Exploration of Cortical Function
Imaging and Modeling Cortical Population Coding Strategies
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Article
Open AccessExtraction of semantic biomedical relations from text using conditional random fields
The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Nam...
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Chapter
Robust Learning of High-dimensional Biological Networks with Bayesian Networks
Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure lea...
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Chapter and Conference Paper
Structure Learning with Nonparametric Decomposable Models
We present a novel approach to structure learning for graphical models. By using nonparametric estimates to model clique densities in decomposable models, both discrete and continuous distributions can be hand...
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Chapter and Conference Paper
Bayesian Substructure Learning - Approximate Learning of Very Large Network Structures
In recent years, Bayesian networks became a popular framework to estimate the dependency structure of a set of variables. However, due to the NP-hardness of structure learning, this is a challenging task and t...
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Chapter
GeneSim™: Intelligent IT Platform for the Biomedical World
Today’s biomedical research and practice operate in a world where data and knowledge sources are ubiquitous, complex, and diverse. At the same time, we face the challenge to provide new, innovative, and target...
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Chapter
Biased Competition and Cooperation: A Mechanism of Mammalian Visual Recognition?
In humans and mammals with higher cognitive capabilities, the neocortex is a very prominent brain structure (Fig. 1). As such it seems to be crucially involved in the cognitive processes. The neocortex can be sub...
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Article
Learning to Attend: Modeling the Sha** 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 (Sigal...
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Chapter
Systems Level Modeling of Gene Regulatory Networks
The perhaps most important signaling network in living cells is constituted by the interactions of proteins with the genome—the gene regulatory network of the cell. From a system level point of view, the vario...
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Chapter
Computational Neuroscience for Cognitive Brain Functions
Cognitive behavior requires complex context-dependent processing of information that partially emerges from the links between attentional perceptual processes and working memory. We describe a computational ne...
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Book
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Chapter
Introduction
One of the central hypotheses in modern neuroscience is that all behavior is generated by the brain. It forms our perceptions from physical properties of our environment and controls our reactions and actions....
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Chapter
Regression Methods for Source Separation
In many statistical analysis tasks, we are confronted with data sets, which are arranged in pairs of data points (t m , x m ), m = 1, ... M. For example, x ...
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Chapter
The Early Visual System of Macaque Monkeys
The cerebral cortex has the shape of a 2–3 mm thick lamina. It consists mainly of cell bodies and local axonal connections, and shows a relatively uniform structure, as one proceeds laterally across its differ...
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Chapter
Optical Imaging As Source Separation Problem
If we want to reliably infer neuronal activity patterns from optical imaging data, we face two problems: (i) Most of the optical signals represent only an indirect measure of neuronal activity. This is particular...
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Chapter
Projection Methods for Source Separation
Projection methods represent a family of particular algorithms, which attempt to find and represent interesting statistical structure in the data. The data to be analyzed are a set of P data vectors x(r) = (x1, ....
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Chapter
Computational Models of Early Vision
In the previous chapters we have seen, that a combination of modern measurement techniques and analysis methods can provide knowledge about how both individual neurons and large neuron populations respond to e...
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Chapter
Neurons and Neuronal Signal Propagation
The human brain contains approximately 1012 to 1013 cells. About 1011 cells out of this pool can be classified as nerve cells or neurons. Although representing only 2–10 % of all brain cells, it is widely accepte...
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Chapter
Optical Imaging of Brain Activity
The last chapter has made clear, that die visual cortex is a highly structured network of many millions of neurons. In order to understand the function of this complex system, it is very important not only to ...
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Chapter
Mean-Field Modeling of Cortical Function
In chapter 3 we have provided a brief overview over die neuroanatomy of die macaque cortex. It has become clear that cortical tissue has a highly complex structure with many neuron types, each of which again s...
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Chapter
Real World Applications of Source Separation Techniques
During the last couple of years, optical imaging of neuronal population activity using Ca2+-sensitive dyes (Tsien, 1980; Poenie, 1992; Galizia et al., 1997) has become an important experimental tool for die inves...
