Publications

Abstract

Biologically inspired spiking networks are an important tool to study the nature of computation and cognition in neural systems. In this work, we investigate the representational capacity of spiking networks engaged in an identity mapping task. We compare two schemes for encoding symbolic input, one in which input is injected as a direct current and one where input is delivered as a spatio-temporal spike pattern. We test the ability of networks to discriminate their input as a function of the number of distinct input symbols. We also compare performance using either membrane potentials or filtered spike trains as state variable. Furthermore, we investigate how the circuit behavior depends on the balance between excitation and inhibition, and the degree of synchrony and regularity in its internal dynamics. Finally, we compare different linear methods of decoding population activity onto desired target labels. Overall, our results suggest that even this simple mapping task is strongly influenced by design choices on input encoding, state-variables, circuit characteristics and decoding methods, and these factors can interact in complex ways. This work highlights the importance of constraining computational network models of behavior by available neurobiological evidence.

Abstract

Sentence production is the process we use to create language-specific sentences that convey particular meanings. In production, there are complex interactions between meaning, words, and syntax at different points in sentences. Computational models can make these interactions explicit and connectionist learning algorithms have been useful for building such models. Connectionist models use domaingeneral mechanisms to learn internal representations and these mechanisms can also explain evidence of long-term syntactic adaptation in adult speakers. This paper will review work showing that these models can generalize words in novel ways and learn typologically-different languages like English and Japanese. It will also present modeling work which shows that connectionist learning algorithms can account for complex sentence production in children and adult production phenomena like structural priming, heavy NP shift, and conceptual/lexical accessibility.

Abstract

Relative clauses are a syntactic device to create complex sentences and they make language structurally productive. Despite a considerable number of experimental studies, it is still largely unclear how children learn relative clauses and how these are processed in the language system. Researchers at the MPI for Psycholinguistics used a computational learning model to gain novel insights into these issues. The model explains the differential development of relative clauses in English as well as cross-linguistic differences

Abstract

This paper investigates whether surprisal theory can account for differential processing difficulty in the NP-/S-coordination ambiguity in Dutch. Surprisal is estimated using a Probabilistic Context-Free Grammar (PCFG), which is induced from an automatically annotated corpus. We find that our lexicalized surprisal model can account for the reading time data from a classic experiment on this ambiguity by Frazier (1987). We argue that syntactic and lexical probabilities, as specified in a PCFG, are sufficient to account for what is commonly referred to as an NP-coordination preference.

Abstract

The problem of auxiliary fronting in complex polar questions occupies a prominent position within the nature versus nurture controversy in language acquisition. We employ a model of statistical learning which uses sequential and semantic information to produce utterances from a bag of words. This linear learner is capable of generating grammatical questions without exposure to these structures in its training environment. We also demonstrate that the model performs superior to n-gram learners on this task. Implications for nativist theories of language acquisition are discussed.