Mission Statement.

Metaneva is constructed to maximize neural data from single unit recordings of rats and monkeys, to strengthen the analytical power of these data sets, to broaden their relevancy for related data sets throughout the field of cognitive sciences, to construct a dynamic computation of the brain and to move towards a translational neuroscience.

Evaluation of Future Experiments.

Our evaluative tool provides the cognitive sciences with a data storage where one can test a future experiment and match it with related experiments. This allows querying a certain type of paradigm as an explorative yet thorough scan of current research. An additional feature is the pre-analysis of the future experiment where one can evaluate the likelihood of brain activity and effect size. Finally the tool allows a relevancy report, providing a detailed report of brain activity given the paradigm as an indication what areas will be activated and what areas are poorly studied (yet showed some activity in previous research) using this particular paradigm.

Conceptual Analysis: Defining a Taxonomy.

A conceptual analysis allows to (re-)define the concepts used throughout the cognitive sciences and starts the development of a cognitive neural conceptual taxonomy. Linking neural systems is possible through the definition of a functional taxonomy.

Meta-Analysis.

A brain area meta-analysis formulates all relevant paradigms and functions for that particular brain area. This allows the study of overlapping neural mechanisms, detailed reports of the connection between functions or brain areas and finally allows the researcher to formulate suggestions on which paradigm needs to be studied. A functional meta-analysis is similar to the previous analysis, yet enables the formulation of the neural network for a particular function, connects brain areas to a single function and again allows suggestions for further research. Both use all relevant data and only at the very end of the analysis one uses semantic formulations to report the process. At all times one is able to track back the entire history record for the particular meta-analysis, allowing a re-analysis using identical criteria or to modify the analysis slightly in order to reproduce or test the reported meta-analysis.

Computational analysis: Dynamic Neural Networks.

The most challenging of all is the computational analysis, where one uses a meta-analysis linking it with detailed anatomical data. The advantage of such detailed data lies in the possibilities it creates for computing neural networks. Detailed data about the anatomical connections allows computing dynamic neural networks. Dynamic neural networks are embedded neural networks, where one links different networks with each other. We call them dynamic since they are not longer standalone neural network (e.g. the neural network of ‘pain’) but include all those mechanisms influencing this specific network (the network of pain linked with perception, action,…).