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Extracting semantic representations from word co-occurrence statistics: A computational study
TLDR
This article presents a systematic exploration of the principal computational possibilities for formulating and validating representations of word meanings from word co-occurrence statistics and finds that, once the best procedures are identified, a very simple approach is surprisingly successful and robust over a range of psychologically relevant evaluation measures.
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Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios
TLDR
It is concluded that modulatory neurons evolve autonomously in the proposed learning tasks, allowing for increased learning and memory capabilities.
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Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVD
TLDR
This article investigates the use of three further factors—namely, the application of stop-lists, word stemming, and dimensionality reduction using singular value decomposition (SVD)—that have been used to provide improved performance elsewhere and introduces an additional semantic task and explores the advantages of using a much larger corpus.
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An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows
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Understanding the Emergence of Modularity in Neural Systems
TLDR
A pattern is established which provides one explanation of why modularity should emerge reliably across a range of neural processing tasks, and the importance of taking into account known physical brain constraints, such as the degree of neural connectivity.
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Explorations in the derivation of word co-occurrence statistics
TLDR
This work has studied aspects of this kind of methodology by systematically examining the effects of different combinations of parameters used in the preparation of co-occurrence statistics by systematically analyzing the results of several psychologically relevant evaluation measures.
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Lifetime Learning as a Factor in Life History Evolution
TLDR
It is concluded that longer periods of parental protection of children do offer clear learning advantages and better adult performance, but only if procreation is not allowed during the protection period.
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Connectionist modelling: Implications for cognitive neuropsychology
Abstract We review here the logic of neuropsychological inference in the context of connectionist modelling, focusing on the inference from double dissociation to modularity of function. The results…
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Representation , Learning , Generalization and Damage in Neural Network Models of Reading Aloud
TLDR
A new class of neural network models of reading aloud based on Sejnowski & Rosenberg’s NETtalk, which are not restricted to mono-syllabic words, require no complicated inputoutput representations and require no preprocessing to align the letters and phonemes in the training data.
The Evolution of Minimal Catastrophic Forgetting in Neural Systems
TLDR
This paper aims to show how simulated evolution can be used to generate neural network models with significantly less catastrophic forgetting than traditionally formulated models, and presents a series of simulation results.
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