37,730 research outputs found
An emergentist perspective on the origin of number sense
open2noopenZorzi, Marco; Testolin, AlbertoZorzi, Marco; Testolin, Albert
A mathematical theory of semantic development in deep neural networks
An extensive body of empirical research has revealed remarkable regularities
in the acquisition, organization, deployment, and neural representation of
human semantic knowledge, thereby raising a fundamental conceptual question:
what are the theoretical principles governing the ability of neural networks to
acquire, organize, and deploy abstract knowledge by integrating across many
individual experiences? We address this question by mathematically analyzing
the nonlinear dynamics of learning in deep linear networks. We find exact
solutions to this learning dynamics that yield a conceptual explanation for the
prevalence of many disparate phenomena in semantic cognition, including the
hierarchical differentiation of concepts through rapid developmental
transitions, the ubiquity of semantic illusions between such transitions, the
emergence of item typicality and category coherence as factors controlling the
speed of semantic processing, changing patterns of inductive projection over
development, and the conservation of semantic similarity in neural
representations across species. Thus, surprisingly, our simple neural model
qualitatively recapitulates many diverse regularities underlying semantic
development, while providing analytic insight into how the statistical
structure of an environment can interact with nonlinear deep learning dynamics
to give rise to these regularities
Hierarchical Temporal Representation in Linear Reservoir Computing
Recently, studies on deep Reservoir Computing (RC) highlighted the role of
layering in deep recurrent neural networks (RNNs). In this paper, the use of
linear recurrent units allows us to bring more evidence on the intrinsic
hierarchical temporal representation in deep RNNs through frequency analysis
applied to the state signals. The potentiality of our approach is assessed on
the class of Multiple Superimposed Oscillator tasks. Furthermore, our
investigation provides useful insights to open a discussion on the main aspects
that characterize the deep learning framework in the temporal domain.Comment: This is a pre-print of the paper submitted to the 27th Italian
Workshop on Neural Networks, WIRN 201
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