大脑序列记忆信息的提取

The purpose of this study was to examine the cognitive and neural mechanism underlying the serial position effects using cognitive experiments and ERPs(the event related potentials), for 11 item lists in very short-term and the continuous-distractor paradigm with Chinese character. The results demonstrated that when the length of list was 11 Chinese character, and the presentation time, the item interval and the retention interval was 400ms, the primacy effect and recency effect belong to the associative memory and absolute memory respectively. The retrieval of the item at the primacy part depended mainly on the context cues, but the retrieval of the item at the recency part depended mainly on the memory trace. The same results was concluded in the continuous-distractor paradigm (the presentation time was 1sec, the item interval is 12sec, and the retention interval was 30sec). Cognitive results revealed the robust serial position effects in the continuous-distractor paradigm. The different retrieval process between items at the primacy part and items at the recency part of the serial position curve was found. The behavioral responses data of ERP illustrated that the responses for the prime and recent items differed neither in accuracy nor reaction time, the retrieval time for the items at the primacy part was longer than that for the items at the recency part. And the accuracy of retrieval for the primacy part item was lower than that for the recency part items. That meant the retrieval of primacy part items needed more cognitive processes. The recent items, compared with the prime items, evoked ERPs that were more positive, this enhanced positivity occurred in a positive component peaking around 360ms. And for the same retrieval direction (forward or backward), the significant positive component difference between the retrieval for prime items and the retrieval for recent items was found. But there was no significant difference between the forward and backward retrieval at both the primacy and recency part of the serial position curve. These revealed the two kind of retrieval (forward and backward) at the same part of the serial position curve belonged to the same property. These findings fit more closely with the notion of the distinct between the associative memory and the absolute memory.

A Theory of Networks for Appxoimation and Learning

Learning an input-output mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multi-dimensional function, that is solving the problem of hypersurface reconstruction. From this point of view, this form of learning is closely related to classical approximation techniques, such as generalized splines and regularization theory. This paper considers the problems of an exact representation and, in more detail, of the approximation of linear and nolinear mappings in terms of simpler functions of fewer variables. Kolmogorov's theorem concerning the representation of functions of several variables in terms of functions of one variable turns out to be almost irrelevant in the context of networks for learning. We develop a theoretical framework for approximation based on regularization techniques that leads to a class of three-layer networks that we call Generalized Radial Basis Functions (GRBF), since they are mathematically related to the well-known Radial Basis Functions, mainly used for strict interpolation tasks. GRBF networks are not only equivalent to generalized splines, but are also closely related to pattern recognition methods such as Parzen windows and potential functions and to several neural network algorithms, such as Kanerva's associative memory, backpropagation and Kohonen's topology preserving map. They also have an interesting interpretation in terms of prototypes that are synthesized and optimally combined during the learning stage. The paper introduces several extensions and applications of the technique and discusses intriguing analogies with neurobiological data.

Abordagem neuropsicológica das alterações na atenção e memória no sindroma de Burnout

