Spikes, Synchrony, and Attentive Learning by Laminar Thalamocortical Circuits

This article develops the Synchronous Matching Adaptive Resonance Theory (SMART) neural model to explain how the brain may coordinate multiple levels of thalamocortical and corticocortical processing to rapidly learn, and stably remember, important information about a changing world. The model clarifies how bottom-up and top-down processes work together to realize this goal, notably how processes of learning, expectation, attention, resonance, and synchrony are coordinated. The model hereby clarifies, for the first time, how the following levels of brain organization coexist to realize cognitive processing properties that regulate fast learning and stable memory of brain representations: single cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and acetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions; aggregate cell recordings, such as current-source densities and local field potentials; and single cell and large-scale inter-areal oscillations in the gamma and beta frequency domains. In particular, the model predicts how laminar circuits of multiple cortical areas interact with primary and higher-order specific thalamic nuclei and nonspecific thalamic nuclei to carry out attentive visual learning and information processing. The model simulates how synchronization of neuronal spiking occurs within and across brain regions, and triggers STDP. Matches between bottom-up adaptively filtered input patterns and learned top-down expectations cause gamma oscillations that support attention, resonance, and learning. Mismatches inhibit learning while causing beta oscillations during reset and hypothesis testing operations that are initiated in the deeper cortical layers. The generality of learned recognition codes is controlled by a vigilance process mediated by acetylcholine.

Neural Pattern Recognition on Multichannel Input Representation

This article presents a new neural pattern recognition architecture on multichannel data representation. The architecture emploies generalized ART modules as building blocks to construct a supervised learning system generating recognition codes on channels dynamically selected in context using serial and parallel match trackings led by inter-ART vigilance signals.

Fuzzy ART

Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP synthesize fuzzy logic and ART networks by exploiting the formal similarity between the computations of fuzzy subsethood and the dynamics of ART category choice, search, and learning. Fuzzy ART self-organizes stable recognition categories in response to arbitrary sequences of analog or binary input patterns. It generalizes the binary ART 1 model, replacing the set-theoretic: intersection (∩) with the fuzzy intersection (∧), or component-wise minimum. A normalization procedure called complement coding leads to a symmetric: theory in which the fuzzy inter:>ec:tion and the fuzzy union (∨), or component-wise maximum, play complementary roles. Complement coding preserves individual feature amplitudes while normalizing the input vector, and prevents a potential category proliferation problem. Adaptive weights :otart equal to one and can only decrease in time. A geometric interpretation of fuzzy AHT represents each category as a box that increases in size as weights decrease. A matching criterion controls search, determining how close an input and a learned representation must be for a category to accept the input as a new exemplar. A vigilance parameter (p) sets the matching criterion and determines how finely or coarsely an ART system will partition inputs. High vigilance creates fine categories, represented by small boxes. Learning stops when boxes cover the input space. With fast learning, fixed vigilance, and an arbitrary input set, learning stabilizes after just one presentation of each input. A fast-commit slow-recode option allows rapid learning of rare events yet buffers memories against recoding by noisy inputs. Fuzzy ARTMAP unites two fuzzy ART networks to solve supervised learning and prediction problems. A Minimax Learning Rule controls ARTMAP category structure, conjointly minimizing predictive error and maximizing code compression. Low vigilance maximizes compression but may therefore cause very different inputs to make the same prediction. When this coarse grouping strategy causes a predictive error, an internal match tracking control process increases vigilance just enough to correct the error. ARTMAP automatically constructs a minimal number of recognition categories, or "hidden units," to meet accuracy criteria. An ARTMAP voting strategy improves prediction by training the system several times using different orderings of the input set. Voting assigns confidence estimates to competing predictions given small, noisy, or incomplete training sets. ARPA benchmark simulations illustrate fuzzy ARTMAP dynamics. The chapter also compares fuzzy ARTMAP to Salzberg's Nested Generalized Exemplar (NGE) and to Simpson's Fuzzy Min-Max Classifier (FMMC); and concludes with a summary of ART and ARTMAP applications.

