Biomedical subjects

Martin Stetter

Publications and source records attributed to Martin Stetter.

9 recordsLinked to original sources

PubMed2006

Learning to attend: modeling the shaping of selectivity in infero-temporal cortex in a categorization task.

Recent experiments on behaving monkeys have shown that learning a visual categorization task makes the neurons in infero-temporal cortex (ITC) more selective to the task-relevant features of the stimuli (Sigala and Logothetis in Nature 415 318-320, 2002). We hypothesize that such a selectivity modulation emerges from the interaction between ITC and other cortical area, presumably the prefrontal cortex (PFC), where the previously learned stimulus categories are encoded. We propose a biologically inspired model of excitatory and inhibitory spiking neurons with plastic synapses, modified according to a reward based Hebbian learning rule, to explain the experimental results and test the validity of our hypothesis. We assume that the ITC neurons, receiving feature selective inputs, form stronger connections with the category specific neurons to which they are consistently associated in rewarded trials. After learning, the top-down influence of PFC neurons enhances the selectivity of the ITC neurons encoding the behaviorally relevant features of the stimuli, as observed in the experiments. We conclude that the perceptual representation in visual areas like ITC can be strongly affected by the interaction with other areas which are devoted to higher cognitive functions.

Algorithms↗

PubMed2006

Dynamic functional tuning of nonlinear cortical networks.

The mammalian neocortex is a highly complex and nonlinear dynamic system. One of its most prominent features is an omnipresent spontaneous neuronal activity. Here the possible functional role of this global background for cognitive flexibility is studied in a prototypic mean-field model area. It is demonstrated that the level of global background current efficiently controls the stimulus-response threshold and the stability and properties of short-term memory states. Moreover, it can dynamically gate arbitrary cortical subnetworks, when applied to parts of the area as a weak bias signal. These results suggest a central functional role of the level of background activation: the dynamic functional tuning of neocortical circuits.

Action Potentials↗

PubMed2005

Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data.

In the present study, we applied the Support Vector Machine (SVM) algorithm to perform multivariate classification of brain states from whole functional magnetic resonance imaging (fMRI) volumes without prior selection of spatial features. In addition, we did a comparative analysis between the SVM and the Fisher Linear Discriminant (FLD) classifier. We applied the methods to two multisubject attention experiments: a face matching and a location matching task. We demonstrate that SVM outperforms FLD in classification performance as well as in robustness of the spatial maps obtained (i.e. discriminating volumes). In addition, the SVM discrimination maps had greater overlap with the general linear model (GLM) analysis compared to the FLD. The analysis presents two phases: during the training, the classifier algorithm finds the set of regions by which the two brain states can be best distinguished from each other. In the next phase, the test phase, given an fMRI volume from a new subject, the classifier predicts the subject's instantaneous brain state.

Aged↗

PubMed2004

Mining functional modules in genetic networks with decomposable graphical models.

In recent years, graphical models have become an increasingly important tool for the structural analysis of genome-wide expression profiles at the systems level. Here we present a new graphical modelling technique, which is based on decomposable graphical models, and apply it to a set of gene expression profiles from acute lymphoblastic leukemia (ALL). The new method explains probabilistic dependencies of expression levels in terms of the concerted action of underlying genetic functional modules, which are represented as so-called "cliques" in the graph. In addition, the method uses continuous-valued (instead of discretized) expression levels, and makes no particular assumption about their probability distribution. We show that the method successfully groups members of known functional modules to cliques. Our method allows the evaluation of the importance of genes for global cellular functions based on both link count and the clique membership count.

Gene Expression Profiling↗

PubMed2004

Identifying interventional and pathogenic mechanisms by generative inverse modeling of gene expression profiles.

MOTIVATION: The characterization of genetic mechanisms underlying normal cellular function, cancer development, pathogenesis, and the effect of drug treatment is one of the most challenging topics for cancer research and molecular biology. Existing methods for inferring genetic regulatory networks from genome-wide expression profiles provide important information about gene interactions and regulatory relationships. However, these methods do not provide information about the impact of possible interventions or changes on such regulatory networks to study cause-effect relationships at a systems-biology level. RESULTS: We present a data-driven method called generative inverse modeling, which simulates the effect of local genetic changes on the global cellular state, as reflected by an altered genome-wide expression profile. For each genetic change we define a pathogenic score by calculating to what extent it transforms the simulated expression patterns into patterns measured for pathologically altered tissues. The method can be used to estimate the relevance of genes for disease-specific genetic mechanisms, e.g., as presented here for pathogenesis. Generative inverse modeling is based on a Bayesian probability density estimation from a set of measured gene-expression patterns.