O sindroma de Burnout, quadro psicofisio-patológico tem sido objecto de investigação intensiva, desde o artigo de Freudenberger (1974) intitulado "Staff Burnout", com dois objectivos: compreendê-lo melhor, através de meios de diagnóstico, e criar técnicas de intervenção terapêutica. Na realidade, desde essa altura, foram efectuados e publicados um número avultado de trabalhos de investigação, nos campos do diagnóstico e caracterização do Burnout, e da sua resolução terapêutica. O pensamento dominante, nessa altura e ainda hoje, é de tendência analítica e/ou psico-social. Este quadro, espoletado por uma sucessão de episódios emocionalmente negativos em contexto ocupacional em indivíduos com provável predisposição genética e sujeitos a situações de pressão laboral, dos mais diversos tipos (podendo ir do “simples” stress por acumulação de tarefas até às situações de mobbing), tem efeitos frequentemente dramáticos ao nível da dinâmica biopsico- social, nos seus mais diversos aspectos. Estes estendem-se, quase sempre, muito para lá das problemáticas laborais, prejudicando, de forma mais ou menos grave, as interacções sociais com particular impacto ao nível da dinâmica familiar. Por outro lado, o Burnout propicia o aparecimento de patologias diversas, já que toda a estrutura psiconeuro-endocrino-imunulógica estará posta em causa, potenciando situações de fragilidade sistémica. No entanto, há aspectos correlacionáveis com este quadro disfuncional que têm sido muito pouco abordados – alterações cognitivo-operativas ou neuropsicológicas. Aliás os trabalhos que sobre eles incidem são em número muito reduzido. Assim após termos registado queixas, acentuadas, ao nível da capacidade de concentração e da memória em pessoas com burnout observadas na clínica hospitalar e privada, decidimos investigar estas situações, usando uma metodologia clínica de tipo qualitativo, e constatámos que, na realidade, as queixas eram pertinentes. Posto isto, achámos que a situação deveria ser aprofundada e partimos para um trabalho mais sistematizado, este, com o objectivo de caracterizar melhor o tipo de disfunções atencionais e mnésicas. Para isso, após uma selecção prévia, a partir de um grupo de 192 enfermeiros que responderam à Escala de Maslach, avaliámos uma amostra de risco constituída por 40 enfermeiros e enfermeiras, de Instituições Psiquiátricas da Grande Lisboa, trabalhando em urgência e enfermaria, que comparámos com uma amostra de igual número de enfermeiros, desenvolvendo a sua actividade na consulta externa ou em ambientes mais protegidos de stress ocupacional continuo. Para o efeito, e após uma anamnese cuidada, aplicámos provas de atenção e memória, sensíveis a qualquer tipo de compromisso encefálico seja ele funcional ou patológico. Para a componente atenção/concentração e a componente vísuo-grafo-espacial usámos a prova de Toulouse-Piéron, assim como as séries de dígitos ou digit span, para a vertente audio-verbal. A dinâmica mnésica foi avaliada através da prova de memória associativa (Escala de Memória de Wechsler) para testar a variante áudio-verbal, e a reprodução de figuras (Escala de Memória de Wechlser). Os resultados, após uma dupla análise clínica e estatística, comprovaram globalmente as hipóteses, indicando uma correlação significativa entre o grau de Burnout e os défices neuropsicológicos detectados: alteração da atenção/concentração e dismnésia, de natureza limitativa face às exigências quotidianas dos indivíduos. Finalmente, com base na revisão da literatura e os resultados deste estudo, foi esquematizado um Modelo Neuropsicológico do sindroma de Burnout, que nos parece espelhar as relações entre este quadro clínico, as alterações cognitivooperativas encontradas e as principais estruturas encefálicas, que julgamos, implicadas em toda a dinâmica do processo disfuncional.

Self-organising neural networks for adaptive control

Self-organizing neural networks have been implemented in a wide range of application areas such as speech processing, image processing, optimization and robotics. Recent variations to the basic model proposed by the authors enable it to order state space using a subset of the input vector and to apply a local adaptation procedure that does not rely on a predefined test duration limit. Both these variations have been incorporated into a new feature map architecture that forms an integral part of an Hybrid Learning System (HLS) based on a genetic-based classifier system. Problems are represented within HLS as objects characterized by environmental features. Objects controlled by the system have preset targets set against a subset of their features. The system's objective is to achieve these targets by evolving a behavioural repertoire that efficiently explores and exploits the problem environment. Feature maps encode two types of knowledge within HLS — long-term memory traces of useful regularities within the environment and the classifier performance data calibrated against an object's feature states and targets. Self-organization of these networks constitutes non-genetic-based (experience-driven) learning within HLS. This paper presents a description of the HLS architecture and an analysis of the modified feature map implementing associative memory. Initial results are presented that demonstrate the behaviour of the system on a simple control task.

Neurofuzzy mixture of experts network parallel learning and model construction algorithms

A connection between a fuzzy neural network model with the mixture of experts network (MEN) modelling approach is established. Based on this linkage, two new neuro-fuzzy MEN construction algorithms are proposed to overcome the curse of dimensionality that is inherent in the majority of associative memory networks and/or other rule based systems. The first construction algorithm employs a function selection manager module in an MEN system. The second construction algorithm is based on a new parallel learning algorithm in which each model rule is trained independently, for which the parameter convergence property of the new learning method is established. As with the first approach, an expert selection criterion is utilised in this algorithm. These two construction methods are equivalent in their effectiveness in overcoming the curse of dimensionality by reducing the dimensionality of the regression vector, but the latter has the additional computational advantage of parallel processing. The proposed algorithms are analysed for effectiveness followed by numerical examples to illustrate their efficacy for some difficult data based modelling problems.

Neurofuzzy design and model construction of nonlinear dynamical processes from data

A common problem in many data based modelling algorithms such as associative memory networks is the problem of the curse of dimensionality. In this paper, a new two-stage neurofuzzy system design and construction algorithm (NeuDeC) for nonlinear dynamical processes is introduced to effectively tackle this problem. A new simple preprocessing method is initially derived and applied to reduce the rule base, followed by a fine model detection process based on the reduced rule set by using forward orthogonal least squares model structure detection. In both stages, new A-optimality experimental design-based criteria we used. In the preprocessing stage, a lower bound of the A-optimality design criterion is derived and applied as a subset selection metric, but in the later stage, the A-optimality design criterion is incorporated into a new composite cost function that minimises model prediction error as well as penalises the model parameter variance. The utilisation of NeuDeC leads to unbiased model parameters with low parameter variance and the additional benefit of a parsimonious model structure. Numerical examples are included to demonstrate the effectiveness of this new modelling approach for high dimensional inputs.

