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

Nativist theories have argued that language involves syntactic principles which are unlearnable from the input children receive. A paradigm case of these innate principles is the structure dependence of auxiliary inversion in complex polar questions (Chomsky, 1968, 1975, 1980). Computational approaches have focused on the properties of the input in explaining how children acquire these questions. In contrast, we argue that messages are structured in a way that supports structure dependence in syntax. We demonstrate this approach within a connectionist model of sentence production (Chang, 2009) which learned to generate a range of complex polar questions from a structured message without positive exemplars in the input. The model also generated different types of error in development that were similar in magnitude to those in children (e.g., auxiliary doubling, Ambridge, Rowland, & Pine, 2008; Crain & Nakayama, 1987). Through model comparisons we trace how meaning constraints and linguistic experience interact during the acquisition of auxiliary inversion. Our results suggest that auxiliary inversion rules in English can be acquired without innate syntactic principles, as long as it is assumed that speakers who ask complex questions express messages that are structured into multiple propositions

Abstract

Relative clause processing depends on the grammatical role of the head noun in the subordinate clause. This has traditionally been explained in terms of cognitive limitations. We suggest that structure-related processing differences arise from differences in experience with these structures. We present a connectionist model which learns to produce utterances with relative clauses from exposure to message-sentence pairs. The model shows how various factors such as frequent subsequences, structural variations, and meaning conspire to create differences in the processing of these structures. The predictions of this learning-based account have been confirmed in behavioral studies with adults. This work shows that structural regularities that govern relative clause processing can be explained within a usage-based approach to recursion.

Abstract

Language acquisition involves learning nonadjacent dependencies that can obtain between words in a sentence. Several artificial grammar learning studies have shown that the ability of adults and children to detect dependencies between A and B in frames AXB is influenced by the amount of variation in the X element. This paper presents a model of statistical learning which displays similar behavior on this task and generalizes in a human-like way. The model was also used to predict human behavior for increased distance and more variation in dependencies. We compare our model-based approach with the standard invariance account of the variability effect.