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Over the past three decades, there has been a growing interest in studying consumer behaviour directly through non-traditional, brain-based, approach using the basic knowledge of human neuroscience. This multidisciplinary approach has evolved into a new marketing branch, known as Neuromarketing, which goes inside the human brain to improve our knowledge of consumer behaviour. Neuromarketing traces neural circuit activities inside the brain using Magnetic Resonance Imaging (MRI) technology. This paper explores the existing literature on Neuromarketing to provide insights into the potential for improving our understanding of consumer behaviour. The paper concludes that Neuromarketing can offer a valuable opportunity to increase precision and validity of measuring consumer reactions to marketing activities, thus improve marketing knowledge of consumer choice behaviour. The paper also addresses the main ethical issues raised by critiques on the unprecedented access to consumers’ mind, and how advocates looked at such criticisms.

Coherence resonance describes a phenomenon in excitable systems in which a suitable dose of noise generates excitation-events that maximizes its periodicity or coherence. The Fano-factor, defined as the ratio of the standard deviation of the time-intervals between successive events and the average time interval, exhibits a minimum at this optimal noise level. It is shown here that a decreasing Fano factor is a necessary but not a sufficient criterion to indicate enhanced coherence of a signal.

I propose and defend the Allocentric-Egocentric Interface Theory of Consciousness. Mental processes form a hierarchy of mental representations with maximally egocentric (self-centered) representations at the bottom and maximally allocentric (other-centered) representations at the top. Phenomenally conscious states are states that are relatively intermediate in this hierarchy. More specifically, conscious states are hybrid states that involve the reciprocal interaction between relatively allocentric and relatively egocentric representations. Thus a conscious state is composed of a pair of representations interacting at the Allocentric-Egocentric Interface. What a person is conscious of is determined by what the contributing allocentric and egocentric representations are representations of. The phenomenal character of conscious states is identical to the representational content of the reciprocally interacting egocentric and allocentric representations.

The contributions of art therapy to treatment, assessment, and research with people who have autism spectrum disorders are presented. An overview of art therapy introduces the reader to the applications and benefits of art therapy, which includes the field's contributions to the knowledge base of autism in the areas of treatment, assessment, and research. The ways in which art therapy can ameliorate symptoms of autism in treatment settings such as schools is described, and applications of art therapy for families and caregivers are presented. Art therapy is discussed as a practical means of assessing symptoms of autism, and the Face Stimulus Assessment is described with support of illustrated case examples. Finally, emerging and expanding areas of art therapy and autism research are delineated, with a focus on the promising intersection of neuroscience and art therapy.

This paper obtains the 1-soliton solution by the ansatz method for the proposed model that governs the propagation of solitons through the neurons. This model is an improved one that describes the solitons in neurosciences more accurately. The ansatz method is applied to obtain the 1-soliton solution to the model. The Lie symmetry analysis is subsequently applied to obtain the conservation laws for the model.

Philosophy and AI have had a difficult relationship from the beginning. The “classic” period from 1950 to 2000 saw four major conflicts, first about the logical coherence of AI as an endeavor, and then about architecture, semantics, and the Frame Problem. Since 2000, these early debates have been largely replaced by arguments about consciousness and ethics, arguments that now involve neuroscientists, lawyers, and economists as well as AI scientists and philosophers. We trace these developments, and speculate about the future.

Deep learning is a popular topic among machine learning researchers nowadays, with great strides being made in recent years to develop robust artificial neural networks for faster convergence to a reasonable accuracy. Network architecture and hyperparameters of the model are fundamental aspects of model convergence. One such important parameter is the initial values of weights, also known as weight initialization. In this paper, we perform two research tasks concerned with the weights of neural networks. First, we develop three novel weight initialization algorithms inspired by the neuroscientific construction of the mammalian brains and then test them on benchmark datasets against other algorithms to compare and assess their performance. We call these algorithms the lognormal weight initialization, modified lognormal weight initialization, and skewed weight initialization. We observe from our results that these initialization algorithms provide state-of-the-art results on all of the benchmark datasets. Second, we analyze the influence of training an artificial neural network on its weight distribution by measuring the correlation between the quantitative metrics of skewness and kurtosis against the model accuracy using linear regression for different weight initializations. Results indicate a positive correlation between network accuracy and skewness of the weight distribution but no affirmative relation between accuracy and kurtosis. This analysis provides further insight into understanding the inner mechanism of neural network training using the shape of weight distribution. Overall, the works in this paper are the first of their kind in incorporating neuroscientific knowledge into the domain of artificial neural network weights.

The paper stresses the universal role that Cellular Nonlinear Networks (CNNs) are assuming today. It is shown that the dynamical behavior of 3D CNN-based models allows us to approach new emerging problems, to open new research frontiers as the generation of new geometrical forms and to establish some links between art, neuroscience and dynamical systems.

Weak electrical noise applied in the water around small paddlefish, Polyodon spathula, increases the spatial range over which they can detect and capture planktonic prey (Daphnia), demonstrating stochastic resonance at the level of an animal's feeding behavior. Here we show that optimal-amplitude (~ 0.5 μ V·cm^-1) noise causes a fish to prefer more vertical angles of attack when striking at prey, as revealed in polar graphs. Increased spatial range is also seen in horizontal directions, as outlying shoulders in the probability distribution of horizontal strike distances. High levels of noise increased the distance that approaching prey travelled along the rostrum (an elongated appendage anterior to the head, functioning as an electrosensitive antenna), before the fish first showed a visible fin or body motion in response. There was no significant effect of optimal-amplitude noise on the rate of strikes, although high-amplitude noise reduced the strike rate. The behavioral data were confirmed in neurophysiological experiments demonstrating that stochastic resonance occurs in individual electroreceptors, and in fact occurs at a similar optimal noise level as in behavioral experiments. We conclude that stochastic resonance can be demonstrated in the behavior of animals, and that animals can make use of the increased sensory information available during near-threshold environmental noise.

The development of relational databases has significantly improved the performance of storage, search, and retrieval functions and has made it possible for applications that perform real-time data acquisition and analysis to interact with these types of databases. The purpose of this research was to develop a user interface for interaction between a data acquisition and analysis application and a relational database using the Oracle9i system. The overall system was designed to have an indexing capability that threads into the data acquisition and analysis programs. Tables were designed and relations within the database for indexing the files and information contained within the files were established. The system provides retrieval capabilities over a broad range of media, including analog, event, and video data types. The system's ability to interact with a data capturing program at the time of the experiment to create both multimedia files as well as the meta-data entries in the relational database avoids manual entries in the database and ensures data integrity and completeness for further interaction with the data by analysis applications.

A distinct property of robot vision systems is that they are embodied. Visual information is extracted for the purpose of moving in and interacting with the environment. Thus, different types of perception-action cycles need to be implemented and evaluated.

In this paper, we study the problem of designing a vision system for the purpose of object grasping in everyday environments. This vision system is firstly targeted at the interaction with the world through recognition and grasping of objects and secondly at being an interface for the reasoning and planning module to the real world. The latter provides the vision system with a certain task that drives it and defines a specific context, i.e. search for or identify a certain object and analyze it for potential later manipulation. We deal with cases of: (i) known objects, (ii) objects similar to already known objects, and (iii) unknown objects. The perception-action cycle is connected to the reasoning system based on the idea of affordances. All three cases are also related to the state of the art and the terminology in the neuroscientific area.