@article{TongEtAl2007, title = {{Learning grammatical structure with Echo State Networks}}, author = {M.H. Tong and A.D. Bickett and E.M. Christiansen and G.W. Cottrell}, journal = {Neural Networks}, number = 3, pages = {424--432}, publisher = {Elsevier}, volume = 20, year = 2007, abstract = {Echo State Networks (ESNs) have been shown to be effective for a number of tasks, including motor control, dynamic time series prediction, and memorizing musical sequences. However, their performance on natural language tasks has been largely unexplored until now. Simple Recurrent Networks (SRNs) have a long history in language modeling and show a striking similarity in architecture to ESNs. A comparison of SRNs and ESNs on a natural language task is therefore a natural choice for experimentation. Elman applies SRNs to a standard task in statistical NLP: predicting the next word in a corpus, given the previous words. Using a simple context-free grammar and an SRN with backpropagation through time (BPTT), Elman showed that the network was able to learn internal representations that were sensitive to linguistic processes that were useful for the prediction task. Here, using ESNs, we show that training such internal representations is unnecessary to achieve levels of performance comparable to SRNs. We also compare the processing capabilities of ESNs to bigrams and trigrams. Due to some unexpected regularities of Elman’s grammar, these statistical techniques are capable of maintaining dependencies over greater distances than might be initially expected. However, we show that the memory of ESNs in this word-prediction task, although noisy, extends significantly beyond that of bigrams and trigrams, enabling ESNs to make good predictions of verb agreement at distances over which these methods operate at chance. Overall, our results indicate a surprising ability of ESNs to learn a grammar, suggesting that they form useful internal representations without learning them.}, biburl = {http://www.bibsonomy.org/bibtex/23c4fa2d5ece32152adfe0a43c25fcd91/tmalsburg}, keywords = {echostatenetworks prediction recurrentneuralnetworks timeseries grammar} } @article{HintonSalakhutdinov2006b, title = {Reducing the dimensionality of data with neural networks}, author = {G E Hinton and R R Salakhutdinov}, journal = {Science}, month = {Jul}, number = 5786, pages = {504-507}, volume = 313, year = 2006, url = {http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&uid=16873662&cmd=showdetailview&indexed=google}, pmid = {16873662}, doi = {10.1126/science.1127647}, description = {Reducing the dimensionality of data with neural ne...[Science. 2006] - PubMed Result}, abstract = {High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such "autoencoder" networks, but this works well only if the initial weights are close to a good solution. We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.}, biburl = {http://www.bibsonomy.org/bibtex/2135bbce97b449ddf5fca7be88102b53c/tmalsburg}, keywords = {neuralnetworks parameterestimation dimensionalityreduction} } @article{JohnstonBarry2006, title = {{Age of acquisition and lexical processing}}, author = {R. Johnston and C. Barry}, journal = {Visual Cognition}, number = {7-8}, pages = {789--845}, publisher = {Psychology Press, part of the Taylor \& Francis Group}, volume = 13, year = 2006, biburl = {http://www.bibsonomy.org/bibtex/28a75579403958b0aec058b586b7c3333/tmalsburg}, keywords = {review mentallexicon reading agoofacquisition frequency} } @article{FiezEtAl1999, title = {Effects of lexicality, frequency, and spelling-to-sound consistency on the functional anatomy of reading}, author = {J A Fiez and D A Balota and M E Raichle and S E Petersen}, journal = {Neuron}, month = {Sep}, number = 1, pages = {205-218}, volume = 24, year = 1999, url = {http://www.ncbi.nlm.nih.gov/pubmed/10677038}, pmid = {10677038}, doi = {}, description = {Effects of lexicality, frequency, and spelling-to-...[Neuron. 1999] - PubMed Result}, abstract = {Functional neuroimaging was used to investigate three factors that affect reading performance: first, whether a stimulus is a word or pronounceable non-word (lexicality), second, how often a word is encountered (frequency), and third, whether the pronunciation has a predictable spelling-to-sound correspondence (consistency). Comparisons between word naming (reading) and visual fixation scans revealed stimulus-related activation differences in seven regions. A left frontal region showed effects of consistency and lexicality, indicating a role in orthographic to phonological transformation. Motor cortex showed an effect of consistency bilaterally, suggesting that motoric processes beyond high-level representations of word phonology influence reading performance. Implications for the integration of these results into theoretical models of word reading are discussed.