Janet Wiles

Potential role for adult neurogenesis in the encoding of time in new memories.

The dentate gyrus in the hippocampus is one of two brain regions with lifelong neurogenesis in mammals. Despite an increasing amount of information about the characteristics of the newborn granule cells, the specific contribution of their robust generation to memory formation by the hippocampus remains unclear. We describe here a possible role that this population of young granule cells may have in the formation of temporal associations in memory. Neurogenesis is a continuous process; the newborn population is only composed of the same cells for a short period of time. As time passes, the young neurons mature or die and others are born, gradually changing the identity of this young population. We discuss the possibility that one cognitive impact of this gradually changing population on hippocampal memory formation is the formation of the temporal clusters of long-term episodic memories seen in some human psychological studies.

Computational Influence of Adult Neurogenesis on Memory Encoding

Adult neurogenesis in the hippocampus leads to the incorporation of thousands of new granule cells into the dentate gyrus every month, but its function remains unclear. Here, we present computational evidence that indicates that adult neurogenesis may make three separate but related contributions to memory formation. First, immature neurons introduce a degree of similarity to memories learned at the same time, a process we refer to as pattern integration. Second, the extended maturation and change in excitability of these neurons make this added similarity a time-dependent effect, supporting the possibility that temporal information is included in new hippocampal memories. Finally, our model suggests that the experience-dependent addition of neurons results in a dentate gyrus network well suited for encoding new memories in familiar contexts while treating novel contexts differently. Taken together, these results indicate that new granule cells may affect hippocampal function in several unique and previously unpredicted ways.

Effective affective user interface design in games

It is proposed that games, which are designed to generate positive affect, are most successful when they facilitate flow (Csikszentmihalyi 1992). Flow is a state of concentration, deep enjoyment, and total absorption in an activity. The study of games, and a resulting understanding of flow in games can inform the design of non-leisure software for positive affect. The paper considers the ways in which computer games contravene Nielsens guidelines for heuristic evaluation (Nielsen and Molich 1990) and how these contraventions impact on flow. The paper also explores the implications for research that stem from the differences between games played on a personal computer and games played on a dedicated console. This research takes important initial steps towards defining how flow in computer games can inform affective design.

A Recurrent Neural Network that Learns to Count

Parallel distributed processing (PDP) architectures demonstrate a potentially radical alternative to the traditional theories of language processing that are based on serial computational models. However, learning complex structural relationships in temporal data presents a serious challenge to PDP systems. For example, automata theory dictates that processing strings from a context-free language (CFL) requires a stack or counter memory device. While some PDP models have been hand-crafted to emulate such a device, it is not clear how a neural network might develop such a device when learning a CFL. This research employs standard backpropagation training techniques for a recurrent neural network (RNN) in the task of learning to predict the next character in a simple deterministic CFL (DCFL). We show that an RNN can learn to recognize the structure of a simple DCFL. We use dynamical systems theory to identify how network states reflect that structure by building counters in phase space. The work is an empirical investigation which is complementary to theoretical analyses of network capabilities, yet original in its specific configuration of dynamics involved. The application of dynamical systems theory helps us relate the simulation results to theoretical results, and the learning task enables us to highlight some issues for understanding dynamical systems that process language with counters.

Toward a theory of human memory: Data structures and access processes

Starting from Marrs ideas about levels of explanation, a theory of the data structures and access processes in human memory is demonstrated on 10 tasks. Functional characteristics of human memory are captured implementation-independently. Our theory generates a multidimensional task classification subsuming existing classifications such as the distinction between tasks that are implicit versus explicit, data driven versus conceptually driven, and simple associative (two-way bindings) versus higher order (threeway bindings), providing a broad basis for new experiments. The formal language clarifies the binding problem in episodic memory, the role of input pathways in both episodic and semantic (lexical) memory, the importance of the input set in episodic memory, and the ubiquitous calculation of an intersection in theories of episodic and lexical access.

Computer games with intelligence

Artificial intelligence (AI) is playing an increasingly important role in the success or failure of computer games. The paper explores the use of AI in games focusing on difficulties faced by game developers and techniques that have proven useful. An argument is presented for the value of collaboration between the game and academic AI communities.

