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PubMed1999

Anti-DNA autoantibodies and systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a systemic autoimmune disease that affects most of the organs and tissues of the body, causing glomerulonephritis, arthritis, and cerebritis. SLE can be fatal with nephritis, in particular, predicting a poor outcome for patients. In this review, we highlight what has been learned about SLE from the study of mouse models, and pay particular attention to anti-DNA autoantibodies, both as pathological agents of lupus nephritis and as DNA-binding proteins. We summarize the current approaches used to treat SLE and discuss the targeting of anti-DNA autoantibodies as a new treatment for lupus nephritis.

Animals↗

PubMed2000

Representing the task in Bayesian reasoning: comment on Lovett and Schunn (1999).

The RCCL model (M. C. Lovett & C. D. Schunn, 1999) produces predictions that are non-novel or that do not truly spring from its principles. However, it offers the valuable insight that learning processes may affect the selection of both representations and strategies within those representations, and points the way to possible theoretical progress on implicit and explicit control. The authors' account of base-rate neglect under direct experience is compared with RCCL, and it is concluded that learning-based models allow for tests that are not fostered by representation-based models.

Bayes Theorem↗

PubMed2006

Development of a physiologically based pharmacokinetic model for deltamethrin in the adult male Sprague-Dawley rat.

Deltamethrin (DLT) is a type II pyrethroid insecticide widely used in agriculture and public health. DLT is a potent neurotoxin that is primarily cleared from the body by metabolism. To better understand the dosimetry of DLT in the central nervous system, a physiologically based pharmacokinetic (PBPK) model for DLT was constructed for the adult, male Sprague-Dawley rat that employed both flow-limited (brain, gastrointestinal [GI] tract, liver, and rapidly perfused tissues) and diffusion-limited (fat, blood/plasma, and slowly perfused tissues) rate equations. The blood was divided into plasma and erythrocytes. Cytochrome P450-mediated metabolism was accounted for in the liver and carboxylesterase (CaE)-mediated metabolism in plasma and liver. Serial blood, brain, and fat samples were taken for DLT analysis for up to 48 h after adult rats received 2 or 10 mg DLT/kg po. Hepatic biotransformation accounted for approximately 78% of these administered doses. Plasma CaEs accounted for biotransformation of approximately 8% of each dosage. Refined PBPK model forecasts compared favorably to the 2- and 10-mg/kg po blood, plasma, brain, and fat DLT profiles, as well as profiles subsequently obtained from adult rats given 1 mg/kg iv. DLT kinetic profiles extracted from published reports of oral and iv experiments were also used for verification of the model's simulations. There was generally good agreement in most instances between predicted and the limited amount of empirical data. It became clear from our modeling efforts that there is considerably more to be learned about processes that govern GI absorption and exsorption, transport, binding, brain uptake and egress, fat deposition, and systemic elimination of DLT and other pyrethroids. The current model can serve as a foundation for construction of models for other pyrethroids and can be improved as more definitive information on DLT kinetic processes becomes available.

Adipose Tissue↗

PubMed1996

Preclinical pharmacology of milnacipran.

Milnacipran (Ixel) is a new antidepressant which has been developed for its selective inhibition of both serotonin and noradrenaline reuptake and its lack of affinity for neurotransmitter receptors. It inhibits virtually equipotently the reuptake of serotonin and noradrenaline both in vitro and in vivo, as demonstrated by the antagonism of centrally acting monoamine displacers. It has no effect on dopamine reuptake. In addition, milnacipran has been shown by intracerebral microdialysis to increase the extracellular levels of both serotonin and noradrenaline after acute administration. Milnacipran is devoid of interactions at any known neurotransmitter receptor or ion channel. In particular, and unlike tricyclic antidepressants, it does not act at noradrenergic, muscarinic or histaminergic receptors. Contrary to tricyclic antidepressants, chronic administration of milnacipran does not modify beta-adrenoceptor binding or second messenger function. Milnacipran is active on various animal models of depression such as the forced swimming test in the mouse, learned helplessness in the rat and the olfactory bulbectomized rat model. This pharmacological profile, associated with an excellent bioavailability in man, was predicted to be that required for a powerful and well-tolerated antidepressant. Subsequent clinical development has shown this prediction to be well founded.

Adrenergic Uptake Inhibitors↗

PubMedDate not supplied

Conditioned response timing and integration in the cerebellum.