}, biburl = {http://www.bibsonomy.org/bibtex/24a4c493325e9aa7ca5481688979e61ba/tmalsburg}, keywords = {mentallexicon wordfrequency imaging fmri} } @article{Vannest:2005:Cogn-Affect-Behav-Neurosci:15913009, title = {Dual-route processing of complex words: new fMRI evidence from derivational suffixation}, author = {J Vannest and T A Polk and R L Lewis}, journal = {Cogn Affect Behav Neurosci}, month = {Mar}, number = 1, pages = {67-76}, volume = 5, year = 2005, url = {http://www.ncbi.nlm.nih.gov/pubmed/15913009}, pmid = {15913009}, doi = {}, description = {Dual-route processing of complex words: new fMRI e...[Cogn Affect Behav Neurosci. 2005] - PubMed Result}, abstract = {Many behavioral models of the comprehension of suffixed words assume a dual-route mechanism in which these words are accessed sometimes from the mental lexicon as whole units and sometimes in terms of their component morphemes (such as happi+ness). In related neuropsychological work, Ullman et al. (1997) proposed a dual-route model for past tense processing, in which the lexicon (used for access to irregularly inflected forms) corresponds to declarative memory and a medial temporal/ parietal circuit, and the rule system (used for computation of regularly inflected forms) corresponds to procedural memory and a frontal (including Broca's area)/basal ganglia circuit. We used functional MRI and a memory encoding task to test this model for derivationally suffixed words, comparing those words that show evidence of decompositional processing in behavioral studies (-ness, -less, and -able words) with derived words that do not show decomposition effects (-ity and -ation words). By examining Broca's area and the basal ganglia as regions of interest, we found that "decomposable" derived and inflected words showed increases in activity relative to nondecomposable suffixed words. Results support a dual-route model of lexical access of complex words that is consistent with the Ullman et al. proposal.}, biburl = {http://www.bibsonomy.org/bibtex/27f1fb9abaec6cae0662e3f2a16ab60ca/tmalsburg}, keywords = {mentallexicon fmri dualroute brainimaging} } @article{SachEtAl2004, title = {Unified inflectional processing of regular and irregular verbs: a PET study}, author = {M Sach and R J Seitz and P Indefrey}, journal = {Neuroreport}, month = {Mar}, number = 3, pages = {533-537}, volume = 15, year = 2004, url = {http://www.ncbi.nlm.nih.gov/pubmed/15094518}, pmid = {15094518}, doi = {}, description = {Unified inflectional processing of regular and irr...[Neuroreport. 2004] - PubMed Result}, abstract = {Psycholinguistic theories propose different models of inflectional processing of regular and irregular verbs: dual mechanism models assume separate modules with lexical frequency sensitivity for irregular verbs. In contradistinction, connectionist models propose a unified process in a single module. We conducted a PET study using a 2 x 2 design with verb regularity and frequency. We found significantly shorter voice onset times for regular verbs and high frequency verbs irrespective of regularity. The PET data showed activations in inferior frontal gyrus (BA 45), nucleus lentiformis, thalamus, and superior medial cerebellum for both regular and irregular verbs but no dissociation for verb regularity. Our results support common processing components for regular and irregular verb inflection.}, biburl = {http://www.bibsonomy.org/bibtex/258e9e8fbdd524bd604f2135f9c30c884/tmalsburg}, keywords = {pet brainimaging singleroute dualroute mentallexicon} } @article{PerforsTenebaumRegierSubmitted, title = {Learnability of abstract syntactic principles}, author = {Amy Perfors and Josh Tenenbaum and Terry Regier}, year = {submitted}, abstract = {Children acquiring language infer the correct form of syntactic constructions for which they appear to have little or no direct evidence, avoiding simple but incorrect generalizations that would be consistent with the data they receive. These generalizations must be guided by some inductive bias – some abstract knowledge – that leads them to prefer the correct hypotheses even in the absence of directly supporting evidence. What form do these inductive constraints take? It is often argued or assumed that they reflect innately specified knowledge of language. A classic example of such an argument moves from the phenomenon of auxiliary fronting in English interrogatives to the conclusion that children must innately know that syntactic rules are defined over hierarchical phrase structures rather than linear sequences of words (e.g., Chomsky 1965, 1971, 1980; Crain & Nakayama, 1987). Here we use a Bayesian framework for grammar induction to argue for a different possibility. We show that, given typical child-directed speech and certain innate domain-general capacities, an unbiased ideal learner could recognize the hierarchical phrase structure of language without having this knowledge innately specified as part of the language faculty. We discuss the implications of