Modeling infant speech sound discrimination using simple associative networks

Infants' responses in speech sound discrimination tasks can be nonmonotonic over time. Stager and Werker (1997) reported such data in a bimodal habituation task. In this task, 8-month-old infants were capable of discriminations that involved minimal contrast pairs, whereas 14-month-old infants were not. It was argued that the older infants' attenuated performance was linked to their processing of the stimuli for meaning. The authors suggested that these data are diagnostic of a qualitative shift in infant cognition. We describe an associative connectionist model showing a similar decrement in discrimination without any qualitative shift in processing. The model suggests that responses to phonemic contrasts may be a nonmonotonic function of experience with language. The implications of this idea are discussed. The model also provides a formal framework for studying habituation-dishabituation behaviors in infancy.

Structure and rheology of aqueous micellar solutions and gels formed from an associative poly(oxybutylene)-poly(oxyethylene)-poly(oxybutylene) triblock copolymer

The structure and shear flow behaviour of aqueous micellar solutions and gels formed by an amphiphilic poly(oxybutylene)-poly(oxyethylene)-poly(oxybutylene) triblock copolymer with a lengthy hydrophilic poly(oxyethylene) block has been investigated by rheology, small angle neutron scattering (SANS) and small-angle X-ray scattering (SAXS). SANS revealed that bridging of chains between micelles introduces, in the micellar solution, an attractive long-range component which can be described through a potential of interaction corresponding to sticky soft spheres. The strength of the attractive interaction increases with increasing concentration. Rheology showed that the dependence of the storage modulus with temperature can be explained as a function of the micellar bridging, micellisation and phase morphology. SAXS studies showed that the orientation adopted by the system in the get phase under shear is similar to that previously observed by us for the gel phase of a poly(oxyethylene)-poly(oxybutylene) diblock copolymer with a long poly(oxyethylene) chain, suggesting that the micellar corona/core length ratio and not the architecture of the block copolymer influences the alignment of the gel phase under shear.

Multimodal semantic-associative collateral labelling and indexing of still images

A novel framework for multimodal semantic-associative collateral image labelling, aiming at associating image regions with textual keywords, is described. Both the primary image and collateral textual modalities are exploited in a cooperative and complementary fashion. The collateral content and context based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix, of the visual keywords, A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. Finally, we use Self Organising Maps to examine the classification and retrieval effectiveness of the proposed high-level image feature vector model which is constructed based on the image labelling results.

Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms

This work compares and contrasts results of classifying time-domain ECG signals with pathological conditions taken from the MITBIH arrhythmia database. Linear discriminant analysis and a multi-layer perceptron were used as classifiers. The neural network was trained by two different methods, namely back-propagation and a genetic algorithm. Converting the time-domain signal into the wavelet domain reduced the dimensionality of the problem at least 10-fold. This was achieved using wavelets from the db6 family as well as using adaptive wavelets generated using two different strategies. The wavelet transforms used in this study were limited to two decomposition levels. A neural network with evolved weights proved to be the best classifier with a maximum of 99.6% accuracy when optimised wavelet-transform ECG data wits presented to its input and 95.9% accuracy when the signals presented to its input were decomposed using db6 wavelets. The linear discriminant analysis achieved a maximum classification accuracy of 95.7% when presented with optimised and 95.5% with db6 wavelet coefficients. It is shown that the much simpler signal representation of a few wavelet coefficients obtained through an optimised discrete wavelet transform facilitates the classification of non-stationary time-variant signals task considerably. In addition, the results indicate that wavelet optimisation may improve the classification ability of a neural network. (c) 2005 Elsevier B.V. All rights reserved.

Global exponential periodicity of a class of bidirectional associative memory networks with finite distributed delays

In this paper, we study the periodic oscillatory behavior of a class of bidirectional associative memory (BAM) networks with finite distributed delays. A set of criteria are proposed for determining global exponential periodicity of the proposed BAM networks, which assume neither differentiability nor monotonicity of the activation function of each neuron. In addition, our criteria are easily checkable. (c) 2005 Elsevier Inc. All rights reserved.

Semantic-associative visual content labelling and retrieval: A multimodal approach

A novel framework referred to as collaterally confirmed labelling (CCL) is proposed, aiming at localising the visual semantics to regions of interest in images with textual keywords. Both the primary image and collateral textual modalities are exploited in a mutually co-referencing and complementary fashion. The collateral content and context-based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix of the visual keywords. A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. We introduce a novel high-level visual content descriptor that is devised for performing semantic-based image classification and retrieval. The proposed image feature vector model is fundamentally underpinned by the CCL framework. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval, respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicate that the proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models. (C) 2007 Elsevier B.V. All rights reserved.