Classical conditioning procedures instill knowledge about the temporal relationships between events. The unconditioned stimulus (US) is the event to be timed. The conditioned response (CR) is viewed as a prediction of the imminence of the US. Knowledge of the elapsed time between conditioned stimuli (CSs) and US delivery is expressed in the topological features of the CR. The peak amplitude of the CR coincides with the timing of the US. A simple connectionist network based on Sutton and Barto's Time Derivative (TD) Model of Pavlovian Reinforcement provides a mechanism that can account for and simulate CR timing in a variety of protocols. This article describes extensions of the model to predictive timing under temporal uncertainty. The model is expressed in terms of equations that operate in real time according to a competitive learning rule. The unfolding of time from the onsets and offsets of events such as CSs is represented by the propagation of activity along a sequence of time-tagged elements. The model can be aligned with anatomical circuits of the cerebellum and brain stem that are essential for learning and performance of conditioned eye-blink responses.

Animals↗

PubMed1979

Validation of a subjective helplessness measure.

Investigated the potential utility and validity of a measure of subjective helplessness, the H25. Helplessness is defined as the degree to which the individual perceives him/herself to be unable to influence or control the initiation and outcome of a variety of potentially reinforcing activities. Alcoholic subjects were classified into three levels of self-reported helplessness. An initial multivariate analysis of variance indicated that the groups differed with respect to severity level across a number of dimensions of depressive symptomatology, with the High Helplessness group appearing significantly more depressed on each of the measures than Low Helplessness subjects. Subsequent analyses supported the construct validity of the H25. Those measures found to be most descriminating between groups and most predictive of the level of helplessness reflected a dimension of behavioral retardation consistent with the motivational dificits noted in the learned helplessness model. Recommendations for the future validation of individual difference measures of helplessness are discussed.

Adult↗

PubMed2003

Wiring the olfactory bulb--activity-dependent models of axonal targeting in the developing olfactory pathway.

Two recent experimental studies /20,21/ revealed that odorant-evoked activity-dependent competition is significant in the organisation and maintenance of the olfactory system. In this paper, we investigate the generation of a chemotopic sensory map in the olfactory bulb through three models driven by high-density optical chemosensor arrays which have similar properties to olfactory receptor neurons. By exposing the sensor arrays to various odours, these models were subjected to Hebbian learning to achieve self-organisation, potentially explaining the activity-dependent competition demonstrated by these recent studies. Our final model also predicts a role for periglomerular cells in the formation of the chemotopic sensory map in the olfactory bulb.

Animals↗

PubMed2004

A self-learning coupled map lattice for vortex shedding in cable and cylinder wakes.

A coupled map lattice (CML) with self-learning features is developed to model flow over freely vibrating cables and stationary cylinders at low Reynolds numbers. Coupled map lattices that combine a series of low-dimensional circle maps with a diffusion model have been used previously to predict qualitative features of these flows. However, the simple nature of these CML models implies that there will be unmodeled wake features if a detailed, quantitative comparison is made with laboratory or simulated wake flows. Motivated by a desire to develop an improved CML model, we incorporate self-learning features into a new CML that is first trained to precisely estimate wake patterns from a target numerical simulation. A new convective-diffusive map that includes additional wake dynamics is developed. The new self-learning CML uses an adaptive estimation scheme (multivariable least-squares algorithm). Studies of this approach are conducted using wake patterns from a Navier-Stokes solution (spectral element-based NEKTAR simulation) of freely vibrating cable wakes at Reynolds numbers Re=100. It is shown that the self-learning model accurately and efficiently estimates the simulated wake patterns. The self-learning scheme is then successfully applied to vortex shedding patterns obtained from experiments on stationary cylinders. This constitutes a first step toward the use of the self-learning CML as a wake model in flow control studies of laboratory wake flows.

Journal Article↗

PubMedDate not supplied

Essential latent knowledge for protein-protein interactions: analysis by an unsupervised learning approach.

Protein-protein interactions play a number of central roles in many cellular functions, including DNA replication, transcription and translation, signal transduction, and metabolic pathways. A recent increase in the number of protein-protein interactions has made predicting unknown protein-protein interactions important for the understanding of living cells. However, the protein-protein interactions experimentally obtained so far are often incomplete and contradictory and, consequently, existing computational prediction methods have integrated evidence (latent knowledge of proteins) from different and more reliable sources. Analyzing the relationships between proteins and the latent knowledge is important to understanding the cellular processes. For this analysis, we propose a new probabilistic model for protein-protein interactions by considering the latent knowledge of proteins. We further present an efficient learning algorithm for this model, based on an EM algorithm. Experimental results have shown that in a supervised test setting, the proposed method outperformed five other competing methods by a statistically significant factor in all cases. Using the probability parameters of a trained model, we have further shown the latent knowledge that is essential to predicting protein-protein interactions. Overall, our experimental results confirm that our proposed model is especially effective for analyzing protein-protein interactions from a viewpoint of the latent knowledge of proteins.

Algorithms↗

PubMed2001

Preattentive perception of multiple illusory line-motion: a formal model of parallel independent-detection in visual search.