this analysis for accounts of human language acquisition. }, biburl = {http://www.bibsonomy.org/bibtex/2348cf3a11bdfbfb4072064907d921c15/tmalsburg}, keywords = {modelcomparison languageacquisition bayesianinference} } @article{HagmannEtAl2008, title = {Mapping the Structural Core of Human Cerebral Cortex}, author = {Patric Hagmann and Leila Cammoun and Xavier Gigandet and Reto Meuli and Christopher J. Honey and Van J. Wedeen and Olaf Sporns}, journal = {PLoS Biology}, pages = {e159}, series = 7, volume = 6, year = 2008, url = {http://dx.doi.org/10.1371}, description = {architecture of the cortex}, abstract = {Structurally segregated and functionally specialized regions of the human cerebral cortex are interconnected by a dense network of cortico-cortical axonal pathways. By using diffusion spectrum imaging, we noninvasively mapped these pathways within and across cortical hemispheres in individual human participants. An analysis of the resulting large-scale structural brain networks reveals a structural core within posterior medial and parietal cerebral cortex, as well as several distinct temporal and frontal modules. Brain regions within the structural core share high degree, strength, and betweenness centrality, and they constitute connector hubs that link all major structural modules. The structural core contains brain regions that form the posterior components of the human default network. Looking both within and outside of core regions, we observed a substantial correspondence between structural connectivity and resting-state functional connectivity measured in the same participants. The spatial and topological centrality of the core within cortex suggests an important role in functional integration.}, biburl = {http://www.bibsonomy.org/bibtex/279fadca83d184107242bb8795ea2e4c3/tmalsburg}, keywords = {cortex braintopology connectivity} } @article{Elman1990, title = {{Finding structure in time}}, author = {J.L. Elman}, journal = {Cognitive Science}, number = 2, pages = {179--211}, publisher = {Elsevier}, volume = 14, year = 1990, abstract = {Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by [Jordan, 1986] which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves; the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands; indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context-dependent, while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction.}, biburl = {http://www.bibsonomy.org/bibtex/2c24e3fdc3f3f52aa9089a19fac845053/tmalsburg}, keywords = {time neuralnetworks recurrentneuralnetworks} } @article{HintonShallice1991, title = {Lesioning an attractor network: investigations of acquired dyslexia}, author = {G E Hinton and T Shallice}, journal = {Psychol Rev}, month = {Jan}, number = 1, pages = {74-95}, volume = 98, year = 1991, url = {http://www.ncbi.nlm.nih.gov/pubmed/2006233}, pmid = {2006233}, doi = {}, description = {Lesioning an attractor network: investigations of ...[Psychol Rev. 1991] - PubMed Result}, abstract = {A recurrent connectionist network was trained to output semantic feature vectors when presented with letter strings. When damaged, the network exhibited characteristics that resembled several of the phenomena found in deep dyslexia and semantic-access dyslexia. Damaged networks sometimes settled to the semantic vectors for semantically similar but visually dissimilar words. With severe damage, a forced-choice decision between categories was possible even when the choice of the particular semantic vector within the category was not possible. The damaged networks typically exhibited many mixed visual and semantic errors in which the output corresponded to a word that was both visually and semantically similar. Surprisingly, damage near the output sometimes caused pure visual errors. Indeed, the characteristic error pattern of deep dyslexia occurred with damage to virtually any part of the network.}, biburl = {http://www.bibsonomy.org/bibtex/243b41f2d2a6368fc8676e61814722ff7/tmalsburg}, keywords = {neuropsychology neuralnetworks lesion dyslexia} } @article{OlshausenField1996, title = {{Emergence of simple-cell receptive field properties by learning a sparse code for natural images}}, author = {B.A. Olshausen and D.J. Field}, journal = {Nature}, number = 6583, pages = {607--609}, volume = 381, year = 1996, abstract = {The receptive fields of simple cells in mammalian primary visual cortex can be characterized as being spatially localized, oriented and bandpass (selective to structure at different spatial scales), comparable to the basis functions of wavelet transforms. One approach to understanding such response properties of visual neurons has been to consider their relationship to the statistical structure of natural images in terms of efficient coding. Along these lines, a number of studies have attempted to train unsupervised learning algorithms on natural images in the hope of developing receptive fields with similar properties, but none has succeeded in producing a full set that spans the image space and contains all three of the above properties. Here we investigate the proposal that a coding strategy that maximizes sparseness is sufficient to account for these properties. We show that a learning algorithm that attempts to find sparse linear codes for natural scenes will develop a complete family of localized, oriented, bandpass receptive fields, similar to those found in the primary visual cortex. The resulting sparse image code provides a more efficient representation for later stages of processing because it possesses a higher degree of statistical independence among its outputs.