Covariance Learning Research Articles

Develop under sex teaching evaluation ories instruction of clinical nursing teaching

Objective To probe into the development teaching evaluation applied new path in the nursing teaching. Methods With development teaching, evaluation theories was basis, design clinical nursing teaching, and apply in teaching. Results Student's result and give lessons teacher the evaluation comparison of the teaching quality ( P＜0.01) . There was covariance learning meaning. Conclusions The development teaching evaluation the theories apply in clinical nursing teaching is a new path which raises clinical nursing teaching quality. Key words: Developmental; Nursing teaching; Evaluation

Discriminative Parameter Training of Unscented Kalman Filter

In this paper, we propose a learning technique to solve parameter adjustment problems of Unscented Kalman Filter (UKF) for accurate localization. The parameters of UKF consist of three kinds of parameters as follows: I) the covariance matrix of input noise, II) the covariance matrix of measurement noise, III) Hyper-parameters of UKF. UKF localization performance critically depends on these parameters. One of discriminative training methods is adopted to obtain the optimal parameters. A coordinate ascent algorithm is used as a training algorithm to optimize these parameters included in complicated functions. Simulation and experimental results are presented. The localization result using the proposed method outperforms the result of hand tuning and the result of covariance learning.

Adaptive-filter Models of the Cerebellum: Computational Analysis

Many current models of the cerebellar cortical microcircuit are equivalent to an adaptive filter using the covariance learning rule. The adaptive filter is a development of the original Marr-Albus framework that deals naturally with continuous time-varying signals, thus addressing the issue of 'timing' in cerebellar function, and it can be connected in a variety of ways to other parts of the system, consistent with the microzonal organization of cerebellar cortex. However, its computational capacities are not well understood. Here we summarise the results of recent work that has focused on two of its intrinsic properties. First, an adaptive filter seeks to decorrelate its (mossy fibre) inputs from a (climbing fibre) teaching signal. This procedure can be used both for sensory processing, e.g. removal of interference from sensory signals, and for learning accurate motor commands, by decorrelating an efference copy of those commands from a sensory signal of inaccuracy. As a model of the cerebellum the adaptive filter thus forms a natural link between events at the cellular level, such as forms of synaptic plasticity and the learning rules they embody, and intelligent behaviour at the system level. Secondly, it has been shown that the covariance learning rule enables the filter to handle input and intrinsic noise optimally. Such optimality may underlie the recently described role of the cerebellum in producing accurate smooth pursuit eye movements in the face of sensory noise. Moreover, it has the consequence of driving most input weights to very small values, consistent with experimental data that many parallel-fibre synapses are normally silent. The effectiveness of silent synapses can only be altered by LTP, so learning tasks depending on a reduction of Purkinje cell firing require the synapses to be embedded in a second, inhibitory pathway from parallel fibre to Purkinje cell. This pathway and the appropriate climbing-fibre related plasticity have been described experimentally, and its presence has implications for asymmetries and hysteresis in behavioural learning rates that are also consistent with experimental observations. These computational properties of the adaptive filter suggest that it is both powerful and realistic enough to be a suitable candidate model of the cerebellar cortical microcircuit.

Implicit learning of evaluative vs. non-evaluative covariations: The role of dimension accessibility

Implicit covariation learning, the development of simple associations without awareness, has been demonstrated repeatedly along the evaluative dimension [De Houwer, J., Thomas, S., & Baeyens, F. (2001). Associative learning of likes and dislikes: A review of 25 years of research on human evaluative conditioning. Psychological Bulletin, 127, 853–869], but associations involving other dimensions appear more difficult to learn implicitly. The present research highlights the unique properties of the evaluative dimension that may predispose it to implicit learning. We provide evidence in the first experiment that implicit covariation learning occurs along the evaluative dimension, but does not spontaneously occur along non-evaluative dimensions. In Experiment 2, implicit learning along non-evaluative dimensions occurred when participants were subliminally primed with the to-be-learned dimension. In the discussion, we integrate findings from implicit evaluative conditioning research with the broader implicit learning literature.

