@inproceedings{Johnson2006,
        title = {A Dynamic Neural Field Theory of Multi-Item Visual Working Memory and Change Detection},
        address = {Vancouver, Canada},
        author = {J S Johnson and J P Spencer and G Schöner},
        booktitle = {Proceedings of the 28th Annual Conference of the Cognitive Science Society (CogSci 2006)},
        pages = {399-404},
        year = 2006,
        url = {ftp://ftp.neuroinformatik.rub.de/pub/manuscripts/articles/JohnsonSpencerSchoner2006.pdf},
        added = {2006-08-20 18:17:55 +0100},
	modified = {2006-08-20 18:23:04 +0100},
	abstract = {Many visually-guided behaviors rely critically on the ability to maintain visual information in working memory.  However, to date, there are few formal models of visual working memory (VWM) that directly interface with the growing empirical literature on this basic cognitive system.  In particular, no current theories address both the maintenance of multiple items in VWM and the process of change detection within a neurally-plausible framework.  In the present study, we describe such an approach, along with initial data from a change detection task that confirm a novel prediction of our model.},
	biburl = {http://www.bibsonomy.org/bibtex/23320d27f4cf30654a0bc87054b14399f/tmalsburg},
	keywords = {dynamicfieldtheory model changedetection interference workingmemory dynamicalsystems}
}

@article{SpencerSchöner2003,
        title = {Bridging the representational gap in the dynamic systems approach to development},
        author = {J.P. Spencer and G. Schöner},
        journal = {Developmental Science},
        number = 4,
        pages = {392--412},
        volume = 6,
        year = 2003,
        abstract = {We describe the relationship between the dynamic systems approach to development and a recent approach to the dynamics of
representational states – the dynamic field approach. Both approaches share an emphasis on the concepts of stability (attractor
states), instability (especially bifurcations), soft-assembly and flexibility. But the dynamic field approach adds the concept of
‘activation’ to capture the strength with which behaviorally relevant information is specified. By explicitly linking these dynamic
systems approaches, we allow for more direct comparisons between dynamic systems theory and connectionism. We note three
current differences between these two approaches to development: (1) the notion of stability is central to how representational
states are conceptualized in the dynamic field approach; (2) the dynamic field approach is more directly concerned with the
sensorimotor origins of cognition; and (3) the dynamic approach is less advanced with regard to learning. We conclude that
proponents of the two approaches can learn from the respective strengths of each approach. We suspect these differences will
largely disappear in the next 20 years.
},
	biburl = {http://www.bibsonomy.org/bibtex/2eee543604ba6298a620a5500a7ac202f/tmalsburg},
	keywords = {perception dynamicalsystems motorcontrol development connectionism}
}

@article{BeimGraben2001,
        title = {Estimating and improving the signal-to-noise ratio of time series by symbolic dynamics},
        author = {Peter Beim Graben},
        journal = {Phys Rev E Stat Nonlin Soft Matter Phys},
        month = {Nov},
        number = {5 Pt 1},
        pages = {051104-051104},
        volume = 64,
        year = 2001,
        url = {http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&uid=11735897&cmd=showdetailview&indexed=google},
        pmid = {11735897},
	doi = {},
	description = {Estimating and improving the signal-to-noise ratio...[Phys Rev E Stat Nonlin Soft Matter Phys. 2001] - PubMed Result},
	abstract = {We investigate the effect of symbolic encoding applied to times series consisting of some deterministic signal and additive noise, as well as time series given by a deterministic signal with randomly distributed initial conditions as a model of event-related brain potentials. We introduce an estimator of the signal-to-noise ratio (SNR) of the system by means of time averages of running complexity measures such as Shannon and R\{\'e\}nyi entropies, and prove its asymptotical equivalence with the linear SNR in the case of Shannon entropies of symbol distributions. A SNR improvement factor is defined, exhibiting a maximum for intermediate values of noise amplitude in analogy to stochastic resonance phenomena. We demonstrate that the maximum of the SNR improvement factor can be shifted toward smaller noise amplitudes by using higher order R\{\'e\}nyi entropies instead of the Shannon entropy. For a further improvement of the SNR, a half wave encoding of noisy time series is introduced. Finally, we discuss the effect of noisy phases on the linear SNR as well as on the SNR defined by symbolic dynamics. It is shown that longer symbol sequences yield an improvement of the latter.},
	biburl = {http://www.bibsonomy.org/bibtex/2b77fa7881d7b1d52f4dd0dd57692d66a/tmalsburg},
	keywords = {dynamicalsystems erp informationtheory eeg method}
}

@article{BeimGrabenEtAl2007,
        title = {Towards dynamical system models of language-related brain potentials},
        author = {Peter beim Graben and Sabrina Gerth and Shravan Vasishth},
        year = 2007,
        abstract = {    Event-related brain potentials (ERP) are important neural corre-
lates of cognitive processes. In the domain of language processing,
the N400 and P600 reflect lexical-semantic integration and syntactic
processing problems, respectively. We suggest an interpretation of
these markers in terms of dynamical system theory and present two
nonlinear dynamical models for syntactic computations where differ-
ent processing strategies correspond to functionally different regions
in the system's phase space.
},
	biburl = {http://www.bibsonomy.org/bibtex/26bbaadd7765cc6aa0fa07d6fcaf88858/tmalsburg},
	keywords = {modeling erp dynamicalsystems computationalpsycholinguistics languageprocessing}
}