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.

Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.

Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps

A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors. The architecture, called Fuzzy ARTMAP, achieves a synthesis of fuzzy logic and Adaptive Resonance Theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Fuzzy ARTMAP also realizes a new Minimax Learning Rule that conjointly minimizes predictive error and maximizes code compression, or generalization. This is achieved by a match tracking process that increases the ART vigilance parameter by the minimum amount needed to correct a predictive error. As a result, the system automatically learns a minimal number of recognition categories, or "hidden units", to met accuracy criteria. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy logic play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Improved prediction is achieved by training the system several times using different orderings of the input set. This voting strategy can also be used to assign probability estimates to competing predictions given small, noisy, or incomplete training sets. Four classes of simulations illustrate Fuzzy ARTMAP performance as compared to benchmark back propagation and genetic algorithm systems. These simulations include (i) finding points inside vs. outside a circle; (ii) learning to tell two spirals apart; (iii) incremental approximation of a piecewise continuous function; and (iv) a letter recognition database. The Fuzzy ARTMAP system is also compared to Salzberg's NGE system and to Simpson's FMMC system.

Sound the alarm: fraud in neuroscience.

We expect scientists to follow a code of honor and conduct and to report their research honestly and accurately, but so-called scientific misconduct, which includes plagiarism, faked data, and altered images, has led to a tenfold increase in the number of retractions over the past decade. Among the reasons for this troubling upsurge is increased competition for journal placement, grant money, and prestigious appointments. The solutions are not easy, but reform and greater vigilance is needed.

DIESE Final Report

The DIESE program (Determination of relevant Indicators for Environmental monitoring: A Strategy for Europe) brought together seven French and British research teams, a private company and the agencies responsible for the management of water bodies of the two countries (ONEMA and the Environmental Agency) in a joint effort to document the ecotoxicological effects related to the presence of chemicals in the environment. To contribute to a better understanding and management of the environment, the program has expanded its efforts to (1) use existing knowledge, or new information acquired during the research program, to identify important biological problems affecting wildlife, (2) increase our understanding of toxicological mechanisms involved and thus be able to identify the causes of the identified dysfunctions and (3) to hone our expertise and vigilance systems in order to better monitor changes in the environment and make appropriate diagnoses. The first part of the program identified clear biological effects, and using biological tests representative of the mechanisms of action of compounds, identified the responsible compounds present in the environment. In connection with the feminization observed in many fish species in European streams, a search for estrogenic and anti-androgenic compounds was conducted. A new test identifying estrogenic compounds has been developed in roach and the ER-Calux test for anti-androgenic effects has been implemented. The results showed that, in addition to biocides such as triclosan and chlorophène, many aromatic hydrocarbon compounds are likely to disturb the physiology of living organisms by interacting with the androgen receptor. Six of these were identified in sediment extracts: benzanthrone, fluoranthene, 1,2- benzodiphenylene sulfide, benzo[a]pyrene, benz[a] anthracene, and 9-phenylcarbazole. The second part of the program aimed at documenting and understanding the mechanisms of action of chemicals leading to physiological changes. This work represents a particular challenge when dealing with molluscs, as knowledge about their physiology and endocrinology is still fragmentary. Thus, new technologies including metabolomic and transcriptomic analyses have been implemented in order to obtain a comprehensive picture of the effects on molluscs. Metabolomic research demonstrated that estrogenic compounds are able to alter the metabolism of eicosanoids and amines, while transcriptomic strategies identified genes whose expression is altered in intersex clams. Because these genes mainly appear as “male” genes, the results suggest that these profound physiological changes result from demasculinisation of male clams. Proteomic studies have also been carried out to elucidate the mechanisms of action of pollutants on fish physiology. These studies generally included a set of molecular marker measurements in an integrative and ecological perspective. The results showed that not only male fish physiology is altered but also female reproductive status is impaired. Moreover, it appeared that other alterations of the fish endocrine system, such as androgenic effects, are at work and that the immune system is also subject to chemical pressure including effects from environmental estrogens. Notably, the immune system, like the endocrine system, seems to show periods of particular sensitivity during development. Measurements on growth and on the general metabolism emphasize the importance of environmental conditions in the physiology of aquatic organisms and in particular the inter-site variability due to temperature,hypoxic conditions, and fish development strategies. They thus provide a unique perspective that allow us to better understand the context and consequences of natural conditions on the population. In a third part of the program, the research conducted had the objective of developing and testing a biomarker strategy to support the environmental management methodologies. Two lanes of specific studies have been followed. The first was to implement, over all or part of the study area, robust biomarkers to establish maps that highlight the water bodies at risk and provide information on sources of compounds and associated disturbances. The second part of the work aimed at exploring methodologies to take advantage of biomarker measurements and to integrate them in a very simple and clear index. Partial or comprehensive maps of the Channel area were produced to report the presence of mutagenic or anti-androgenic compounds in the sediments, intersex fish and clams, and imposex. These maps may remain to be completed and work will be necessary to confront this information in order to learn relevant lessons for management of the environment, a goal that the DIESE program has contributed to by providing some necessary and original information.

