Inhibition of return (IOR) is a cognitive phenomenon whereby reaction times (RTs) are slower to cued relative to uncued targets at cue-target onset asynchronies (CTOAs) greater than approximately 300 ms. One important theory of IOR proposes that there are two mutually exclusive forms of IOR, with an attentional/perceptual form arising when the oculomotor system is actively suppressed, and a motoric form arising when it is engaged (Taylor & Klein, 2000). Other theories propose that IOR is the result of multiple, additive neural mechanisms (Abrams & Dobkin, 1994). Here, we have performed computational simulations and empirical investigations in an attempt to reconcile these two competing theories. Using a dynamic neural field (DNF) model of the intermediate layers of the superior colliculus (iSC), we have modeled both a sensory adaptation mechanism of IOR, and a motoric mechanism resulting from the aftereffects of saccadic eye movements. Simulating these mechanisms, we replicated behavior and neurophysiology in a number of variations on the traditional cue-target paradigm (Posner, 1980). Predictions driven by these simulations have led to the proposal of many behavioral and neuroimaging experiments which further examine the plausibility of a 2-mechanisms theory of IOR. Contrary to our original predictions, we demonstrated that saccades are biased away from cued targets in a paired target saccade averaging paradigm, even at short CTOAs. In paradigms thought to recruit both sensory and motoric mechanisms, we robustly demonstrated that there are at least two independent, additive mechanisms of IOR when tasks require saccadic responses to targets. When similar paradigms were tested with manual responses to targets, additivity effects did not hold, implying that the motoric mechanism of IOR does not transfer from the oculomotor to skeletomotor systems. Furthermore, across numerous experiments using event-related potential (ERP) techniques, we have demonstrated that P1 component reductions are neither necessary, nor sufficient, for the behavioral exhibition of IOR. We propose that a comprehensive framework for behavioral IOR must include (at least) four independent neural mechanisms, differentially active depending on circumstances, including sensory adaptation, saccadic aftereffects, local inhibition, and cortical habituation.

To survive, many biological organisms need to accurately infer which features of their environment predict future rewards and punishments. In machine learning terms, this is the problem of spatial credit assignment, for which many supervised learning algorithms have been developed. In this thesis, I mainly propose that a dual-pathway, regression-like strategy and associated biological implementations may be used to solve this problem. Using David Marr's (1982) three-level philosophy of computational neuroscience, the thesis and its contributions are organized as follows: - Computational Level: Here, the spatial credit assignment problem is formally defined and modeled using probability density functions. The specific challenges of the problem faced by organisms and machine learning algorithms alike are also identified. - Algorithmic Level: I present and evaluate the novel hypothesis that the general strategy used by animals is to perform a regression over past experiences. I also introduce an extension of a probabilistic model for regression that substantially improves generalization without resorting to regularization. This approach subdues residual associations to irrelevant features, as does regularization. - Physical Level: Here, the neuroscience of classical conditioning and of the basal ganglia is briefly reviewed. Then, two novel models of the basal ganglia are put forward: 1) an online-learning model that supports the regression hypothesis and 2) a biological implementation of the probabilistic model previously introduced. Finally, we compare these models to others in the literature. In short, this thesis establishes a theoretical framework for studying the spatial credit assignment problem, offers a simple hypothesis for how biological systems solve it, and implements basal ganglia-based algorithms in support. The thesis brings to light novel approaches for machine learning and several explanations for biological structures and classical conditioning phenomena. / Note: While the thesis contains content from two articles (one journal, one conference), their publishers do not require special permission for their use in dissertations (information confirming this is in an appendix of the thesis itself).

Whether we want to drive a car, play a ball game, or even enjoy watching a flying bird, we need to track moving objects. This is possible via smooth pursuit eye movements (SPEMs), which maintain the image of the moving object on the fovea (i.e., a very small portion of the retina with high visual resolution). At first glance, performing an accurate SPEM by the brain may seem trivial. However, imperfect visual coding, processing and transmission delays, wide variety of object sizes, and background textures make the task challenging. Furthermore, the existence of distractors in the environment makes it even more complicated and it is no wonder why understanding SPEM has been a classic question of human motor control. To understand physiological systems of which SPEM is an example, creation of models has played an influential role. Models make quantitative predictions that can be tested in experiments. Therefore, modelling SPEM is not only valuable to learn neurobiological mechanisms of smooth pursuit or more generally gaze control but also beneficial to give insight into other sensory-motor functions. In this thesis, I present a neurocomputational SPEM model based on Neural Engineering Framework (NEF) to drive an eye-like robot. The model interacts with the real world in real time. It uses naturalistic images as input and by the use of spiking model neurons controls the robot. This work can be the first step towards more thorough validation of abstract SPEM control models. Besides, it is a small step toward neural models that drive robots to accomplish more intricate sensory-motor tasks such as reaching and grasping.

Thesis (Ph. D.)--Mechanical Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Steve M. Potter; Committee Member: Eric Schumacher; Committee Member: Robert J. Butera; Committee Member: Stephan P. DeWeerth; Committee Member: Thomas D. DeMarse.