Silent Synapses, LTP, and the Indirect Parallel-Fibre Pathway: Computational Consequences of Optimal Cerebellar Noise-Processing

Computational analysis of neural systems is at its most useful when it uncovers principles that provide a unified account of phenomena across multiple scales and levels of description. Here we analyse a widely used model of the cerebellar contribution to sensori-motor learning to demonstrate both that its response to intrinsic and sensor noise is optimal, and that the unexpected synaptic and behavioural consequences of this optimality can explain a wide range of experimental data. The response of the Marr-Albus adaptive-filter model of the cerebellar microcircuit to noise was examined in the context of vestibulo-ocular reflex calibration. We found that, when appropriately connected, an adaptive-filter model using the covariance learning rule to adjust the weights of synapses between parallel fibres and Purkinje cells learns weight values that are optimal given the relative amount of signal and noise carried by each parallel fibre. This optimality principle is consistent with data on the cerebellar role in smooth pursuit eye movements, and predicts that many synaptic weights must be very small, providing an explanation for the experimentally observed preponderance of silent synapses. Such a preponderance has in its turn two further consequences. First, an additional inhibitory pathway from parallel fibre to Purkinje cell is required if Purkinje cell activity is to be altered in either direction from a starting point of silent synapses. Second, cerebellar learning tasks must often proceed via LTP, rather than LTD as is widely assumed. Taken together, these considerations have profound behavioural consequences, including the optimal combination of sensori-motor information, and asymmetry and hysteresis of sensori-motor learning rates.

STUDY OF HUMAN THERMOREGULATION: ADAPTIVE OPTIMIZATION CONTROL THEORY ANALYSIS

An example of homeostasis is temperature regulation at a desired level; this physiological process leads to the preservation of a stable biological environment. A control-theory–based model permits a biomedical engineer to understand the complex operation of thermoregulation, by converting general information to knowledge, and can be integrated to see how systemic parameters influence the entire system. The thermal inputs organized in the hypothalamus to activate thermoregulation responses to heat and cold stimuli, with the widely accepted set-point hypothesis for the regulation of body temperature from a control systems point of view, are, however, not entirely known. There are circumstances (e.g. fever) in which the presumed set-point mechanism appears to break down. This paper evaluates a novel set-level adaptive optimal thermal control paradigm inspired by Hebbian covariance synaptic adaptation, previously proposed based on its potential to predict the homeostatic respiratory system. It introduces a Hebbian feedback covariance learning (HFCL) concept in order to align a neuronal network into the analysis of the thermoregulation system. Hebbian theory is concerned with how neurons connect among themselves to become engrams. The passive-active mathematical model for simulating human thermoregulation during exercise was compared in cool, warm, and hot environments, and then was translated into MATLAB to predict thermoregulation. The two-node core and shell model predictions are comparable with observed thermoregulation responses from the existing literature. The thermoregulation changes with respect to proportionality constant and sensitivity of the receptors. A reasonably general agreement with the measured mean group data of earlier performed laboratory exercise studies was obtained for peak temperature, although it tended to overpredict the core body temperature.

A global bioheat model with self‐tuning optimal regulation of body temperature using Hebbian feedback covariance learning

