Holographic Reduced Representations for Oscillator Recall: A Model of Phonological Production

This paper describes a new computational model of phonological production, Holographic Reduced Representations for Oscillator Recall, or HORROR. HORROR's architecture accounts for phonological speech error patterns by combining the hierarchical oscillating context signal of the OSCAR serial-order model~\cite{VousdenEtAl:2000,BrownEtAl:2000} with a holographic associative memory~\cite{Plate:1995}. The resulting model is novel in a number of ways. Most importantly, all of the noise needed to generate errors is intrinsic to the system, instead of being generated by an external process. The model features fully-distributed hierarchical phoneme representations and a single distributed associative memory. Using fewer parameters and a more parsimonious design than OSCAR, HORROR accounts for error type proportions, the syllable-position constraint, and other constraints seen in the human speech error data

Self-Organizing Grammar Induction Using a Neural Network Model

This paper presents a self-organizing, real-time, hierarchical neural network model of sequential processing, and shows how it can be used to induce recognition codes corresponding to word categories and elementary grammatical structures. The model, first introduced in Mannes (1992), learns to recognize, store, and recall sequences of unitized patterns in a stable manner, either using short-term memory alone, or using long-term memory weights. Memory capacity is only limited by the number of nodes provided. Sequences are mapped to unitized patterns, making the model suitable for hierarchical operation. By using multiple modules arranged in a hierarchy and a simple mapping between output of lower levels and the input of higher levels, the induction of codes representing word category and simple phrase structures is an emergent property of the model. Simulation results are reported to illustrate this behavior.National Science Foundation (IRI-9024877

A Neural Network Model of Spatio-Temporal Pattern Recognition, Recall and Timing

This paper describes the design of a self~organizing, hierarchical neural network model of unsupervised serial learning. The model learns to recognize, store, and recall sequences of unitized patterns, using either short-term memory (STM) or both STM and long-term memory (LTM) mechanisms. Timing information is learned and recall {both from STM and from LTM) is performed with a learned rhythmical structure. The network, bearing similarities with ART (Carpenter & Grossberg 1987a), learns to map temporal sequences to unitized patterns, which makes it suitable for hierarchical operation. It is therefore capable of self-organizing codes for sequences of sequences. The capacity is only limited by the number of nodes provided. Selected simulation results are reported to illustrate system properties.National Science Foundation (IRI-9024877

Memory effect and phase transition in a hierarchical trap model for spin glass

We introduce an efficient dynamical tree method that enables us, for the first time, to explicitly demonstrate thermo-remanent magnetization memory effect in a hierarchical energy landscape. Our simulation nicely reproduces the nontrivial waiting-time and waiting-temperature dependences in this non-equilibrium phenomenon. We further investigate the condensation effect, in which a small set of micro-states dominates the thermodynamic behavior, in the multi-layer trap model. Importantly, a structural phase transition of the tree is shown to coincide with the onset of condensation phenomenon. Our results underscore the importance of hierarchical structure and demonstrate the intimate relation between glassy behavior and structure of barrier trees