Determination of relevant Indicators for Environmental monitoring: A Strategy for Europe (DIESE) Final Scientific Report

The DIESE program (Determination of relevant Indicators for Environmental monitoring: A Strategy for Europe) brought together seven French and British research teams, a private company and the agencies responsible for the management of water bodies of the two countries (ONEMA and the Environmental Agency) in a joint effort to document the ecotoxicological effects related to the presence of chemicals in the environment. To contribute to a better understanding and management of the environment, the program has expanded its efforts to (1) use existing knowledge, or new information acquired during the research program, to identify important biological problems affecting the wildlife, (2) increase our understanding of toxicological mechanisms involved and thus be able to identify the causes of the identified dysfunctions and (3) to hone our expertise and vigilance system in order to better monitor changes in the environment and make appropriate diagnoses.

Substance Use among Young People Living in Residential State Care

Existing empirical evidence on substance use among young people living in residential state care during adolescence is comparatively limited. This paper reports on substance use trends of young people living in residential state care during three annual data-sweeps when aged 14, 15 and 16 years. A repeated cross-sectional research design was utilised in the research. The findings suggest some similarities for lifetime prevalence rates for tobacco and alcohol use for those living in residential state care with a group of same-age young people not living in residential state care who participated in the research. However, solvent abuse and cannabis use was higher among those living in care. More frequent substance use was reported by the residential care sample for all substances at each stage of the study. These findings suggest that young people living in state care continue to merit higher levels of vigilance from researchers and policy-makers in order to fully understand this behaviour and develop appropriate prevention initiatives to meet their needs regarding potential drug problems.

Shared or unshared parental care in overwintering brent geese (Branta bernicla hrota)

We examine brood size effects on the behaviour of wintering parent and juvenile brent geese (Branta bernicla hrota) to test predictions of shared and unshared parental care models. The behaviour of both parents and offspring appear to be influenced by declining food availability over the winter. Parental vigilance increased with brood size and may be explained by vigilance having functions in addition to antipredator behaviour where the benefits are shared among the brood. There was no increase in parental aggression with brood size and this does not fit the prediction of shared care. Nevertheless, large families are able to monopolize better feeding areas compared with smaller families and large families static feed more but walk feed less than do small families, the former apparently being the preferred mode. The presence of additional young, rather than increasing the amount of parental aggression, seems to enhance the family's competitive ability. Because parents with large broods benefit from enhanced access to resources there is likely to be no additional significant cost in the parental care of larger broods (sensu Trivers 1972).

Attention deficits in Alzheimer's disease and vascular dementia

OBJECTIVE:

To compare the performance of patients with mild-moderate Alzheimer's disease (AD) and vascular dementia (VaD) on tests of information processing and attention.

METHOD:

Patients with AD (n=75) and VaD (n=46) were recruited from a memory clinic along with dementia-free participants (n=28). They underwent specific tests of attention from the Cognitive Drug Research battery, and pen and paper tests including Colour Trails A and B and Stroop. All patients had a CT brain scan that was independently scored for white-matter change/ischaemia.

