Scratchpad Memory (SPM) is an alternative to cache. With SPM, the CPU¡¦s fast internal memory (ie, the SRAM) is directly mapped into the memory address space. This has the advantages of reduced power (by avoiding the memory management Unit, MMU, that a cache need to manage its tags and evictions), reduced area (for the same reason), and predictability.[1] Predictability is important in real-time systems, because each task must be assigned a deadline. If a task finishes early, there is no benefit. But if a task finishes late, then the effect is undesirable or even catastrophic. This means that the worst-case execution time (WCET) is more important than average-case execution time (ACET). The disadvantage of SPM, when compared to cache, is that the SPM requires software management of the fast memory. In a previous student¡¦s work from our laboratory, [3], an SPM allocator was presented for WCET-targeted compilation. Compared to that work, this current thesis make four key contributions. First, it introduces a significant amount of code infrastructure to allow library variables to be allocated to SPM. These variables turn out to represent a majority of all data accesses in many programs. Second, this provides support for allocating variables within recursive programs. Third, we support allocation of temporary variable (PC-relative addressing). Fourth, we have developed a simulator to obtain cycle-accurate information on memory behavior. In [3], the costs of allocation were not modeled, nor were the behaviors of the ARM¡¦s complex memory subsystem. Keywords: SPM, memory allocation, memory modeling, library variables, WCET

Gamma Hydroxybutyrate (GHB) was banned from the consumer market by the Food and Drug Administration in 1991. Despite the ban, use of GHB has continued to contribute to thousands of emergency department visits and numerous fatalities in recent years. Efforts to reduce the use of this drug have had limited impact, which may be the result of using traditional prevention strategies that focus exclusively on educating people about of negative consequences of substance use rather than addressing the factors that motivate use. In an effort to identify motivational factors that could be targeted in future prevention efforts, the present study was designed to examine outcome expectancies for GHB that may promote use of this drug. Methodology that has led to successful strategies to reduce alcohol use was applied to identify GHB expectancies and model cognitive processes likely to encourage or discourage GHB use. Individual differences scaling was used to empirically model a two dimensional semantic network of GHB expectancies stored in memory, and preference mapping was used to model likely paths of expectancy activation for male and female GHB users and nonusers. Differences in expectancies between GHB users and nonusers followed patterns previously identified in relation to alcohol expectancies and alcohol use. Conclusions were limited by relatively low numbers of GHB users in the sample, despite the use of a very large number of participants, overall. Despite this limitation these findings lay the groundwork for development and validation of GHB expectancy based prevention strategies.

Memory models typically assume that recall is a two-stage process with learning affecting both processes to the same degree. This equal learning assumption is difficult to reconcile with studies of the 'testing effect', which reveal different forgetting rates following learning from test practice versus learning from restudy. Here we present a new memory model, termed Primary and Convergent Retrieval (PCR) that assumes successful recall leads to a selective enhancement for the second stage of recall (Convergent Retrieval). We applied this model to existing testing effect data. In two new experiments, we confirmed novel predictions of the PCR model for transfer between retrieval cues and for recall latencies. This is the first formally specified model of the testing effect and it has broad implications for the nature of learning and retrieval.

Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing changes these networks stubbornly maintain their functions, which persist although destabilizing synaptic and nonsynaptic mechanisms should ostensibly propel them towards runaway excitation or quiescence. What dynamical phenomena exist to act together to balance such learning with information processing? What types of activity patterns do they underpin, and how do these patterns relate to our perceptual experiences? What enables learning and memory operations to occur despite such massive and constant neural reorganization? Progress towards answering many of these questions can be pursued through large-scale neuronal simulations.    In this thesis, a Hebbian learning rule for spiking neurons inspired by statistical inference is introduced. The spike-based version of the Bayesian Confidence Propagation Neural Network (BCPNN) learning rule involves changes in both synaptic strengths and intrinsic neuronal currents. The model is motivated by molecular cascades whose functional outcomes are mapped onto biological mechanisms such as Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability. Temporally interacting memory traces enable spike-timing dependence, a stable learning regime that remains competitive, postsynaptic activity regulation, spike-based reinforcement learning and intrinsic graded persistent firing levels.    The thesis seeks to demonstrate how multiple interacting plasticity mechanisms can coordinate reinforcement, auto- and hetero-associative learning within large-scale, spiking, plastic neuronal networks. Spiking neural networks can represent information in the form of probability distributions, and a biophysical realization of Bayesian computation can help reconcile disparate experimental observations. / <p>QC 20170421</p>