The phenomenon referred to as illusory line-motion (ILM; O. Hikosaka, S. Miyauchi, & S. Shimojo, 1993a) has been described as a measure of the local facilitation of attention gradient. However, J. Kawahara, K. Yokosawa, S. Nishida, and T. Sato (1996) have demonstrated a spatially parallel search for an "odd man out" in the ILM direction. Apart from showing preattentive ILM perception in terms of an analogy between line-motion and apparent motion, the authors examined whether ILM perception is possible without attention from another point of view. Four experiments revealed that the ILM target can be detected in parallel without invoking attentional facilitation and invalidated the possible contribution of attentional set in parallel ILM search. Participants were able to correctly detect the ILM target among multiple nontargets, even when the line orientation was changed from trial to trial. The authors' independent-detection model predicted ILM search performance well on several occasions. These findings strongly support a preattentive and stimulus-driven explanation of ILM perception.

Attention↗

PubMed1998

The relationship between assessment measures at Newcastle Medical School (Australia) and performance ratings during internship.

This study examined the utility of the domain assessment measures used in the final 2 years at Newcastle medical school in predicting performance ratings in the first year of postgraduate training (internship). Performance ratings were obtained from the clinical supervisors of two graduating classes of the University of Newcastle medical students during their five terms of internship. Three or more ratings were obtained from 57% of interns. Univariate analysis indicated that scores for three of the five domains (professional skills; identification, prevention and management of illness; self-directed learning) were significantly positively correlated with intern performance ratings. Multivariate analysis indicated that only the domain assessing identification, prevention and management of illness was predictive of higher intern performance ratings. The results support the notion that there is some value in the domain assessment model used at Newcastle in predicting the performance of junior doctors.

Australia↗

PubMed2006

Computational models of the hippocampal region: implications for prediction of risk for Alzheimer's disease in non-demented elderly.

We have pursued an interdisciplinary research program to develop novel behavioral assessment tools for evaluating specific memory impairments following damage to the medial temporal lobe, including the hippocampus and associated structures that show pathology early in the course of Alzheimer's disease (AD). Our approach uses computational models to identify the functional consequences of hippocampal-region damage, leading to testable predictions in both rodents and humans. Our modeling argues that hippocampal-region dysfunction may selectively impair the ability to generalize when familiar information is presented in novel recombinations. Previous research has shown that specific reductions in hippocampal volume in non-demented elderly individuals correlate with future development of AD. In two previous studies, we tested non-demented elderly with and without mild hippocampal atrophy (HA) on stimulus-response learning tasks. Individuals with and without HA could learn the initial information, but the HA group was selectively impaired on transfer tests where familiar features and objects were recombined. This suggests that such generalization deficits may be behavioral markers of HA, and an early indicator of risk for subsequent cognitive decline. Converging support for the relevance of these tasks to aging and Alzheimer's disease comes from our recent fMRI studies of individuals with mild cognitive impairment (MCI). Activity in the hippocampus declines with progressive training on these tasks, suggesting that the hippocampus is important for learning new stimulus representations that support subsequent transfer. Individuals with HA may be able to learn, but in a more hippocampal-independent fashion that does not support later transfer. Ultimately, this line of research could lead to a novel battery of behavioral tests sensitive to very mild hippocampal atrophy and risk for decline to AD, allowing early diagnosis and also allowing researchers to test new Alzheimer's drugs that target individuals in the earliest stages of the disease - before significant cognitive decline. A new mouse version of one of our tasks shows promise for translating these paradigms into rodents, allowing for future studies of therapeutic interventions in transgenic mouse models of AD.

Aged↗

PubMedDate not supplied

The predictive brain: temporal coincidence and temporal order in synaptic learning mechanisms.

Some forms of synaptic plasticity depend on the temporal coincidence of presynaptic activity and postsynaptic response. This requirement is consistent with the Hebbian, or correlational, type of learning rule used in many neural network models. Recent evidence suggests that synaptic plasticity may depend in part on the production of a membrane permeant-diffusible signal so that spatial volume may also be involved in correlational learning rules. This latter form of synaptic change has been called volume learning. In both Hebbian and volume learning rules, interaction among synaptic inputs depends on the degree of coincidence of the inputs and is otherwise insensitive to their exact temporal order. Conditioning experiments and psychophysical studies have shown, however, that most animals are highly sensitive to the temporal order of the sensory inputs. Although these experiments assay the behavior of the entire animal or perceptual system, they raise the possibility that nervous systems may be sensitive to temporally ordered events at many spatial and temporal scales. We suggest here the existence of a new class of learning rule, called a predictive Hebbian learning rule, that is sensitive to the temporal ordering of synaptic inputs. We show how this predictive learning rule could act at single synaptic connections and through diffuse neuromodulatory systems.

Animals↗

PubMed1995

Disturbances of learning processes in the basal ganglia in the pathogenesis of Parkinson's disease: a novel theory.

