The emerging function of verb inflections in German lenguage acquisition is modeled with a connectionist network. A network that is initially presented only with a semantic representation of sentences uses the inflectional verb ending -t to mark those sentences that are low in transitivity, whereas all other verb endings occur randomly. This behavior matches an early stage in German language acquisition where verb endings encode a similar semantic rather than a grammatical function. When information about the siuface structure of the sentence is added to the input data, the network learns to use the correct verb inflections in a process very similar to children's learning. This second phase is facilitated by the semeintic phase, suggesting that there is no shift from semantic to grammatical encoding, but rather an extension of the initial semantic encoding to include grammatical information. This can be seen as evidence for the strong version of the functionalist hypothesis of language acquisition.

Cells in the visual cortex are selective not only to ocular dominance and orientation of the input, but also to its size and spatial frequency. The simulations reported in this paper show how size selectivity could develop through Hebbian self-organization, and how receptive fields of different sizes could organize into columns like those for orientation and ocular dominance. The lateral connections in the network self-organize cooperatively and simultaneously with the receptive field sizes, and produce patterns of lateral connectivity that closely follow the receptive field organization. Together with our previous work on ocular dominance and orientation selectivity, these results suggest that a single Hebbian self-organizing process can give rise to all the major receptive field properties in the visual cortex, and also to structured patterns of lateral interactions, some of which have been verified experimentally and others predicted by the model.

Simple Recurrent Networks (SRNs) have been widely used in natural language processing tasks. However, their ability to handle long-term dependencies between sentence constituents is somewhat limited. NARX networks have recently been shown to outperform SRNs by preserving past information in explicit delays from the network's prior output. However, it is unclear how the number of delays should be determined. In this study on a shift-reduce parsing task, we demonstrate that comparable performance can be derived more elegantly by using a SARDNET self-organizing map. The resulting architecture can represent arbitrarily long sequences and is cognitively more plausible.

How do people understand concepts such as dog, aggressive dog, dog house or house dog? The meaning of a concept depends crucially on the concepts around it. While this hypothesis has existed for a long time, only recently it has become possible to test it based on neuroimaging and quantify it using computational modeling. In this paper, a neural network is trained with backpropagation to map attribute- based semantic representations to fMRI images of subjects reading everyday sentences. Backpropagation is then extended to the attributes, demonstrating how word meanings change in different contexts. Across a large corpus of sentences, the new attributes are more similar to the attributes of other words in the sentence than they are to the original attributes, demonstrating that the meaning of the context is transferred to a degree to each word in the sentence. Such dynamic conceptual combination effects could be included in natural language processing systems to encode rich contextual embeddings to mirror human performance more accurately.

A model for lexical disambiguation is presented that is bfised on combining the frequencies of past contexts of ambiguous words. The frequencies axe encoded in the word representations and define the words' semantics. A Simple Recurrent Network (SRN) parser combines the context frequencies one word at a time, edways producing the most likely interpretation of the current sentence at its output. This disambiguation process is most striking when the interpretation involves semantic flipping, that is, an cilternation between two opposing meanings as more words are read in. The sense of throsing a ball alternates between deince zind baseball as indicators such as the agent, location, and recipient are input. The SR N parser demonstrates how the context frequencies are dynamically combined to determine the interpretation of such sentences. We hypothesize that several other aspects of ambiguity resolution are based on similar mechanisms, and can be naturally approached from the distributed connectionist viewpoint.