Experience-driven formation of parts-based representations in a model of layered visual memory

Growing neuropsychological and neurophysiological evidence suggests that the visual cortex uses parts-based representations to encode, store and retrieve relevant objects. In such a scheme, objects are represented as a set of spatially distributed local features, or parts, arranged in stereotypical fashion. To encode the local appearance and to represent the relations between the constituent parts, there has to be an appropriate memory structure formed by previous experience with visual objects. Here, we propose a model how a hierarchical memory structure supporting efficient storage and rapid recall of parts-based representations can be established by an experience-driven process of self-organization. The process is based on the collaboration of slow bidirectional synaptic plasticity and homeostatic unit activity regulation, both running at the top of fast activity dynamics with winner-take-all character modulated by an oscillatory rhythm. These neural mechanisms lay down the basis for cooperation and competition between the distributed units and their synaptic connections. Choosing human face recognition as a test task, we show that, under the condition of open-ended, unsupervised incremental learning, the system is able to form memory traces for individual faces in a parts-based fashion. On a lower memory layer the synaptic structure is developed to represent local facial features and their interrelations, while the identities of different persons are captured explicitly on a higher layer. An additional property of the resulting representations is the sparseness of both the activity during the recall and the synaptic patterns comprising the memory traces.Comment: 34 pages, 12 Figures, 1 Table, published in Frontiers in Computational Neuroscience (Special Issue on Complex Systems Science and Brain Dynamics), http://www.frontiersin.org/neuroscience/computationalneuroscience/paper/10.3389/neuro.10/015.2009

Neuroeducation: Learning, Arts, and the Brain

Excerpts presentations and discussions from a May 2009 conference on the intersection of cognitive neuroscience, the arts, and learning -- the effects of early arts education on other aspects of cognition and implications for policy and practice

Estimating the potential impact of nonvoters on outcomes of parlimentary elections in proportional systems with the applications to German national elections from 1949 to 2005

"If [voter] turnout was 100%, would it affect the election result?" (Bernhagen and Marsh 2007) is a frequently asked research question. So far, the question has been primarily answered regarding the changes in the distribution of votes. This article extends the analysis to changes in the distribution of seats and government formation. It proposes a method that factors in apportionment methods, barring clauses, size of parliaments, leverage of nonvoters, closeness of election results, and individual characteristics of nonvoters. The method is then applied to German national elections from 1949 to 2005. The application shows that Germany's Social Democratic Party (SPD) would have gained from the counterfactual participation of nonvoters, although usually not enough to result in a government change. However by the 1994 and 2005 elections evidence shows that such a government change could have happened. --

Changes in the Competitive Position of the Czech Republic, Hungary and Poland in the EU Market

This paper aims at comparing the uneven process of changes in competitiveness among three accession countries' manufacturing industries, the Czech Republic, Hungary and Poland, during the period prior to their EU membership (1996-2003). It demonstrates that the three countries improved competitiveness in the majority of their manufacturing industries. However, these changes were differentiated across time, among industries, in terms of the quality of segments and between the three countries overall. A drop in the productivity gap between the manufacturing industries of the three accession and the incumbent EU countries played the major role in improvement in competitiveness. It determined the drop in relative unit labour costs. The paper shows that changes in competitive advantages of a given country's industry reflect changes in relative (as compared to foreign) productivity rather than differences in level and changes in productivity among industries of a given country. The dynamics and levels of productivity among the Czech and Polish larger winners were lower than the manufacturing average of both countries. However, since the improvement in productivity in these industries in both countries was larger than in their incumbent EU counterparts, the former pushed the latter out of the EU market. Poland's and the Czech Republic's export specialisation in less productive industries implies that their export expansion to the EU would result in lower than potential economic growth in both countries. The paper shows that Smith's law of absolute advantages tends to determine changes in market share.competitiveness, productivity, transition economies, manufacturing industry, EU integration

Trade and Growth with Heterogenous Firms

This paper explores the impact of trade on growth when firms are heterogeneous. We find that greater openness produces anti-and pro-growth effects. The Melitz-model selection effects raises the expected cost of introducing a new variety and this tends to slow the rate of new-variety introduction and hence growth. The pro-growth effect stems from the impact that freer trade has on the marginal cost of innovating. The balance of the two effects is ambiguous with the sign depending upon the exact nature of the innovation technology and its connection to international trade in goods and ideas. We consider five special cases (these include the Grossman-Helpman, the Coe-Helpman and Rivera-Batiz-Romer models) two of which suggest that trade harms growth; the others predicting the opposite.

On the application of reservoir computing networks for noisy image recognition

Reservoir Computing Networks (RCNs) are a special type of single layer recurrent neural networks, in which the input and the recurrent connections are randomly generated and only the output weights are trained. Besides the ability to process temporal information, the key points of RCN are easy training and robustness against noise. Recently, we introduced a simple strategy to tune the parameters of RCNs. Evaluation in the domain of noise robust speech recognition proved that this method was effective. The aim of this work is to extend that study to the field of image processing, by showing that the proposed parameter tuning procedure is equally valid in the field of image processing and conforming that RCNs are apt at temporal modeling and are robust with respect to noise. In particular, we investigate the potential of RCNs in achieving competitive performance on the well-known MNIST dataset by following the aforementioned parameter optimizing strategy. Moreover, we achieve good noise robust recognition by utilizing such a network to denoise images and supplying them to a recognizer that is solely trained on clean images. The experiments demonstrate that the proposed RCN-based handwritten digit recognizer achieves an error rate of 0.81 percent on the clean test data of the MNIST benchmark and that the proposed RCN-based denoiser can effectively reduce the error rate on the various types of noise. (c) 2017 Elsevier B.V. All rights reserved