A hypothesis is proposed that the cortico-basal ganglia-thalamocortical circuit is a neural optimal control system containing a model of the controlled object. To predict the behaviour of the object, the model uses the language of afferent signals that enter the system. Based on these ideas, it is suggested that the skeletomotor basal ganglia-thalamocortical circuit serves to model the motor behaviour of the body of an individual and its environment during motor planning and performance. Association basal ganglia-thalamocortical circuits are capable of modeling the behaviour of abstract objects such as thoughts. Within the limits of the proposed theory, the dopaminergic system serves to distribute an error signal within the striatum. These error signals contain information about mismatch between model (predicted) afferent flow and real afferent flow coming from the controlled object. An error signal is necessary to tune the model on the object, and is delivered to a structure within the circuit that is responsible for the production of the error signal. An error signal is minimal when the model properly describes the object behaviour. The process of learning is initiated when an error signal increases and is complete when the error signal is minimized. Parkinson's disease is thus considered to be the consequence of modeling disfunction because of progressive functional and structural degeneration of the error distribution system in the substantia nigra pars compacta. Other clinical applications of the proposed theory are also discussed.

Afferent Pathways↗

PubMed2003

Information processing, dimensionality reduction and reinforcement learning in the basal ganglia.

Modeling of the basal ganglia has played a major role in our understanding of this elusive group of nuclei. Models of the basal ganglia have undergone evolutionary and revolutionary changes over the last 20 years, as new research in the fields of anatomy, physiology and biochemistry of these nuclei has yielded new information. Early models dealt with a single pathway through the nuclei and focused on the nature of the processing performed within it, convergence of information versus parallel processing of information. Later, the Albin-DeLong "box-and-arrow" model characterized the inter-nuclei interaction as multiple pathways while maintaining a simplistic scalar representation of the nuclei themselves. This model made a breakthrough by providing key insights into the behavior of these nuclei in hypo- and hyper-kinetic movement disorders. The next generation of models elaborated the intra-nuclei interactions and focused on the role of the basal ganglia in action selection and sequence generation which form the most current consensus regarding basal ganglia function in both normal and pathological conditions. However, new findings challenge these models and point to a different neural network approach to information processing in the basal ganglia. Here, we take an in-depth look at the reinforcement driven dimensionality reduction (RDDR) model which postulates that the basal ganglia compress cortical information according to a reinforcement signal using optimal extraction methods. The model provides new insights and experimental predictions on the computational capacity of the basal ganglia and their role in health and disease.

Animals↗

PubMed1998

Verbal contextual generalization in children with and without learning disabilities.

Braine's (1963) model of language development emphasizes the use of auditory or temporal processing so verbal contextual generalization can be produced. Recent literature on auditory or temporal processing skills of children with learning disabilities led to the prediction that these children would find it much more difficult to generalize contextually than would children without learning disabilities. The present study did not support this prediction. The implications were discussed in view of research on auditory or temporal processing skills of children with learning disabilities.

Age Factors↗

PubMed2001

Productive inventory and case/agreement contingencies: a methodological note on Rispoli (1999).

Rispoli (1999) suggests that previous studies arguing for a contingency between the case of subject pronouns and the presence/absence of verbal agreement in the acquisition of English (e.g. Schütze, 1997) suffer from methodological problems, and presents new data that fail to support earlier findings. I show that Rispoli's methodology unnecessarily biases his study against finding the predicted contingencies: it fails to take account of children's productive lexical inventory of pronoun forms. As a result, syntactic versus morphological sources of error fail to be distinguished. I explain why this distinction is crucial within the AGR/Tense Omission Model, and clarify its predictions.

Child Language↗

PubMed1991

Composite models never (well, hardly ever) compromise: reply to Schooler and Tanaka (1991)

With respect to the influence of postevent information, Schooler and Tanaka (1991) made a useful distinction between composite recollections--in which subjects retrieve "items from both the original and the postevent sources" (p.97)--and compromise recollections--in which subjects retrieve" at least one feature that cannot be exclusively associated with either the original or the postevent sources, but which reflects some compromise between [the] two" (p.97). Schooler and Tanaka argued that only the latter constitutes good evidence for blend-memory representations of the CHARM-type. As it turns out, Schooler and Tanaka's intuitions (and Metcalfe & Bjork's, initially) are faulty. Compromise recall--defined as a preference for an intervening alternative over either of the actually presented alternatives--is not normally a prediction of CHARM and may not be a prediction of composite-trace models in general. Only under specialized conditions--a systematic displacement of the test alternatives or a systematic shift attributable to assimilation to prior semantic knowledge--will computer simulations of CHARM produce unimodal compromise recollection. Equally surprising is the fact that separate-trace models, under a different set of conditions, can predict compromise recollection.

Association Learning↗