}, biburl = {http://www.bibsonomy.org/bibtex/23843dd9c3578f649d69b34b07fd3a567/tmalsburg}, keywords = {featureextraction receptivefields} } @misc{Cottrell2006, title = {New life for neural networks}, author = {G W Cottrell}, journal = {Science}, month = {Jul}, number = 5786, pages = {454-455}, volume = 313, year = 2006, url = {http://www.ncbi.nlm.nih.gov/pubmed/16873635}, pmid = {16873635}, doi = {10.1126/science.1129813}, description = {Computer science. New life for neural networks. [Science. 2006] - PubMed Result}, abstract = {With the help of neural networks, data sets with many dimensions can be analyzed to find lower dimensional structure within them.}, biburl = {http://www.bibsonomy.org/bibtex/2337843cb36ee330a59d8f6a27cd7e6a8/tmalsburg}, keywords = {neuralnetworks autoencoders comment dimensionalityreduction} } @article{CrisantiAmitGutfreund1986, title = {{Saturation level of the Hopfield model for neural network}}, author = {A. Crisanti and D.J. Amit and H. Gutfreund}, journal = {Europhysics Letters}, number = 337, pages = {157--158}, volume = 2, year = 1986, biburl = {http://www.bibsonomy.org/bibtex/217bdbc6888361b0e444c8f32bdccd118/tmalsburg}, keywords = {neuralnetworks associativememory toread modeling} } @article{RumelhartHintonWIlliams1986, title = {{Learning representations by back-propagating errors}}, author = {D.E. Rumelhart and G.E. Hintont and R.J. Williams}, journal = {Nature}, number = 6088, pages = {533--536}, volume = 323, year = 1986, abstract = {We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal 'hidden' units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure.}, biburl = {http://www.bibsonomy.org/bibtex/248f905216b1ce71f8fda6c3150dc6a09/tmalsburg}, keywords = {algorithm modeling learning neuralnetworks} } @article{Hopfield1982, title = {Neural networks and physical systems with emergent collective computational abilities}, author = {John J Hopfield}, journal = {Proc Natl Acad Sci U S A}, month = {Apr}, number = 8, pages = {2554-2558}, volume = 79, year = 1982, url = {http://www.ncbi.nlm.nih.gov/pubmed/6953413}, pmid = {6953413}, doi = {}, description = {Neural networks and physical systems with emergent...[Proc Natl Acad Sci U S A. 1982] - PubMed Result}, abstract = {Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.}, biburl = {http://www.bibsonomy.org/bibtex/24cfdc33e478b63ad1c1bee1dd743263f/tmalsburg}, keywords = {memory neuralnetworks energyfunction} } @proceedings{BerkesWiskott2005, title = {On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields}, author = {Pietro Berkes and Laurenz Wiskott}, month = {February}, year = 2005, url = {http://cogprints.org/4081/}, abstract = {In this paper we introduce some mathematical and numerical tools to analyze and interpret inhomogeneous quadratic forms. The resulting characterization is in some aspects similar to that given by experimental studies of cortical cells, making it particularly suitable for application to second-order approximations and theoretical models of physiological receptive fields. We first discuss two ways of analyzing a quadratic form by visualizing the coefficients of its quadratic and linear term directly and by considering the eigenvectors of its quadratic term. We then present an algorithm to compute the optimal excitatory and inhibitory stimuli, i.e. the stimuli that maximize and minimize the considered quadratic form, respectively, given a fixed energy constraint. The analysis of the optimal stimuli is completed by considering their invariances, which are the transformations to which the quadratic form is most insensitive. We introduce a test to determine which of these are statistically significant. Next we propose a way to measure the relative contribution of the quadratic and linear term to the total output of the quadratic form. Furthermore, we derive simpler versions of the above techniques in the special case of a quadratic form without linear term and discuss the analysis of such functions in previous theoretical and experimental studies. In the final part of the paper we show that for each quadratic form it is possible to build an equivalent two-layer neural network, which is compatible with (but more general than) related networks used in some recent papers and