Thesis (Ph. D.)--University of California, San Diego, 2009. / Title from first page of PDF file (viewed April 1, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 150-163).

Thesis (Ph.D.)--University of Alberta, 2009. / A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy, Centre for Neuroscience. Title from pdf file main screen (viewed on October 31, 2009). Includes bibliographical references.

Thesis (Ph. D.)--University of California, San Diego, 2008. / Title from first page of PDF file (viewed June 1, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 124-131).

Thesis (Ph. D.) University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 2, 2007) Includes bibliographical references.

New memories are labile, but over time some of them are stabilized. This thesis investigates the network mechanisms in the brain underlying the gradual consolidation of memory representations. Specifically, I performed a causal test of the long-standing hypothesis that the offline reactivation of new, memory-representing cell assemblies supports memory consolidation by stabilizing those assemblies and increasing the likelihood of their later reinstatement - and therefore presumably of memory recall. I performed multi-unit extracellular recordings in the dorsal CA1 region of behaving mice, from which I detected short-timescale (25 ms) co-activation patterns of principal neurons during exploration of open-field enclosures. These cell assembly patterns appeared to represent space as their expression was spatially tuned and environment specific; and these patterns were preferentially reactivated during sharp wave-ripples (SWRs) in subsequent sleep. Importantly, after exposure to a novel - but not a familiar - enclosure, the strength with which an assembly pattern was reactivated predicted its later reinstatement strength during context re-exposure. Moreover, optogenetic silencing of hippocampal pyramidal neurons during on-the-fly detected SWRs during the sleep following exposure to a novel - but again not a familiar - enclosure impaired subsequent assembly pattern reinstatement. These results are direct evidence for a causal role of SWR-associated reactivation in the stability of new hippocampal cell assemblies. Surprisingly, offline reactivation was only important for the stability of a subset of the assembly patterns expressed in a novel enclosure. Optogenetic SWR silencing only impaired the reinstatement of "gradually strengthened" patterns that had had a significant increasing trend in their expression strength throughout the initial exposure session. Consistent with this result, a positive correlation between reactivation and subsequent reinstatement was only found for these gradually strengthened patterns and not for the other, "early stabilized" patterns. An interesting interpretation is that the properties of the gradually strengthened patterns are all consistent with the Hebbian postulate of "fire together, wire together". To enable investigation of the relation between interneurons and principal cell assembly patterns from extracellular recordings, as a final contribution this thesis describes a statistical framework for the unsupervised classification of interneurons based on their firing properties alone.

Adaptation is a fundamental property of the nervous system that underlies the maintenance of successful actions through flexible reconfiguration of sensorimotor processing. The primary aims of this thesis are 1) to investigate the computational and neural underpinnings of sensorimotor memory formation during prism adaptation (PA) in humans, and 2) how they interact with anodal transcranial direct current stimulation (a-tDCS) of the primary motor cortex (M1), in order to 3) improve efficacy of prism therapy for post-stroke spatial neglect. In chapter 4, we modify an influential state-space model of adaptation in order to characterize the contribution of short and long memory timescales to motor behaviour as sensorimotor after-effects (AEs) develop during PA. This enables us, in the multimodal 7 Tesla MRI experiment reported in chapter 5, to demonstrate that the level of M1 excitation:inhibition causally sets the relative contribution of long versus short memory timescales during PA, thus determining behavioural persistence of the AE at retention in young healthy adults. This finding offers a bridge between different levels of investigation by providing a biologically plausible neuro-computational model of how sensorimotor memories are formed and enhanced by a-tDCS. In chapter 6, we use the ageing motor system as a model of reduced GABAergic inhibition and show that the age-related decrease in M1 GABA explains why older adults demonstrate more persistent prism AEs. Taken together, these data indicate that the reduction in M1 GABAergic inhibition via excitatory a-tDCS during PA has the potential to enhance persistence of adaptation memory in both young and older adults. Informed by these results, we subsequently ask whether standard (multi-session) PA therapy combined with left M1 a-tDCS translates to greater and/or longer-lasting clinical improvements in post-stroke spatial neglect patients. In chapter 7, we compare the multimodal neuroimaging data of six neglect patients to normative data of age-matched controls. We show that in all patients, the lesion interrupted long-range frontoparietal connections, and we provide direct evidence for a pathological left dominance of activity within the lateral occipital cortex during deployment of bilateral visuospatial attention. In chapter 8, we present the behavioural performance of these patients throughout the two phases of the clinical study (i.e. before and after either PA + real M1 a-tDCS or PA + sham M1 atDCS). There was no clear effect of a-tDCS on the therapeutic effect of PA in these patients. The results of the studies presented in this thesis provide a novel insight into the neurocomputational mechanisms of sensorimotor memory formation and its modulation by a-tDCS in the healthy brain. Further investigation of how these mechanisms relate to therapeutic improvements following PA in certain neglect patients is needed.