Computational Biology↗

PubMed2004

Hunting drug targets by systems-level modeling of gene expression profiles.

Structural learning of Bayesian networks applied to sets of genome-wide expression patterns has been recently discovered as a potentially useful tool for the systems-level statistical description of gene interactions. We train and analyze Bayesian networks with the goal of inferring biological aspects of gene function. Our two-component approach focuses on supporting the drug discovery process by identifying genes with central roles for the network operation, which could act as drug targets. The first component, referred to as scale-free analysis, uses topological measures of the network-related to a high-traffic load of genes-as estimators for their functional importance. The second component, referred to as generative inverse modeling, is a method of estimating the effect of a simulated drug treatment or mutation on the global state of the network, as measured in the expression profile. We show for a dataset from acute lymphoblastic leukemia patients that both approaches are suitable for finding genes with central cellular functions. In addition, generative inverse modeling correctly identifies a known oncogene in a purely data-driven way.

Algorithms↗

PubMed2004

Cooperation and biased competition model can explain attentional filtering in the prefrontal cortex.

Recent neurophysiological experimental results suggest that the prefrontal cortex plays an important role in filtering out unattended visual inputs. Here we propose a neurodynamical computational model of a part of the prefrontal cortex to account for the neural mechanisms defining this attentional filtering effect. Similar models have been employed to explain experimental results obtained during the performance of attention and working memory tasks. In this previous work the principle of biased competition was shown to successfully account for the experimental data. To model the attentional filtering effect, the biased competition model was extended to enable cooperation between stimulus selective neurons. We show that, in a biological relevant minimal model, competition and cooperation between the neurons are sufficient conditions for reproducing the attentional effect. Furthermore, a characterization of the parameter regime where the cooperation effect is observed is presented. Finally, we also reveal parameter regimes where the network has different modes of operation: selective working memory, attentional filtering, pure competition and noncompetitive amplification.

Animals↗

PubMed2004

Modular biased-competition and cooperation: a candidate mechanism for selective working memory.

Prefrontal cortex (PFC) has been suggested to play an important role in executive cognitive functions, participating in planning and controlling behaviour. The results of several recent electrophysiological studies indicate that PFC might be involved not only in the active maintenance of information but in doing so in a context- or task-dependent manner. In a delayed-match-to-sample paradigm, recordings from neurons in the PFC showed their ability to selectively represent information, which is needed for task completion, suggesting that task-irrelevant information does not access working memory. We present a neurodynamical computational model of a part of the PFC to account for the selective representation of information in working memory. We show that a network of biological realistic integrate-and-fire excitatory and inhibitory neurons, implementing the mechanisms of local or modular biased-competition, which is transmitted through cooperation to different subsets of neuronal pools, can explain the formation of selective context-dependent working memory. The modes of operation of the network are characterized and the corresponding parameter settings revealed. Modular competition and cooperation might constitute general mechanisms for implementing context-dependent formation of working memory.

Action Potentials↗

PubMed2002

Modeling the link between functional imaging and neuronal activity: synaptic metabolic demand and spike rates.

Functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) measurements reflect changes in the hemodynamics which are thought to be related to local synaptic input to neuron populations. The local neuronal spiking activity, which is believed to form the basis of neuronal coding and communication, is not directly reflected in fMRI/PET measurements. We used a mean-field neuronal model of recurrently coupled excitatory and inhibitory neuronal populations to characterize the relationship between the synaptic activity (reflected in the PET and fMRI measurements) and the neuronal spike rates, averaged over brain areas. We analyzed this relation for a number of cases. For a single brain area and in the absence of external input to its inhibitory neurons, the relation between average spike rates and synaptic activity is linear. However, departures from linearity are found when: (i) the local synaptic strengths vary, (ii) the external inputs vary, in the presence of external input to the inhibitory population, or (iii) the synchronization between oscillations of the average spike rates of two areas changes. We further show that an increase in the imaging signal can reflect a decrease in average spiking activity, in the presence of external input to the inhibitory population. Synaptic activity can also be associated with silent neuronal populations, when input to the excitatory population does not reach the activation threshold or for certain synchronizations between oscillations of two areas. In conclusion, caution should be used when interpreting neuroimaging results in terms of mean spike rates.

Algorithms↗