with the energy model of complex cells. We show that the neural network is unique only up to an arbitrary orthogonal transformation of the excitatory and inhibitory subunits in the first layer. }, biburl = {http://www.bibsonomy.org/bibtex/2aa224ebbc857eaacdb9d7931bf07ad4a/tmalsburg}, keywords = {receptivefields nonlinearanalysis visualization quadraticforms} } @techreport{Berkes2005, title = {{Pattern Recognition with Slow Feature Analysis}}, author = {Pietro Berkes}, journal = {Cognitive Sciences EPrint Archive}, year = 2005, url = {http://cogprints.org/4104/}, abstract = {Slow feature analysis (SFA) is a new unsupervised algorithm to learn nonlinear functions that ex tract slowly varying signals out of the input data. In this paper we describe its application to pattern recognition. In this context in order to be slowly varying the functions learned by SFA need to respond similarly to the patterns belonging to the same class. We prove that, given input patterns belonging to C non-overlapping classes and a large enough function space, the optimal solution consists of C − 1 output signals that are constant for each individual class. As a consequence, their output provides a feature space suitable to perform classification with simple methods, such as Gaussian classifiers. We then show as an example the application of SFA to the MNIST handwritten digits database. The performance of SFA is comparable to that of other established algorithms. Finally, we suggest some possible extensions to the proposed method. Our approach is in particular attractive because for a given input signal and a fixed function space it has no parameters, it is easy to implement and apply, and it has low memory requirements and high speed during recognition. SFA finds the global solution (within the considered function space) in a single iteration without convergence issues. Moreover, the proposed method is completely problem-independent.}, biburl = {http://www.bibsonomy.org/bibtex/22979c91ccbf9d406db77d7e701ea6f94/tmalsburg}, keywords = {dimensionalityreduction tutorial emdeddings unsupervised patternrecognition sfa machinelearning} } @article{WiskottSejnowski2002, title = {Slow feature analysis: unsupervised learning of invariances}, author = {L Wiskott and T J Sejnowski}, journal = {Neural Comput}, month = {Apr}, number = 4, pages = {715-770}, volume = 14, year = 2002, url = {http://www.ncbi.nlm.nih.gov/pubmed/11936959}, pmid = {11936959}, doi = {10.1162/089976602317318938}, description = {Slow feature analysis: unsupervised learning of in...[Neural Comput. 2002] - PubMed Result}, abstract = {Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.}, biburl = {http://www.bibsonomy.org/bibtex/2d4a82f50398c9e1e041e443bed2c9a7c/tmalsburg}, keywords = {embeddings neuralnetworks learning transformationinvariance dimensionalityreduction} } @article{Segev1998, title = {Sound grounds for computing dendrites}, author = {Idan Segev}, journal = {Nature}, month = {May}, number = 6682, pages = {207-208}, volume = 393, year = 1998, url = {http://www.ncbi.nlm.nih.gov/pubmed/9607753}, pmid = {9607753}, doi = {10.1038/30340}, description = {Sound grounds for computing dendrites. [Nature. 1998] - PubMed Result}, abstract = {Dendrites are projections that typically originate from the cell body of neurons and are the main site for incoming synaptic inputs. Their function is largely unknown. But there is now clear-cut evidence that, in auditory brain stem, dendrites enrich the computational power of neurons.}, biburl = {http://www.bibsonomy.org/bibtex/29dd76b096b14f9e3b2bec97fafab4a64/tmalsburg}, keywords = {computation dendrites auditoryperception} } @article{HäusserMel2003, title = {Dendrites: bug or feature?}, author = {Michael Häusser and Barlett Mel}, journal = {Curr Opin Neurobiol}, month = {Jun}, number = 3, pages = {372-383}, volume = 13, year = 2003, url = {http://www.ncbi.nlm.nih.gov/pubmed/12850223}, pmid = {12850223}, doi = {}, description = {Dendrites: bug or feature? [Curr Opin Neurobiol. 2003] - PubMed Result}, abstract = {The integrative properties of dendrites are determined by a complex mixture of factors, including their morphology, the spatio-temporal patterning of synaptic inputs, the balance of excitation and inhibition, and neuromodulatory influences, all of which interact with the many voltage-gated conductances present in the dendritic membrane. Recent efforts to grapple with this complexity have focused on identifying functional compartments in the dendritic tree, the number and size of which depend on the aspect of dendritic function being considered. We discuss how dendritic compartments and the interactions between them help to enhance the computational power of the neuron and define the rules for the induction of synaptic plasticity.}, biburl = {http://www.bibsonomy.org/bibtex/27c3e5a4e85646c96718455a6bc9635ca/tmalsburg}, keywords = {synapses model computation neurons dendrites} }