Making sense of big text: a visual-first approach for analysing text data using Leximancer and Discursis

This article reports on Leximancer and Discursis, two visual text analytic software tools developed at the University of Queensland. Both analyse spatial and temporal relationships in text data, but in complementary ways: Leximancer focuses on thematic analysis, while Discursis focuses on sequential analysis. Our report explains how they work, how to work with them and how visual concepts are relevant to all stages of their use in analytic decision-making.

A Gene Network Model for Developing Cell Lineages

Biological development is a remarkably complex process. A single cell, in an appropriate environment, contains sufficient information to generate a variety of differentiated cell types, whose spatial and temporal dynamics interact to form detailed morphological patterns. While several different physical and chemical processes play an important role in the development of an organism, the locus of control is the cells gene regulatory network. We designed a dynamic recurrent gene network (DRGN) model and evaluated its ability to control the developmental trajectories of cells during embryogenesis. Three tasks were developed to evaluate the model, inspired by cell lineage specification in C. elegans, describing the variation in gene activity required for early cell diversification, combinatorial control of cell lineages, and cell lineage termination. Three corresponding sets of simulations compared performance on the tasks for different gene network sizes, demonstrating the ability of DRGNs to perform the tasks with minimal external input. The model and task definition represent a new means of linking the fundamental properties of genetic networks with the topology of the cell lineages whose development they control.

Conceptual Recurrence Plots: Revealing Patterns in Human Discourse

Human discourse contains a rich mixture of conceptual information. Visualization of the global and local patterns within this data stream is a complex and challenging problem. Recurrence plots are an information visualization technique that can reveal trends and features in complex time series data. The recurrence plot technique works by measuring the similarity of points in a time series to all other points in the same time series and plotting the results in two dimensions. Previous studies have applied recurrence plotting techniques to textual data; however, these approaches plot recurrence using term-based similarity rather than conceptual similarity of the text. We introduce conceptual recurrence plots, which use a model of language to measure similarity between pairs of text utterances, and the similarity of all utterances is measured and displayed. In this paper, we explore how the descriptive power of the recurrence plotting technique can be used to discover patterns of interaction across a series of conversation transcripts. The results suggest that the conceptual recurrence plotting technique is a useful tool for exploring the structure of human discourse.

Solving Navigational Uncertainty Using Grid Cells on Robots

To successfully navigate their habitats, many mammals use a combination of two mechanisms, path integration and calibration using landmarks, which together enable them to estimate their location and orientation, or pose. In large natural environments, both these mechanisms are characterized by uncertainty: the path integration process is subject to the accumulation of error, while landmark calibration is limited by perceptual ambiguity. It remains unclear how animals form coherent spatial representations in the presence of such uncertainty. Navigation research using robots has determined that uncertainty can be effectively addressed by maintaining multiple probabilistic estimates of a robots pose. Here we show how conjunctive grid cells in dorsocaudal medial entorhinal cortex (dMEC) may maintain multiple estimates of pose using a brain-based robot navigation system known as RatSLAM. Based both on rodent spatially-responsive cells and functional engineering principles, the cells at the core of the RatSLAM computational model have similar characteristics to rodent grid cells, which we demonstrate by replicating the seminal Moser experiments. We apply the RatSLAM model to a new experimental paradigm designed to examine the responses of a robot or animal in the presence of perceptual ambiguity. Our computational approach enables us to observe short-term population coding of multiple location hypotheses, a phenomenon which would not be easily observable in rodent recordings. We present behavioral and neural evidence demonstrating that the conjunctive grid cells maintain and propagate multiple estimates of pose, enabling the correct pose estimate to be resolved over time even without uniquely identifying cues. While recent research has focused on the grid-like firing characteristics, accuracy and representational capacity of grid cells, our results identify a possible critical and unique role for conjunctive grid cells in filtering sensory uncertainty. We anticipate our study to be a starting point for animal experiments that test navigation in perceptually ambiguous environments.