reading difficulties of the dyslexics.

Abstract

Recently, it has been proposed that sequence learning engages a combination of modality-specific operating networks and modality-independent computational principles. In the present study, we compared the behavioural and EEG outcomes of implicit artificial grammar learning in the visual vs. auditory modality. We controlled for the influence of surface characteristics of sequences (Associative Chunk Strength), thus focusing on the strictly structural aspects of sequence learning, and we adapted the paradigms to compensate for known frailties of the visual modality compared to audition (temporal presentation, fast presentation rate). The behavioural outcomes were similar across modalities. Favouring the idea of modality-specificity, ERPs in response to grammar violations differed in topography and latency (earlier and more anterior component in the visual modality), and ERPs in response to surface features emerged only in the auditory modality. In favour of modality-independence, we observed three common functional properties in the late ERPs of the two grammars: both were free of interactions between structural and surface influences, both were more extended in a grammaticality classification test than in a preference classification test, and both correlated positively and strongly with theta event-related-synchronization during baseline testing. Our findings support the idea of modality-specificity combined with modality-independence, and suggest that memory for visual vs. auditory sequences may largely contribute to cross-modal differences.

Abstract

Several studies have reported an association between dyslexia and implicit learning deficits. It has been suggested that the weakness in implicit learning observed in dyslexic individuals may be related to sequential processing and implicit sequence learning. In the present article, we review the current literature on implicit learning and dyslexia. We describe a novel, forced-choice structural "mere exposure" artificial grammar learning paradigm and characterize this paradigm in normal readers in relation to the standard grammaticality classification paradigm. We argue that preference classification is a more optimal measure of the outcome of implicit acquisition since in the preference version participants are kept completely unaware of the underlying generative mechanism, while in the grammaticality version, the subjects have, at least in principle, been informed about the existence of an underlying complex set of rules at the point of classification (but not during acquisition). On the basis of the "mere exposure effect," we tested the prediction that the development of preference will correlate with the grammaticality status of the classification items. In addition, we examined the effects of grammaticality (grammatical/nongrammatical) and associative chunk strength (ACS; high/low) on the classification tasks (preference/grammaticality). Using a balanced ACS design in which the factors of grammaticality (grammatical/nongrammatical) and ACS (high/low) were independently controlled in a 2 × 2 factorial design, we confirmed our predictions. We discuss the suitability of this task for further investigation of the implicit learning characteristics in dyslexia.

Abstract

Human implicit learning can be investigated with implicit artificial grammar learning, a paradigm that has been proposed as a simple model for aspects of natural language acquisition. In the present study we compared the typical yes–no grammaticality classification, with yes–no preference classification. In the case of preference instruction no reference to the underlying generative mechanism (i.e., grammar) is needed and the subjects are therefore completely uninformed about an underlying structure in the acquisition material. In experiment 1, subjects engaged in a short-term memory task using only grammatical strings without performance feedback for 5 days. As a result of the 5 acquisition days, classification performance was independent of instruction type and both the preference and the grammaticality group acquired relevant knowledge of the underlying generative mechanism to a similar degree. Changing the grammatical stings to random strings in the acquisition material (experiment 2) resulted in classification being driven by local substring familiarity. Contrasting repeated vs. non-repeated preference classification (experiment 3) showed that the effect of local substring familiarity decreases with repeated classification. This was not the case for repeated grammaticality classifications. We conclude that classification performance is largely independent of instruction type and that forced-choice preference classification is equivalent to the typical grammaticality classification.

Abstract

Cognitive neuroscience approaches the brain as a cognitive system: a system that functionally is conceptualized in terms of information processing. We outline some aspects of this concept and consider a physical system to be an information processing device when a subclass of its physical states can be viewed as representational/cognitive and transitions between these can be conceptualized as a process operating on these states by implementing operations on the corresponding representational structures. We identify a generic and fundamental problem in cognition: sequentially organized structured processing. Structured sequence processing provides the brain, in an essential sense, with its processing logic. In an approach addressing this problem, we illustrate how to integrate levels of analysis within a framework of adaptive dynamical systems. We note that the dynamical system framework lends itself to a description of asynchronous event-driven devices, which is likely to be important in cognition because the brain appears to be an asynchronous processing system. We use the human language faculty and natural language processing as a concrete example through out.

