Connectivity in real and evolved associative memories

Peer reviewe

Reevaluating evaluative conditioning: A nonassociative explanation of conditioning effects in the visual evaluative conditioning paradigm

In 2 studies, the authors investigated whether evaluative conditioning (EC) is an associative phenomenon. Experiment 1 compared a standard EC paradigm with nonpaired and no-treatment control conditions. EC effects were obtained only when the conditioned stimulus (CS) and unconditioned stimulus (UCS) were rated as perceptually similar. However, similar EC effects were obtained in both control groups. An earlier failure to obtain EC effects was reanalyzed in Experiment 2. Conditioning-like effects were found when comparing a CS with the most perceptually similar UCSs used in the procedure but not when analyzing a CS rating with respect to the UCS with which it was paired during conditioning. The implications are that EC effects found in many studies are not due to associative learning and that the special characteristics of EC (conditioning without awareness and resistance to extinction) are probably nonassociative artifacts of the EC paradigm

Why will rat's go where rats will not

Experimental evidence indicates that regular plurals are nearly always omitted from English compounds (e.g., rats-eater) while irregular plurals may be included within these structures (e.g., mice-chaser). This phenomenon is considered to be good evidence to support the dual mechanism model of morphological processing (Pinker & Prince, 1992). However, evidence from neural net modelling has shown that a single route associative memory based account might provide an equally, if not more, valid explanation of the compounding phenomenon

Plural morphology in compounding is not good evidence to support the dual mechanism model

The compounding phenomena is considered to be good evidence to support the dual mechanism model of morphological processing (Pinker & Prince, 1992). However evidence from initial neural net modeling has shown that a single route associative memory based account might provide an equally, if not more valid explanation of the treatment of plurals in compounds. Further neural net modeling and empirical work is proposed to test this single route accoun

High capacity associative memory with bipolar and binary, biased patterns

The high capacity associative memory model is interesting due to its significantly higher capacity when compared with the standard Hopfield model. These networks can use either bipolar or binary patterns, which may also be biased. This paper investigates the performance of a high capacity associative memory model trained with biased patterns, using either bipolar or binary representations. Our results indicate that the binary network performs less well under low bias, but better in other situations, compared with the bipolar network.Peer reviewe

Connection Strategies in Associative Memory Models

“The original publication is available at www.springerlink.com”. Copyright Springer.The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks.Peer reviewe

Inhabiting the good city: The politics of hate and the urbanisms of hope

This is the authors' Word version of a book chapter published by Continuum © 2011.This book chapter discusses the involvement of the Church of England in 'disconected' urban areas

Special features of RAD Sequencing data:implications for genotyping

Restriction site-associated DNA Sequencing (RAD-Seq) is an economical and efficient method for SNP discovery and genotyping. As with other sequencing-by-synthesis methods, RAD-Seq produces stochastic count data and requires sensitive analysis to develop or genotype markers accurately. We show that there are several sources of bias specific to RAD-Seq that are not explicitly addressed by current genotyping tools, namely restriction fragment bias, restriction site heterozygosity and PCR GC content bias. We explore the performance of existing analysis tools given these biases and discuss approaches to limiting or handling biases in RAD-Seq data. While these biases need to be taken seriously, we believe RAD loci affected by them can be excluded or processed with relative ease in most cases and that most RAD loci will be accurately genotyped by existing tools