Optimal piecewise locally linear modeling

Associative memory networks such as Radial Basis Functions, Neurofuzzy and Fuzzy Logic used for modelling nonlinear processes suffer from the curse of dimensionality (COD), in that as the input dimension increases the parameterization, computation cost, training data requirements, etc. increase exponentially. Here a new algorithm is introduced for the construction of a Delaunay input space partitioned optimal piecewise locally linear models to overcome the COD as well as generate locally linear models directly amenable to linear control and estimation algorithms. The training of the model is configured as a new mixture of experts network with a new fast decision rule derived using convex set theory. A very fast simulated reannealing (VFSR) algorithm is utilized to search a global optimal solution of the Delaunay input space partition. A benchmark non-linear time series is used to demonstrate the new approach.

Data based constructive identification - overcoming the curse of dimensionality

The modelling of a nonlinear stochastic dynamical processes from data involves solving the problems of data gathering, preprocessing, model architecture selection, learning or adaptation, parametric evaluation and model validation. For a given model architecture such as associative memory networks, a common problem in non-linear modelling is the problem of "the curse of dimensionality". A series of complementary data based constructive identification schemes, mainly based on but not limited to an operating point dependent fuzzy models, are introduced in this paper with the aim to overcome the curse of dimensionality. These include (i) a mixture of experts algorithm based on a forward constrained regression algorithm; (ii) an inherent parsimonious delaunay input space partition based piecewise local lineal modelling concept; (iii) a neurofuzzy model constructive approach based on forward orthogonal least squares and optimal experimental design and finally (iv) the neurofuzzy model construction algorithm based on basis functions that are Bézier Bernstein polynomial functions and the additive decomposition. Illustrative examples demonstrate their applicability, showing that the final major hurdle in data based modelling has almost been removed.

Both younger and older adults have difficulty updating emotional memories

Objective. The main purpose of the study was to examine whether emotion impairs associative memory for previously seen items in older adults, as previously observed in younger adults. Method. Thirty-two younger adults and 32 older adults participated. The experiment consisted of 2 parts. In Part 1, participants learned picture–object associations for negative and neutral pictures. In Part 2, they learned picture–location associations for negative and neutral pictures; half of these pictures were seen in Part 1 whereas the other half were new. The dependent measure was how many locations of negative versus neutral items in the new versus old categories participants remembered in Part 2. Results. Both groups had more difficulty learning the locations of old negative pictures than of new negative pictures. However, this pattern was not observed for neutral items. Discussion. Despite the fact that older adults showed overall decline in associative memory, the impairing effect of emotion on updating associative memory was similar between younger and older adults.

Spatial ability is impaired and mineralcorticoid receptor mRNA expression in zebra finches (Taeniopygia guttata) selected for acute high corticosterone response to stress

In mammals, stress hormones have profound influences on spatial learning and memory. Here, we investigated whether glucocorticoids influence cognitive abilities in birds by testing a line of zebra finches selectively bred to respond to an acute stressor with high plasma corticosterone (CORT) levels. Cognitive performance was assessed by spatial and visual one-trial associative memory tasks. Task performance in the high CORT birds was compared with that of the random-bred birds from a control breeding line. The birds selected for high CORT in response to an acute stressor performed less well than the controls in the spatial task, but there were no significant differences between the lines in performance during the visual task. The birds from the two lines did not differ in their plasma CORT levels immediately after the performance of the memory tasks; nevertheless, there were significant differences in peak plasma CORT between the lines. The high CORT birds also had significantly lower mineralocorticoid receptor mRNA expression in the hippocampus than the control birds. There was no measurable difference between the lines in glucocorticoid receptor mRNA density in either the hippocampus or the paraventricular nucleus. Together, these findings provide evidence to suggest that stress hormones have important regulatory roles in avian spatial cognition.

Scaling connectionist compositional representations

The Recursive Auto-Associative Memory (RAAM) has come to dominate connectionist investigations into representing compositional structure. Although an adequate model when dealing with limited data, the capacity of RAAM to scale-up to real-world tasks has been frequently questioned. RAAM networks are difficult to train (due to the moving target effect) and as such training times can be lengthy. Investigations into RAAM have produced many variants in an attempt to overcome such limitations. We outline how one such model ((S)RAAM) is able to quickly produce context-sensitive representations that may be used to aid a deterministic parsing process. By substituting a symbolic stack in an existing hybrid parser, we show that (S)RAAM is more than capable of encoding the real-world data sets employed. We conclude by suggesting that models such as (S)RAAM offer valuable insights into the features of connectionist compositional representations.