Abstract

Remembering complex, multidimensional information typically requires strategic memory retrieval, during which information is structured, for instance by spatial- or temporal associations. Although brain regions involved in strategic memory retrieval in general have been identified, differences in retrieval operations related to distinct retrieval strategies are not well-understood. Thus, our aim was to identify brain regions whose activity is differentially involved in spatial-associative and temporal-associative retrieval. First, we showed that our behavioral paradigm probing memory for a set of object-location associations promoted the use of a spatial-associative structure following an encoding condition that provided multiple associations to neighboring objects (spatial-associative condition) and the use of a temporal- associative structure following another study condition that provided predominantly temporal associations between sequentially presented items (temporal-associative condition). Next, we used an adapted version of this paradigm for functional MRI, where we contrasted brain activity related to the recall of object-location associations that were either encoded in the spatial- or the temporal-associative condition. In addition to brain regions generally involved in recall, we found that activity in higher-order visual regions, including the fusiform gyrus, the lingual gyrus, and the cuneus, was relatively enhanced when subjects used a spatial-associative structure for retrieval. In contrast, activity in the globus pallidus and the thalamus was relatively enhanced when subjects used a temporal-associative structure for retrieval. In conclusion, we provide evidence for differential involvement of these brain regions related to different types of strategic memory retrieval and the neural structures described play a role in either spatial-associative or temporal-associative memory retrieval.

Abstract

We used simultaneously recorded EEG and fMRI to investigate in which areas the BOLD signal correlates with frontal theta power changes, while subjects were quietly lying resting in the scanner with their eyes open. To obtain a reliable estimate of frontal theta power we applied ICA on band-pass filtered (2–9 Hz) EEG data. For each subject we selected the component that best matched the mid-frontal scalp topography associated with the frontal theta rhythm. We applied a time-frequency analysis on this component and used the time course of the frequency bin with the highest overall power to form a regressor that modeled spontaneous fluctuations in frontal theta power. No significant positive BOLD correlations with this regressor were observed. Extensive negative correlations were observed in the areas that together form the default mode network. We conclude that frontal theta activity can be seen as an EEG index of default mode network activity.

Abstract

We investigated how the hippocampus and its adjacent mediotemporal structures contribute to contextual and noncontextual declarative memory retrieval by manipulating the amount of contextual information across two levels of the same contextual dimension in a source memory task. A first analysis identified medial temporal lobe (MTL) substructures mediating either contextual or noncontextual retrieval. A linearly weighted analysis elucidated which MTL substructures show a gradually increasing neural activity, depending on the amount of contextual information retrieved. A hippocampal engagement was found during both levels of source memory but not during item memory retrieval. The anterior MTL including the perirhinal cortex was only engaged during item memory retrieval by an activity decrease. Only the posterior parahippocampal cortex showed an activation increasing with the amount of contextual information retrieved. If one assumes a roughly linear relationship between the blood-oxygenation level-dependent (BOLD) signal and the associated cognitive process, our results suggest that the posterior parahippocampal cortex is involved in contextual retrieval on the basis of memory strength while the hippocampus processes representations of item-context binding. The anterior MTL including perirhinal cortex seems to be particularly engaged in familiarity-based item recognition. If one assumes departure from linearity, however, our results can also be explained by one-dimensional modulation of memory strength.

Abstract

The human capacity to implicitly acquire knowledge of structured sequences has recently been investigated in artificial grammar learning using functional magnetic resonance imaging. It was found that the left inferior frontal cortex (IFC; Brodmann's area (BA) 44/45) was related to classification performance. The objective of this study was to investigate whether the IFC (BA 44/45) is causally related to classification of artificial syntactic structures by means of an off-line repetitive transcranial magnetic stimulation (rTMS) paradigm. We manipulated the stimulus material in a 2 × 2 factorial design with grammaticality status and local substring familiarity as factors. The participants showed a reliable effect of grammaticality on classification of novel items after 5days of exposure to grammatical exemplars without performance feedback in an implicit acquisition task. The results show that rTMS of BA 44/45 improves syntactic classification performance by increasing the rejection rate of non-grammatical items and by shortening reaction times of correct rejections specifically after left-sided stimulation. A similar pattern of results is observed in FMRI experiments on artificial syntactic classification. These results suggest that activity in the inferior frontal region is causally related to artificial syntax processing.

