Quantum-inspired feature and parameter optimization of evolving spiking neural networks with a case study from ecological modelling

The paper introduces a framework and implementation of an integrated connectionist system, where the features and the parameters of an evolving spiking neural network are optimised together using a quantum representation of the features and a quantum inspired evolutionary algorithm for optimisation. The proposed model is applied on ecological data modeling problem demonstrating a significantly better classification accuracy than traditional neural network approaches and a more appropriate feature subset selected from a larger initial number of features. Results are compared to a naive Bayesian classifier

Comparing the E-Z Reader Model to Other Models of Eye Movement Control in Reading

The E-Z Reader model provides a theoretical framework for understanding how word identification, visual processing, attention, and oculomotor control jointly determine when and where the eyes move during reading. Thus, in contrast to other reading models reviewed in this article, E-Z Reader can simultaneously account for many of the known effects of linguistic, visual, and oculomotor factors on eye movement control during reading. Furthermore, the core principles of the model have been generalized to other task domains (e.g., equation solving, visual search), and are broadly consistent with what is known about the architecture of the neural systems that support reading

Empirical Lessons for Philosophical Theories of Mental Content

This thesis concerns the content of mental representations. It draws lessons for philosophical theories of content from some empirical findings about brains and behaviour drawn from experimental psychology (cognitive, developmental, comparative), cognitive neuroscience and cognitive science (computational modelling). Chapter 1 motivates a naturalist and realist approach to mental representation. Chapter 2 sets out and defends a theory of content for static feedforward connectionist networks, and explains how the theory can be extended to other supervised networks. The theory takes forward Churchland’s state space semantics by making a new and clearer proposal about the syntax of connectionist networks − one which nicely accounts for representational development. Chapter 3 argues that the same theoretical approach can be extended to unsupervised connectionist networks, and to some of the representational systems found in real brains. The approach can also show why connectionist systems sometimes show typicality effects, explaining them without relying upon prototype structure. That is discussed in chapter 4, which also argues that prototype structure, where it does exist, does not determine content. The thesis goes on to defend some unorthodox features of the foregoing theory: that a role is assigned to external samples in specifying syntax, that both inputs to and outputs from the system have a role in determining content, and that the content of a representation is partly determined by the circumstances in which it developed. Each, it is argued, may also be a fruitful way of thinking about mental content more generally. Reliance on developmental factors prompts a swampman-type objection. This is rebutted by reference to three possible reasons why content is attributed at all. Two of these motivations support the idea that content is partly determined by historical factors, and the third is consistent with it. The result: some empirical lessons for philosophical theories of mental content.Philosophy of Min

Learning the Thematic Roles of Words in Sentences via Connectionist Networks that Satisfy Strong Systematicity

This thesis presents two connectionist models, which can learn the thematic roles of words in sentences by receiving aspects of real world situations to which the sentences are referring, and exhibit strong systematicity without prior syntactic knowledge. The models are intended towards cognitively and biologically plausible connectionist models. Current models could be parts of the larger network to represent the meaning of a whole sentence. The first model, closest, of the two models, to being purely connectionist, attains an acceptable result (98.31% of the roles correctly identified). The second one, not purely connectionist, achieves a perfect result. It could be argued that humans learn the thematic roles, as an emergent property of learning the relationship between the words/sentences and the real world situations. However, it is not claimed that the models are the human learning mechanism for language acquisition