RESULTS:

Attention was impaired in both AD and VaD patients. VaD patients had more impaired choice reaction times and were less accurate on a vigilance test measuring sustained attention. Deficits in selective and divided attention occurred in both patient groups and showed the strongest correlations with Mini Mental State Examination scores.

CONCLUSION:

This study demonstrates problems with the attentional network in mild-moderate AD and VaD. The authors propose that attention should be tested routinely in a memory clinic setting.

Retinal drusen:Harbingers of age, safe havens for trouble

Drusen are small focal extracellular deposits underneath the retina, visible ophthalmoscopically as yellow dots. The more hard drusen there are, the greater the risk of developing soft drusen and retinal pigmentary changes, which in turn increase the risk of developing advanced age-related macular degeneration. Much remains to be discovered about drusen. For the patient with drusen, basic advice on diet and smoking and maintenance of a high level of vigilance for visual changes is appropriate management. © The Author 2009. Published by Oxford University Press [on behalf of the British Geriatrics Society]. All rights reserved.

Supernatural Beliefs, Unconscious Threat and Judgment Bias in Tibetan Buddhists

Individuals who have been subtly reminded of death display heightened in-group favouritism, or “worldview defense.” Terror management theory argues (i) that death cues engender worldview defense via psychological mechanisms specifically evolved to suppress death anxiety, and (ii) that the core function of religiosity is to suppress death anxiety. Thus, terror management theory predicts that extremely religious individuals will not evince worldview defense. Here, two studies are presented in support of an alternative perspective. According to the unconscious vigilance hypothesis, subtly processed threats (which need not pertain to death) heighten sensitivity to affectively valenced stimuli (which need not pertain to cultural attitudes). From this perspective, religiosity mitigates the influence of mortality-salience only insofar as afterlife doctrines reduce the perceived threat posed by death. Tibetan Buddhism portrays death as a perilous gateway to rebirth rather than an end to suffering; faith in this doctrine should therefore not be expected to nullify mortality-salience effects. In Study 1, devout Tibetan Buddhists who were subtly reminded of death produced exaggerated aesthetic ratings unrelated to cultural worldviews. In Study 2, devout Tibetan Buddhists produced worldview defense following subliminal exposure to non-death cues of threat. The results demonstrate both the domain-generality of the process underlying worldview defense and the importance of religious doctrinal content in moderating mortality-salience effects.

Obsessive-compulsive symptoms and attentional bias: An eye-tracking methodology

Background and objectives: Cognitive models suggest that attentional biases are integral in the maintenance of obsessive-compulsive symptoms (OCS). Such biases have been established experimentally in anxiety disorders; however, the evidence is unclear in Obsessive Compulsive disorder (OCD). In the present study, an eye-tracking methodology was employed to explore attentional biases in relation to OCS.
Methods: A convenience sample of 85 community volunteers was assessed on OCS using the Yale-Brown Obsessive Compulsive Scale-self report. Participants completed an eye-tracking paradigm where they were exposed to OCD, Aversive and Neutral visual stimuli. Indices of attentional bias were derived from the eye-tracking data.
Results: Simple linear regressions were performed with OCS severity as the predictor and eye-tracking measures of the different attentional biases for each of the three stimuli types were the criterion variables. Findings revealed that OCS severity moderately predicted greater frequency and duration of fixations on OCD stimuli, which reflect the maintenance attentional bias. No significant results were found in support of other biases.
Limitations: Interpretations based on a non-clinical sample limit the generalisability of the conclusions, although use of such samples in OCD research has been found to be comparable to clinical populations. Future research would include both clinical and sub-clinical participants.
Conclusions: Results provide some support for the theory of maintained attention in OCD attentional biases, as opposed to vigilance theory. Individuals with greater OCS do not orient to OCD stimuli any faster than individuals with lower OCS, but once a threat is identified, these individuals allocate more attention to OCS-relevant stimuli.