In the lower brain, body temperature is continually being regulated almost flawlessly despite huge fluctuations in ambient and physiological conditions that constantly threaten the well-being of the body. The underlying control problem defining thermal homeostasis is one of great enormity: Many systems and sub-systems are involved in temperature regulation and physiological processes are intrinsically complex and intertwined. Thus the defining control system has to take into account the complications of nonlinearities, system uncertainties, delayed feedback loops as well as internal and external disturbances. In this paper, we propose a self-tuning adaptive thermal controller based upon Hebbian feedback covariance learning where the system is to be regulated continually to best suit its environment. This hypothesis is supported in part by postulations of the presence of adaptive optimization behavior in biological systems of certain organisms which face limited resources vital for survival. We demonstrate the use of Hebbian feedback covariance learning as a possible self-adaptive controller in body temperature regulation. The model postulates an important role of Hebbian covariance adaptation as a means of reinforcement learning in the thermal controller. The passive system is based on a simplified 2-node core and shell representation of the body, where global responses are captured. Model predictions are consistent with observed thermoregulatory responses to conditions of exercise and rest, and heat and cold stress. An important implication of the model is that optimal physiological behaviors arising from self-tuning adaptive regulation in the thermal controller may be responsible for the departure from homeostasis in abnormal states, e.g., fever. This was previously unexplained using the conventional "set-point" control theory.

Internal models in sensorimotor integration: perspectives from adaptive control theory

Internal models and adaptive controls are empirical and mathematical paradigms that have evolved separately to describe learning control processes in brain systems and engineering systems, respectively. This paper presents a comprehensive appraisal of the correlation between these paradigms with a view to forging a unified theoretical framework that may benefit both disciplines. It is suggested that the classic equilibrium-point theory of impedance control of arm movement is analogous to continuous gain-scheduling or high-gain adaptive control within or across movement trials, respectively, and that the recently proposed inverse internal model is akin to adaptive sliding control originally for robotic manipulator applications. Modular internal models' architecture for multiple motor tasks is a form of multi-model adaptive control. Stochastic methods, such as generalized predictive control, reinforcement learning, Bayesian learning and Hebbian feedback covariance learning, are reviewed and their possible relevance to motor control is discussed. Possible applicability of a Luenberger observer and an extended Kalman filter to state estimation problems—such as sensorimotor prediction or the resolution of vestibular sensory ambiguity—is also discussed. The important role played by vestibular system identification in postural control suggests an indirect adaptive control scheme whereby system states or parameters are explicitly estimated prior to the implementation of control. This interdisciplinary framework should facilitate the experimental elucidation of the mechanisms of internal models in sensorimotor systems and the reverse engineering of such neural mechanisms into novel brain-inspired adaptive control paradigms in future.

When obvious covariations are not even learned implicitly

The present experiments examine the impact of covariation type and attention on implicit covariation learning. Experiments 1 and 2 compare learning of stimulus–stimulus (s–s) and stimulus–response (s–r) covariations. Although stimuli and responses were predicted by a distinct feature of the display, implicit learning neither of s–s nor of s–r covariations was observed. Experiments 3–5 explore the impact of attention on implicit learning of an s–r covariation. Distinct features either of the targets or of an incidental but centrally presented object predicted the responses. Implicit covariation learning was restricted to predictors that were part of to-be-attended targets. Finally, Experiment 6 shows implicit learning of a partial s–r covariation in which only one of two responses is predicted by target features. It is argued that implicit learning is based on the formation of associations between simultaneously activated distinct representations. Because only attended stimuli seem to reach a sufficient level of mental distinctiveness, attending the predictive information most likely is an indispensable prerequisite for implicit covariation learning to occur.

Neuroanatomical correlates of phonological processing of Chinese characters and alphabetic words: a meta-analysis.

