A Survey of Brain Inspired Technologies for Engineering

Cognitive engineering is a multi-disciplinary field and hence it is difficult to find a review article consolidating the leading developments in the field. The in-credible pace at which technology is advancing pushes the boundaries of what is achievable in cognitive engineering. There are also differing approaches to cognitive engineering brought about from the multi-disciplinary nature of the field and the vastness of possible applications. Thus research communities require more frequent reviews to keep up to date with the latest trends. In this paper we shall dis-cuss some of the approaches to cognitive engineering holistically to clarify the reasoning behind the different approaches and to highlight their strengths and weaknesses. We shall then show how developments from seemingly disjointed views could be integrated to achieve the same goal of creating cognitive machines. By reviewing the major contributions in the different fields and showing the potential for a combined approach, this work intends to assist the research community in devising more unified methods and techniques for developing cognitive machines

The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling

Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the �experimenter�, and Mary, the �computational modeller�. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling

Filling the Gaps: Hume and Connectionism on the Continued Existence of Unperceived Objects

In Book I, part iv, section 2 of the Treatise, "Of scepticism with regard to the senses," Hume presents two different answers to the question of how we come to believe in the continued existence of unperceived objects. He rejects his first answer shortly after its formulation, and the remainder of the section articulates an alternative account of the development of the belief. The account that Hume adopts, however, is susceptible to a number of insurmountable objections, which motivates a reassessment of his original proposal. This paper defends a version of Hume's initial explanation of the belief in continued existence and examines some of its philosophical implications

How nouns and verbs differentially affect the behavior of artificial organisms

This paper presents an Artificial Life and Neural Network (ALNN) model for the evolution of syntax. The simulation methodology provides a unifying approach for the study of the evolution of language and its interaction with other behavioral and neural factors. The model uses an object manipulation task to simulate the evolution of language based on a simple verb-noun rule. The analyses of results focus on the interaction between language and other non-linguistic abilities, and on the neural control of linguistic abilities. The model shows that the beneficial effects of language on non-linguistic behavior are explained by the emergence of distinct internal representation patterns for the processing of verbs and nouns

A Transfer of Sequence Function Via Equivalence in a Connectionist Network

Connectionist networks may provide useful models of stimulus equivalence and transfer of function phenomena. Such models have been applied to a range of behavioral tasks and have demonstrated transfers of function via equivalence relations following appropriate training, with networks accurately simulating the behavior of human subjects. In the current study, a connectionist network was pretrained on a series of equivalence and sequence tasks to simulate the preexperimental experience of an adult subject. It was then exposed to the equivalent of six conditional discriminations, and was tested for the formation of three 3-member equivalence classes (corresponding to A 1-A2-A3, B1-B2-B3, C1-C2-C3). It was subsequently trained to produce a pair of four part sequences (corresponding to B1 -+B2-+Ct1 -+B3 and B3-+ B2-+Ct2-+ B1 , where Ct1 and Ct2 represented contextual cues) before being tested for transfer, through equivalence, of the sequence responses to the C stimuli. Following appropriate pretraining, the network showed the formation of three equivalence classes and a transfer of sequence function to the nontrained C stimuli (producing the novel sequences C1 -+C2-. Ct1 -+C3 and C3-+C2-+Ct2-+C1) . A control network, which was not exposed to conditional discrimination training, failed to demonstrate equivalence and the transfer of sequence function, as predicted by findings from experimental demonstrations with human participants. Network performance was analyzed as a function of amount of pretraining and a number of psychologically plausible training methods are presented. The data suggest that connectionist networks may provide accurate and plausible models of stimulus equivalence and transfer of function phenomena in natural language

A feedback model of visual attention

Feedback connections are a prominent feature of cortical anatomy and are likely to have significant functional role in neural information processing. We present a neural network model of cortical feedback that successfully simulates neurophysiological data associated with attention. In this domain our model can be considered a more detailed, and biologically plausible, implementation of the biased competition model of attention. However, our model is more general as it can also explain a variety of other top-down processes in vision, such as figure/ground segmentation and contextual cueing. This model thus suggests that a common mechanism, involving cortical feedback pathways, is responsible for a range of phenomena and provides a unified account of currently disparate areas of research