Perceptual learning, roving and the unsupervised bias

Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when . certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervised neural network models can cope easily with roving conditions. What makes humans so inferior compared to these models? As experimental and conceptual work has shown, human perceptual learning is neither supervised nor unsupervised but reward-based learning. Reward-based learning suffers from the so-called unsupervised bias, i.e., to prevent synaptic " drift" , the . average reward has to be exactly estimated. However, this is impossible when two or more stimulus types with different rewards are presented during training (and the reward is estimated by a running average). For this reason, we propose no learning occurs in roving conditions. However, roving hinders perceptual learning only for combinations of similar stimulus types but not for dissimilar ones. In this latter case, we propose that a critic can estimate the reward for each stimulus type separately. One implication of our analysis is that the critic cannot be located in the visual system. © 2011 Elsevier Ltd.

An examination of strain space versus stress space for the formulation of elastoplastic constitutive models

Several elastoplastic soil models have been proposed over the years that are formulated in strain space rather than stress space due to certain analytical and computational advantages. One such model, BRICK (Simpson 1992), has been continuously utilized and developed for industrial applications within Arup Geotechnics for more than two decades. This paper aims to describe the advantages and difficulties associated with strain space modeling. In addition, it will show how recent advances in modeling the effects of stress history, stiffness anisotropy, strength anisotropy and time-dependence in conventional stress space models can be transferred to the BRICK model. © 2010 Taylor & Francis Group, London.

Is titin a 'winding filament'? A new twist on muscle contraction.

Recent studies have demonstrated a role for the elastic protein titin in active muscle, but the mechanisms by which titin plays this role remain to be elucidated. In active muscle, Ca(2+)-binding has been shown to increase titin stiffness, but the observed increase is too small to explain the increased stiffness of parallel elastic elements upon muscle activation. We propose a 'winding filament' mechanism for titin's role in active muscle. First, we hypothesize that Ca(2+)-dependent binding of titin's N2A region to thin filaments increases titin stiffness by preventing low-force straightening of proximal immunoglobulin domains that occurs during passive stretch. This mechanism explains the difference in length dependence of force between skeletal myofibrils and cardiac myocytes. Second, we hypothesize that cross-bridges serve not only as motors that pull thin filaments towards the M-line, but also as rotors that wind titin on the thin filaments, storing elastic potential energy in PEVK during force development and active stretch. Energy stored during force development can be recovered during active shortening. The winding filament hypothesis accounts for force enhancement during stretch and force depression during shortening, and provides testable predictions that will encourage new directions for research on mechanisms of muscle contraction.

Numerical simulation of post-flame oxidation of hydrocarbons in spark ignition engines

About 50-90 percent of the hydrocarbons that escape combustion during flame passage in spark-ignition engine operation are oxidized in the cylinder before leaving the system. The process involves the transport of unreacted fuel from cold walls towards the hotter burned gas regions and subsequent reaction. In order to understand controlling factors in the process, a transient one-dimensional reactive-diffusive model has been formulated for simulating the oxidation processes taking place in the reactive layer between hot burned gases and cold unreacted air/fuel mixture, with initial and boundary conditions provided by the emergence of hydrocarbons from the piston top land crevice. Energy and species conservation equations are solved for the entire process, using a detailed chemical kinetic mechanism for propane. Simulation results show that the post-flame oxidation process takes place within a reactive layer where intermediate hydrocarbon products are formed at temperatures above 1100-1200 K, followed by a carbon monoxide conversion region closer to the hot burned gases. Model results show that most of hydrocarbons leaving the crevice are completely oxidized inside the cylinder. The largest contribution of remaining hydrocarbons are those leaving the crevice at temperatures below 1400 K. The largest fraction of non-fuel (intermediate) hydrocarbons results from hydrocarbons leaving the crevice when core temperatures are around 1400 K Copyright © 1997 Society of Automotive Engineers, Inc.

Encoding of mixtures in a simple olfactory system.

Natural odors are usually mixtures; yet, humans and animals can experience them as unitary percepts. Olfaction also enables stimulus categorization and generalization. We studied how these computations are performed with the responses of 168 locust antennal lobe projection neurons (PNs) to varying mixtures of two monomolecular odors, and of 174 PNs and 209 mushroom body Kenyon cells (KCs) to mixtures of up to eight monomolecular odors. Single-PN responses showed strong hypoadditivity and population trajectories clustered by odor concentration and mixture similarity. KC responses were much sparser on average than those of PNs and often signaled the presence of single components in mixtures. Linear classifiers could read out the responses of both populations in single time bins to perform odor identification, categorization, and generalization. Our results suggest that odor representations in the mushroom body may result from competing optimization constraints to facilitate memorization (sparseness) while enabling identification, classification, and generalization.

Encoding of mixtures in a simple olfactory system

Natural odors are usually mixtures; yet, humans and animals can experience them as unitary percepts. Olfaction also enables stimulus categorization and generalization. We studied how these computations are performed with the responses of 168 locust antennal lobe projection neurons (PNs) to varying mixtures of two monomolecular odors, and of 174 PNs and 209 mushroom body Kenyon cells (KCs) to mixtures of up to eight monomolecular odors. Single-PN responses showed strong hypoadditivity and population trajectories clustered by odor concentration and mixture similarity. KC responses were much sparser on average than those of PNs and often signaled the presence of single components in mixtures. Linear classifiers could read out the responses of both populations in single time bins to perform odor identification, categorization, and generalization. Our results suggest that odor representations in the mushroom body may result from competing optimization constraints to facilitate memorization (sparseness) while enabling identification, classification, and generalization