We used the activation likelihood estimation (ALE) method to quantitatively synthesize data from 19 published brain mapping studies of phonological processing in reading, six with Chinese and 13 with alphabetic languages. It demonstrated high concordance of cortical activity across multiple studies in each written language system as well as significant differences of activation likelihood between languages. Four neural systems for the phonological processing of Chinese characters included: (1) a left dorsal lateral frontal system at Brodmann area (BA) 9; (2) the dorsal aspect of left inferior parietal system; (3) a bilateral ventral-occipitotemporal system including portions of fusiform gyrus and middle occipital gyrus; and (4) a left ventral prefrontal system covering the superior aspect of inferior frontal gyrus. For phonological processing of written alphabetic words, cortical areas identified here are consistent with the three neural systems proposed previously in the literature: (1) a ventral prefrontal system involving superior portions of left inferior frontal gyrus; (2) a left dorsal temporoparietal system including mid-superior temporal gyri and the ventral aspect of inferior parietal cortex (supramarginal region); and (3) a left ventral occipitotemporal system. Contributions of each of these systems to phonological processing in reading were discussed, and a covariant learning hypothesis is offered to account for the findings that left middle frontal gyrus is responsible for addressed phonology in Chinese whereas left temporoparietal regions mediate assembled phonology in alphabetic languages. Language form, cognitive process, and learning strategy drive the development of functional neuroanatomy.

Self-regulation mechanism of temporally asymmetric Hebbian plasticity.

Recent biological experimental findings have shown that synaptic plasticity depends on the relative timing of the pre- and postsynaptic spikes. This determines whether long-term potentiation (LTP) or long-term depression (LTD) is induced. This synaptic plasticity has been called temporally asymmetric Hebbian plasticity (TAH). Many authors have numerically demonstrated that neural networks are capable of storing spatiotemporal patterns. However, the mathematical mechanism of the storage of spatiotemporal patterns is still unknown, and the effect of LTD is particularly unknown. In this article, we employ a simple neural network model and show that interference between LTP and LTD disappears in a sparse coding scheme. On the other hand, the covariance learning rule is known to be indispensable for the storage of sparse patterns. We also show that TAH has the same qualitative effect as the covariance rule when spatiotemporal patterns are embedded in the network.

Judgement frequency, belief revision, and serial processing of causal information.

The main aim of this research was to study the cognitive architecture underlying causal/covariation learning by investigating the frequency of judgement effect. Previous research has shown that decreasing the number of trials between opportunities to make a judgement in a covariation learning task led to a higher score after an a or d type of trial (positive cases) than after b and c trials (negative cases). Experiment I replicated this effect using a trial-by-trial procedure and examined the conditions under which it occurs. Experiment 2 demonstrated a similar frequency of judgement effect when the information was presented in the form of contingency tables. Associative or statistical single-mechanism accounts of causal and covariation learning do not provide a satisfactory explanation for these findings. An alternative belief revision model is presented.

Effects of Aging on Implicit Covariation Learning

This study used a matrix-scanning task to investigate implicit covariation learning in an older adult population. Across 5 blocks of 60 trials, 72 older adults and 72 younger adults searched visual matrices and indicated the quadrant location of the target ‘6’. There were three conditions. In the constant covariation condition (AAAAA), the matrices systematically co-occurred with a unique location of the target throughout testing. In the changed and no covariation conditions, respectively, there was either a change in the covariation (AAABA) or the location of the target appeared randomly within the matrices. For the older adults, search times markedly improved following exposure to a covariation as compared to no covariation. Furthermore, changing the learned covariation significantly disrupted the participants’ performances with search times returning to normal following reinstatement of the learned covariation. Although older adults exhibited more impairment on explicit measures of learning compared to younger adults, both groups were able to use information provided in the visual displays to guide their search for the target. For both groups, these results occurred despite the participants’ inability to verbalize knowledge for the covariation. This study, therefore, generalizes the findings from other studies of implicit learning, particularly implicit motor sequence studies, and suggests that implicit processes, unlike explicit ones, may be relatively unaffected by aging.

Research on irrelevance, helplessness, and immunization against helplessness in Spain: past, present, and future.

The main contribution to helplessness research in Spain was on immunization against helplessness and how it was modulated by predictability in animals or attributions in humans. Recent research have focused on factors determining the perception of non-contingency during learned irrelevance and the illusion of control during an uncontrollable experience. Its subsequent effects on covariation learning and the detection of controllability showed the influence of prior beliefs and attentional factors on covariation learning an learned helplessness whose influence should be integrated into current new learning models.