Exponential stability for markovian jumping stochastic BAM neural networks with mode-dependent probabilistic time-varying delays and impulse control

Abstract
In this article, an exponential stability analysis of Markovian jumping stochastic bidirectional associative memory (BAM) neural networks with mode-dependent probabilistic time-varying delays and impulsive control is investigated. By establishment of a stochastic variable with Bernoulli distribution, the information of probabilistic time-varying delay is considered and transformed into one with deterministic time-varying delay and stochastic parameters. By fully taking the inherent characteristic of such kind of stochastic BAM neural networks into account, a novel Lyapunov-Krasovskii functional is constructed with as many as possible positive definite matrices which depends on the system mode and a triple-integral term is introduced for deriving the delay-dependent stability conditions. Furthermore, mode-dependent mean square exponential stability criteria are derived by constructing a new Lyapunov-Krasovskii functional with modes in the integral terms and using some stochastic analysis techniques. The criteria are formulated in terms of a set of linear matrix inequalities, which can be checked efficiently by use of some standard numerical packages. Finally, numerical examples and its simulations are given to demonstrate the usefulness and effectiveness of the proposed results.

Manipulação do contexto da tarefa A-não-B: efeitos no comportamento perseverativo no olhar

Pós-graduação em Ciências da Motricidade - IBRC

Developmental aspects of synaesthesia across the adult lifespan

In synaesthesia, stimuli such as sounds, words or letters trigger experiences of colors, shapes or tastes and the consistency of these experiences is a hallmark of this condition. In this study we investigate for the first time whether there are age-related changes in the consistency of synaesthetic experiences. We tested a sample of more than 400 grapheme-color synaesthetes who have color experiences when they see letters and/or digits with a well-established test of consistency. Our results showed a decline in the number of consistent grapheme-color associations across the adult lifespan. We also assessed age-related changes in the breadth of the color spectrum. The results showed that the appearance of primary colors (i.e., red, blue, and green) was mainly age-invariant. However, there was a decline in the occurrence of lurid colors while brown and achromatic tones occurred more often as concurrents in older age. These shifts in the color spectrum suggest that synaesthesia does not simply fade, but rather undergoes more comprehensive changes. We propose that these changes are the result of a combination of both age-related perceptual and memory processing shifts.

Tracking the Flow of Information through the Hippocampal Formation in the Rat

The hippocampus receives input from upper levels of the association cortex and is implicated in many mnemonic processes, but the exact mechanisms by which it codes and stores information is an unresolved topic. This work examines the flow of information through the hippocampal formation while attempting to determine the computations that each of the hippocampal subfields performs in learning and memory. The formation, storage, and recall of hippocampal-dependent memories theoretically utilize an autoassociative attractor network that functions by implementing two competitive, yet complementary, processes. Pattern separation, hypothesized to occur in the dentate gyrus (DG), refers to the ability to decrease the similarity among incoming information by producing output patterns that overlap less than the inputs. In contrast, pattern completion, hypothesized to occur in the CA3 region, refers to the ability to reproduce a previously stored output pattern from a partial or degraded input pattern. Prior to addressing the functional role of the DG and CA3 subfields, the spatial firing properties of neurons in the dentate gyrus were examined. The principal cell of the dentate gyrus, the granule cell, has spatially selective place fields; however, the behavioral correlates of another excitatory cell, the mossy cell of the dentate polymorphic layer, are unknown. This report shows that putative mossy cells have spatially selective firing that consists of multiple fields similar to previously reported properties of granule cells. Other cells recorded from the DG had single place fields. Compared to cells with multiple fields, cells with single fields fired at a lower rate during sleep, were less likely to burst, and were more likely to be recorded simultaneously with a large population of neurons that were active during sleep and silent during behavior. These data suggest that single-field and multiple-field cells constitute at least two distinct cell classes in the DG. Based on these characteristics, we propose that putative mossy cells tend to fire in multiple, distinct locations in an environment, whereas putative granule cells tend to fire in single locations, similar to place fields of the CA1 and CA3 regions. Experimental evidence supporting the theories of pattern separation and pattern completion comes from both behavioral and electrophysiological tests. These studies specifically focused on the function of each subregion and made implicit assumptions about how environmental manipulations changed the representations encoded by the hippocampal inputs. However, the cell populations that provided these inputs were in most cases not directly examined. We conducted a series of studies to investigate the neural activity in the entorhinal cortex, dentate gyrus, and CA3 in the same experimental conditions, which allowed a direct comparison between the input and output representations. The results show that the dentate gyrus representation changes between the familiar and cue altered environments more than its input representations, whereas the CA3 representation changes less than its input representations. These findings are consistent with longstanding computational models proposing that (1) CA3 is an associative memory system performing pattern completion in order to recall previous memories from partial inputs, and (2) the dentate gyrus performs pattern separation to help store different memories in ways that reduce interference when the memories are subsequently recalled.