Abstract

The acute neural effects of progesterone are mediated by its neuroactive metabolites allopregnanolone and pregnanolone. These neurosteroids potentiate the inhibitory actions of c-aminobutyric acid (GABA). Progesterone is known to produce anxiolytic effects in animals, but recent animal studies suggest that pregnanolone increases anxiety after a period of low allopregnanolone concentration. This effect is potentially mediated by the amygdala and related to the negative mood symptoms in humans that are observed during increased allopregnanolone levels. Therefore, we investigated with functional magnetic resonance imaging (MRI) whether a single progesterone administration to healthy young women in their follicular phase modulates the amygdala response to salient, biologically relevant stimuli. The progesterone administration increased the plasma concentrations of progesterone and allopregnanolone to levels that are reached during the luteal phase and early pregnancy. The imaging results show that progesterone selectively increased amygdala reactivity. Furthermore, functional connectivity analyses indicate that progesterone modulated functional coupling of the amygdala with distant brain regions. These results reveal a neural mechanism by which progesterone may mediate adverse effects on anxiety and mood.

Abstract

Purpose of review: The human brain supports acquisition mechanisms that can extract structural regularities implicitly from experience without the induction of an explicit model. Reber defined the process by which an individual comes to respond appropriately to the statistical structure of the input ensemble as implicit learning. He argued that the capacity to generalize to new input is based on the acquisition of abstract representations that reflect underlying structural regularities in the acquisition input. We focus this review of the implicit learning literature on studies published during 2004 and 2005. We will not review studies of repetition priming ('implicit memory'). Instead we focus on two commonly used experimental paradigms: the serial reaction time task and artificial grammar learning. Previous comprehensive reviews can be found in Seger's 1994 article and the Handbook of Implicit Learning. Recent findings: Emerging themes include the interaction between implicit and explicit processes, the role of the medial temporal lobe, developmental aspects of implicit learning, age-dependence, the role of sleep and consolidation. Summary: The attempts to characterize the interaction between implicit and explicit learning are promising although not well understood. The same can be said about the role of sleep and consolidation. Despite the fact that lesion studies have relatively consistently suggested that the medial temporal lobe memory system is not necessary for implicit learning, a number of functional magnetic resonance studies have reported medial temporal lobe activation in implicit learning. This issue merits further research. Finally, the clinical relevance of implicit learning remains to be determined.

Abstract

In this paper we investigate the neural correlates of syntactic classification of an acquired grammatical sequence structure in an event-related FMRI study. During acquisition, participants were engaged in an implicit short-term memory task without performance feedback. We manipulated the statistical frequency-based and rule-based characteristics of the classification stimuli independently in order to investigate their role in artificial grammar acquisition. The participants performed reliably above chance on the classification task. We observed a partly overlapping corticostriatal processing network activated by both manipulations including inferior prefrontal, cingulate, inferior parietal regions, and the caudate nucleus. More specifically, the left inferior frontal BA 45 and the caudate nucleus were sensitive to syntactic violations and endorsement, respectively. In contrast, these structures were insensitive to the frequency-based manipulation.

Abstract

The posterior medial parietal cortex and left prefrontal cortex (PFC) have both been implicated in the recollection of past episodes. In a previous study, we found the posterior precuneus and left lateral inferior frontal cortex to be activated during episodic source memory retrieval. This study further examines the role of posterior precuneal and left prefrontal activation during episodic source memory retrieval using a similar source memory paradigm but with longer latency between encoding and retrieval. Our results suggest that both the precuneus and the left inferior PFC are important for regeneration of rich episodic contextual associations and that the precuneus activates in tandem with the left inferior PFC during correct source retrieval. Further, results suggest that the left ventro-lateral frontal region/ frontal operculum is involved in searching for task-relevant information (BA 47) and subsequent monitoring or scrutiny (BA 44/45) while regions in the dorsal inferior frontal cortex are important for information selection (BA 45/46).

Abstract

Recent FMRI studies indicate that language related brain regions are engaged in artificial grammar (AG) processing. In the present study we investigate the Reber grammar by means of formal analysis and network simulations. We outline a new method for describing the network dynamics and propose an approach to grammar extraction based on the state-space dynamics of the network. We conclude that statistical frequency-based and rule-based acquisition procedures can be viewed as complementary perspectives on grammar learning, and more generally, that classical cognitive models can be viewed as a special case of a dynamical systems perspective on information processing

Abstract

We present two simple arguments for the potential relevance of a neurobiological analogue of the finite-state architecture. The first assumes the classical cognitive framework, is wellknown, and is based on the assumption that the brain is finite with respect to its memory organization. The second is formulated within a general dynamical systems framework and is based on the assumption that the brain sustains some level of noise and/or does not utilize infinite precision processing. We briefly review the classical cognitive framework based on Church–Turing computability and non-classical approaches based on analog processing in dynamical systems. We conclude that the dynamical neurobiological analogue of the finitestate architecture appears to be relevant, at least at an implementational level, for cognitive brain systems