A Hebbian feedback covariance learning paradigm for self-tuning optimal control

We propose a novel adaptive optimal control paradigm inspired by Hebbian covariance synaptic adaptation, a preeminent model of learning and memory as well as other malleable functions in the brain. The adaptation is driven by the spontaneous fluctuations in the system input and output, the covariance of which provides useful information about the changes in the system behavior. The control structure represents a novel form of associative reinforcement learning in which the reinforcement signal is implicitly given by the covariance of the input-output (I/O) signals. Theoretical foundations for the paradigm are derived using Lyapunov theory and are verified by means of computer simulations. The learning algorithm is applicable to a general class of nonlinear adaptive control problems. This on-line direct adaptive control method benefits from a computationally straightforward design, proof of convergence, no need for complete system identification, robustness to noise and uncertainties, and the ability to optimize a general performance criterion in terms of system states and control signals. These attractive properties of Hebbian feedback covariance learning control lend themselves to future investigations into the computational functions of synaptic plasticity in biological neurons.

海馬ＣＡ１野の入力と出力逆伝搬の時間タイミング依存性ＬＴＰ／ＬＴＤ

Spatial distributions of LTP/LTD were investigated in the CA1 area of hippocampal slices by optical imaging method. A pair of electrical pulse stimuli was used to stimulate the schaffer collateral (Stim. A) and the stratum oriens (Stim. B) with various sets of the relative timing (τ). LTP was induced with -10ms<τ<10ms, τ=0ms led to the widest and strongest LTP. On the other hand, τ=±20ms induced LTD. In particular, this LTD at τ=20ms was something that had not been observed by others using cultured hippocampal neurons. The LTD at τ=20ms was blocked by bicuculline (GABA (gamma-aminobutyric acid) receptor antagonist). The importance of precise spike timing in hippocampal CA1 network is discussed in the light of computational neuroscience involving the covariance learning rule.

Synaptic learning models of map separation in the hippocampus

When rats trained to forage in one environment are exposed to a second, highly similar environment, their hippocampal place code exhibits a partial remapping in the new environment that becomes more complete with repeated exposures (Shapiro, Tanila, Eichenbaum, Hippocampus 7 (6) (1997) 624–642, Bostock, Muller, Kubie, Hippocampus 1 (2) (1991) 193–206). If the perforant path projection to CA3 functions as a pattern completion mechanism, and the DG projection via the mossy fibers performs pattern separation (O'Reilly, McClelland, Hippocampal conjunctive encoding, storage, and recall: avoiding a trade-off, Hippocampus 4 (6) (1994) 661–682), then partial remapping can be understood as the combined effect of these two projections. We investigated learning rules that could be responsible for the gradual separation of two maps, and found that, while simple Hebbian learning and Hebbian covariance learning would not produce the separation effect, BCM learning was one rule that would.

The belief revision model: asymmetrical effects of noncontingency on human covariation learning

A noncontingent experience affects the subsequent detection of positive and negative contingencies between the same events. Experiments 1 and 2 showed that such preexposure can produce both an impairment in the detection of subsequent positive contingency and a facilitation of a negative one, independent of the level of contingency during the contingent phase. Experiment 3 raised difficulties for a model that assumes that associations to the context can explain this asymmetrical effect. Experiment 4 suggested that the different weights usually assigned to the different types of trials when computing the contingency between events can change as a result of a noncontingent experience with the same events. This change supports an account of the asymmetrical effect by a belief revision model based on a mechanism that updates the weights of the different trial types as a function of previous experience. More generally, the belief revision model is a statistical (i.e., nonassociative) model of learning that is capable of accounting for trial-order effects, which have long posed problems for statistical models.

The effect of frequency of judgement and the type of trials on covariation learning.

The effect of frequency of judgement and the type of trials on covariation learning.

The effect of frequency of judgement and the type of trials on covariation learning.

The effect of frequency of judgement and the type of trials on covariation learning.