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

Lexicalized theories of syntax often assume that verb-structure regularities are mediated by lemmas, which abstract over variation in verb tense and aspect. German syntax seems to challenge this assumption, because verb position depends on tense and aspect. To examine how German speakers link these elements, a structural priming study was performed which varied syntactic structure, verb position (encoded by tense and aspect), and verb overlap. Abstract structural priming was found, both within and across verb position, but priming was larger when the verb position was the same between prime and target. Priming was boosted by verb overlap, but there was no interaction with verb position. The results can be explained by a lemma model where tense and aspect are linked to structural choices in German. Since the architecture of this lemma model is not consistent with results from English, a connectionist model was developed which could explain the cross-linguistic variation in the production system. Together, these findings support the view that language learning plays an important role in determining the nature of structural priming in different languages

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.