Auto-associative memory using n-tuple techniques

The use of n-tuple or weightless neural networks as pattern recognition devices has been well documented. They have a significant advantages over more common networks paradigms, such as the multilayer perceptron in that they can be easily implemented in digital hardware using standard random access memories. To date, n-tuple networks have predominantly been used as fast pattern classification devices. The paper describes how n-tuple techniques can be used in the hardware implementation of a general auto-associative network.

I owe you: age-related similarities and differences in associative memory for gains and losses

Older adults often experience associative memory impairments but can sometimes remember important information. The current experiments investigate potential age-related similarities and differences associate memory for gains and losses. Younger and older participants were presented with faces and associated dollar amounts, which indicated how much money the person “owed” the participant, and were later given a cued recall test for the dollar amount. Experiment 1 examined face-dollar amount pairs while Experiment 2 included negative dollar amounts to examine both gains and losses. While younger adults recalled more information relative to older adults, both groups were more accurate in recalling the correct value associated with high value faces compared to lower value faces and remembered gist-information about the values. However, negative values (losses) did not have a strong impact on recall among older adults versus younger adults, illustrating important associative memory differences between younger and older adults.

Regional response differences across the human amygdaloid complex during social conditioning

The amygdala is consistently implicated in biologically relevant learning tasks such as Pavlovian conditioning. In humans, the ability to identify individual faces based on the social outcomes they have predicted in the past constitutes a critical form of associative learning that can be likened to “social conditioning.” To capture such learning in a laboratory setting, participants learned about faces that predicted negative, positive, or neutral social outcomes. Participants reported liking or disliking the faces in accordance with their learned social value. During acquisition, we observed differential functional magnetic resonance imaging activation across the human amygdaloid complex consistent with previous lesion, electrophysiological, and functional neuroimaging data. A region of the medial ventral amygdala and a region of the dorsal amygdala/substantia innominata showed signal increases to both Negative and Positive faces, whereas a lateral ventral region displayed a linear representation of the valence of faces such that Negative > Positive > Neutral. This lateral ventral locus also differed from the dorsal and medial loci in that the magnitude of these responses was more resistant to habituation. These findings document a role for the human amygdala in social learning and reveal coarse regional dissociations in amygdala activity that are consistent with previous human and nonhuman animal data.

Apparent digestibility and nitrogen utilisation of diets based on maize and grass silage fed to beef steers

Substituting grass silage with maize silage in forage mixtures may result in one forage influencing the nutritive value of another in terms of whole tract nutrient digestibility and N utilisation. This experiment investigated effects of four forage combinations being, grass silage (G); 67 g/100 g grass silage + 33 g/100 g maize silage (GGM); 67 g/100 g maize silage + 33 g/100 g grass silage (MMG); maize silage (M). All diets were formulated to be isonitrogenous (22.4 g N/kg dry matter [DM]) using a concentrate mixture. Ration digestibility and N balance was determined using 7 Holstein Friesian steers (mean body weight 411.0 +/- 120.9 kg) in a cross-over design. Inclusion of maize silage in the diet had a positive linear effect on forage and total DM intake (P = 0.001), and on apparent DM and organic matter digestibility (both P = 0.048). Regardless of the silage ratio used, the metabolisable energy concentration of maize silage was calculated to be higher than that of grass silage (P = 0.058), and linearly related to the relative proportions of the two silages in the forage mixture. Inclusion of maize silage in the diet resulted in a linear decline in the apparent digestibility of starch (P = 0.022), neutral detergent fibre (P < 0.001) and acid detergent fibre (P = 0.003). Nitrogen retention, expressed as amount retained per day or in terms of body weight (g/100 kg) increased linearly with maize inclusion (P = 0.047 and 0.046, respectively). Replacing grass silage with maize silage caused linear responses according to the proportions of each forage in the diet, and that there were no associative effects of combining forages. (C) 2004 Elsevier B.V. All rights reserved.

The cognitive neurochemistry of nicotine

The rational for this review is to provide a coherent formulation of the cognitive neurochemistry of nicotine, with the aim of suggesting research and clinical applications. The first part is a comprehensive review of the empirical studies of the enhancing effects of nicotine on information processing, especially those on attentional and mnemonic processing. Then, these studies are put in the context of recent studies on the neurochemistry of nicotine and cholinergic drugs, in general. They suggest a positive effect of nicotine on processes acting on encoded material during the post acquisition phase, the process of consolidation. Thus, the involvement of nicotinic receptors in mnemonic processing is modulation of the excitability of neurons in the hippocampal formation to enable associative processing.