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Neuroinformatics.Pdf 1 23 Your article is protected by copyright and all rights are held exclusively by Springer Science +Business Media New York. This e-offprint is for personal use only and shall not be self- archived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com”. 1 23 Author's personal copy Neuroinform DOI 10.1007/s12021-013-9210-5 ORIGINAL ARTICLE Action and Language Mechanisms in the Brain: Data, Models and Neuroinformatics Michael A. Arbib & James J. Bonaiuto & Ina Bornkessel-Schlesewsky & David Kemmerer & Brian MacWhinney & Finn Årup Nielsen & Erhan Oztop # Springer Science+Business Media New York 2013 Abstract We assess the challenges of studying action and Databasing empirical data . Federation of databases . language mechanisms in the brain, both singly and in relation Collaboratory workspaces to each other to provide a novel perspective on neuroinformatics, integrating the development of databases for encoding – separately or together – neurocomputational models and Bridging the Gap Between Models and Experiments empirical data that serve systems and cognitive neuroscience. The present article offers a perspective on the neuroinformatics Keywords Linking models and experiments . Models, challenges of linking neuroscience data with models of the neurocomputational . Action and the brain . Language and the neural and other interactions that yield those data. We will brain . Mirrorsystems . Multi-level data . Multi-level models . focus on cognitive and systems neuroscience. The perspective is particularly informed by consideration of the brain M. A. Arbib (*) mechanisms underlying action and language and emerged Computer Science and Neuroscience Graduate Program, University from discussions held as part of the Workshop on “Action, of Southern California, Los Angeles, CA, USA Language and Neuroinformatics” held in July of 2011 (see the e-mail: [email protected] section “Acknowledgement, Discussion Groups and Workshop J. J. Bonaiuto Participants” for further details). The Appendix provides a Division of Biology, California Institute of Technology, Pasadena, Tabulation of Present and Future Resources relevant to the CA, USA issues discussed in this article. e-mail: [email protected] I. Bornkessel-Schlesewsky What is a Model? Neurolinguistics, University of Marburg, Marburg, Germany e-mail: [email protected] For one researcher, a model may be a technique for marshaling D. Kemmerer diverse data into a coherent format, whereas for others a model Speech, Language, & Hearing Sciences and Psychological Sciences, (conceptual or computational) provides an account of how Purdue University, West Lafayette, IN, USA interactions of entities within the brain mediate between inputs, e-mail: [email protected] internal states and outputs. We refer to the former as “data B. MacWhinney models” and the latter as “processing models.” Clearly, the Psychology, Computational Linguistics, and Modern Languages, result of data modeling is crucial to any specification of what it Carnegie Mellon University, Pittsburgh, PA, USA is that a processing model has to explain. If the word “model” e-mail: [email protected] is used in what follows without a qualifying adjective or F. Å. Nielsen context, it will be in the sense of a “processing model”. Technical University of Denmark, Copenhagen, Denmark There is a third type of model – not a model of data, not a e-mail: [email protected] model of the brain, but a model in the brain. This idea goes back, at least, to Craik (1943). It relates both to the general E. Oztop Ozyegin University, Istanbul, Turkey notion of perceptual schemas and motor schemas (Arbib e-mail: [email protected] 1981), the control theory concepts of feedback and Author's personal copy Neuroinform feedforward, and the notion of forward and inverse models of this general framework. BODB requires that SEDs associated a system (Wolpert and Kawato 1998). Thus, in modeling with a model be divided (at least) into those which are used to neural mechanisms for action and action recognition, design the model, and those used to test the model. For processing models of how the brain employs forward and computational models, the latter SEDs are compared with inverse models may play a crucial role (Oztop et al. 2013; summaries of simulation results (SSRs) obtained using the Oztop et al. 2005). model. If there are summary data, then somewhere there are Fostering Modeler-Experimentalist Collaboration unsummarized data. Often such data are only available, if at all, in Supplementary Material for a published paper, or on a Even among experimentalists who have rich interactions with laboratory Website. This raises two challenges: The modelers, few make explicit what challenges – whether at the development of further Websites for the integration of level of explaining specific data or in search of a conceptual unsummarized data and the linkage of summarized data framework – they want modelers to address, and few will (perhaps in another Website) to the data they summarize. For adjust their agenda to test novel predictions of models. Thus a example, the huge volumes of data associated with each challenge of particular interest here is to chart how individual fMRI scan in one comparison for a specific neuroinformatics could provide means to deepen these experimental-control condition comparison may be interactions. Among the relevant issues are understanding summarized into brain imaging tables which might aggregate how to summarize data sets into a form for which modeling multiple scans for that comparison into a single table, losing is appropriate, and appreciating the value of models which do detail but, hopefully, gaining conceptual clarity in the process. not fit data but do provide fresh insights – this in addition to BrainMap (Laird et al. 2005), brainmap.org, provides the the more obvious appreciation of those which do so – while classic repository for such brain imaging data, and Laird avoiding something like the “epicycles” used to adjust the et al. (2009) discuss the potential analyses that are possible orbits of Ptolemaic astronomy, i.e., without introducing ad hoc using the BrainMap database and coordinate-based ALE mechanisms whose only raison d’etre is to explain a very (activation likelihood estimation) meta-analyses, along with limited data set. some examples of how these tools can be applied to create a Arbib and colleagues (Arbib et al. 2014b)argueforindexing probabilistic atlas and an ontological system describing models not only with respect to brain structures (e.g., a model of function-structure correspondences. Nielsen (2013) focuses circuits in basal ganglia and prefrontal cortex) but also with on neuroinformatics tools of the BrainMap-based Brede Wiki, respect to brain operating principles (BOPs) which provide http://neuro.imm.dtu.dk/wiki/, which support federation and general mechanisms (such as reinforcement learning, winner- interaction of brain imaging data. (See the section on take-all, feedforward-feedback coupling, etc.) which may be “Federation” below for more on the general issues concerning employed in analyzing the roles of very different brain regions federation.). in diverse behaviors. Moreover, they argue that each model A range of brain models has been documented in the should be associated with summaries of empirical data (SEDs) ModelDB database, http://senselab.med.yale.edu/modeldb/. defined at the granularity of the model. There are at least two Model DB and BODB seem good targets for federation. The problems here. (a) Even if experimentalists make clear the exact former provides documented code for each model, with methodology used to extract data and process it – as in the instructions on how to run it to get published results; the framework offered by Lohrey et al. (2009)tointegrateanobject latter links each model to the summaries of experimental model, research methods (workflows), the capture of data used to design and test it, and provides tools to retrieve experimental data sets and the provenance of those data sets and compare models which address related sets of data. While for fMRI – the problem remains of integrating data gathered BODB tends to focus on systems and cognitive neuroscience with different protocols into a meaningful challenge for models “up a level” from many of the models in ModelDB, modeling. (b) If model A explains view A of data set D, while Bohland et al. (2013) have focused on the challenges of model B explains view B of D; and yet the models are different, linking data at multiple levels from genes and gene expression how may one build on them to more fully address aspects of D that guide brain developmentandprovidethemolecular revealed in the combination of the 2 views? For example, one signatures of populations of neurons to the architecture of model might be successful if it can explain the averaged functional brain subsystems. How can modelers begin to build responses of a brain region to a key set of stimuli rather than systems that span the layers between information
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