Risk-sensitive optimal feedback control accounts for sensorimotor behavior under uncertainty

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models. © 2010 Nagengast et al.

Phylogenetic analyses of Fringillidae (Aves : Passeriformes) using mitochondrial tRNA gene sequences and secondary structure

Fringillidae is a large and diverse family of Passeriformes. So far, however, Fringillidae relationships deduced from morphological features and by a number of molecular approaches have remained unproven. Recently, much attention has been attracted to mitochondrial tRNA genes, whose sequence and secondary structural characteristics have shown to be useful for Acrodont Lizards and deep-branch phylogenetic studies. In order to identify useful phylogenetic markers and test Fringillidae relationships, we have sequenced three major clusters of mitochondrial tRNA genes from 15 Fringillidae, taxa. A coincident tree, with coturnix as outgroup, was obtained through Maximum-likelihood method using combined dataset of 11 mitochondrial tRNA gene sequences. The result was similar to that through Neighbor-joining but different from Maximum-parsimony methods. Phylogenetic trees constructed with stem-region sequences of 11 genes had many different topologies and lower confidence than with total sequences. On the other hand, some secondary structural characteristics may provide phylogenetic information on relatively short internal branches at under-genus level. In summary, our data indicate that mitochondrial tRNA genes can achieve high confidence on Fringillidae phylogeny at subfamily level, and stem-region sequences may be suitable only at above-family level. Secondary structural characteristics may also be useful to resolve phylogenetic relationship between different genera of Fringillidae with good performance.

A molecular model of braid-like DNA structure

The three-dimensional molecular models of DNA triple helices and triple-stranded brain-like structure were built up by molecular architecture, and their structural features and energy decomposition were examined. The results showed: (i) The base triplet is the element forming braid-like and triple helix DNA; (ii) Under specified conditions, DNA could form the triplet-stranded braid-like structure; (iii) DNA stability of the braid-like structure is less than that of the triple helix structure. (C) 1995 Academic Press Limited.

Comparative studies on parallel and antiparallel duplex and triplex DNA

Parallel strand models for base sequences d(A)(10). d(T)(10), d(AT)(5) . d(TA)(5), d(G(5)C(5)). d(C(5)G(5)), d(GC)(5) . d(CG)(5) and d(CTATAGGGAT). d(GATATCCCTA), where reverse Watson-Crick A-T pairing with two H-bonds and reverse Watson-Crick G-C pairing with one H-bond or with two H-bonds were adopted, and three models of d(T)(14). d(A)(14). d(T)(14) triple helix with different strand orientations were built up by molecular architecture and energy minimization. Comparisons of parallel duplex models with their corresponding B-DNA models and comparisons among the three triple helices showed: (i) conformational energies of parallel AT duplex models were a little lower, while for GC duplex models they were about 8% higher than that of their corresponding B-DNA models; (ii) the energy differences between parallel and B-type duplex models and among the three triple helices arose mainly from base stacking energies, especially for GC base pairing; (iii) the parallel duplexes with one H-bond G-C pairs were less stable than those with two H-bonds G-C pairs. The present paper includes a brief discussion about the effect of base stacking and base sequences on DNA conformations. (C) 1997 Academic Press Limited.

SNPNB: analyzing neighboring-nucleotide biases on single nucleotide polymorphisms (SNPs)

SNPNB is a user-friendly and platform-independent application for analyzing Single Nucleotide Polymorphism NeighBoring sequence context and nucleotide bias patterns, and subsequently evaluating the effective SNP size for the bias patterns observed from the whole data. It was implemented by Java and Perl. SNPNB can efficiently handle genome-wide or chromosome-wide SNP data analysis in a PC or a workstation. It provides visualizations of the bias patterns for SNPs or each type of SNPs.

Paradoxical evidence integration in rapid decision processes

Decisions about noisy stimuli require evidence integration over time. Traditionally, evidence integration and decision making are described as a one-stage process: a decision is made when evidence for the presence of a stimulus crosses a threshold. Here, we show that one-stage models cannot explain psychophysical experiments on feature fusion, where two visual stimuli are presented in rapid succession. Paradoxically, the second stimulus biases decisions more strongly than the first one, contrary to predictions of one-stage models and intuition. We present a two-stage model where sensory information is integrated and buffered before it is fed into a drift diffusion process. The model is tested in a series of psychophysical experiments and explains both accuracy and reaction time distributions. © 2012 Rüter et al.

A Structured Model of Video Reproduces Primary Visual Cortical Organisation

The visual system must learn to infer the presence of objects and features in the world from the images it encounters, and as such it must, either implicitly or explicitly, model the way these elements interact to create the image. Do the response properties of cells in the mammalian visual system reflect this constraint? To address this question, we constructed a probabilistic model in which the identity and attributes of simple visual elements were represented explicitly and learnt the parameters of this model from unparsed, natural video sequences. After learning, the behaviour and grouping of variables in the probabilistic model corresponded closely to functional and anatomical properties of simple and complex cells in the primary visual cortex (V1). In particular, feature identity variables were activated in a way that resembled the activity of complex cells, while feature attribute variables responded much like simple cells. Furthermore, the grouping of the attributes within the model closely parallelled the reported anatomical grouping of simple cells in cat V1. Thus, this generative model makes explicit an interpretation of complex and simple cells as elements in the segmentation of a visual scene into basic independent features, along with a parametrisation of their moment-by-moment appearances. We speculate that such a segmentation may form the initial stage of a hierarchical system that progressively separates the identity and appearance of more articulated visual elements, culminating in view-invariant object recognition.

A Balance Equation Determines a Switch in Neuronal Excitability

We use the qualitative insight of a planar neuronal phase portrait to detect an excitability switch in arbitrary conductance-based models from a simple mathematical condition. The condition expresses a balance between ion channels that provide a negative feedback at resting potential (restorative channels) and those that provide a positive feedback at resting potential (regenerative channels). Geometrically, the condition imposes a transcritical bifurcation that rules the switch of excitability through the variation of a single physiological parameter. Our analysis of six different published conductance based models always finds the transcritical bifurcation and the associated switch in excitability, which suggests that the mathematical predictions have a physiological relevance and that a same regulatory mechanism is potentially involved in the excitability and signaling of many neurons. © 2013 Franci et al.

How modeling can reconcile apparently discrepant experimental results: The case of pacemaking in dopaminergic neurons

Midbrain dopaminergic neurons are endowed with endogenous slow pacemaking properties. In recent years, many different groups have studied the basis for this phenomenon, often with conflicting conclusions. In particular, the role of a slowly-inactivating L-type calcium channel in the depolarizing phase between spikes is controversial, and the analysis of slow oscillatory potential (SOP) recordings during the blockade of sodium channels has led to conflicting conclusions. Based on a minimal model of a dopaminergic neuron, our analysis suggests that the same experimental protocol may lead to drastically different observations in almost identical neurons. For example, complete L-type calcium channel blockade eliminates spontaneous firing or has almost no effect in two neurons differing by less than 1% in their maximal sodium conductance. The same prediction can be reproduced in a state of the art detailed model of a dopaminergic neuron. Some of these predictions are confirmed experimentally using single-cell recordings in brain slices. Our minimal model exhibits SOPs when sodium channels are blocked, these SOPs being uncorrelated with the spiking activity, as has been shown experimentally. We also show that block of a specific conductance (in this case, the SK conductance) can have a different effect on these two oscillatory behaviors (pacemaking and SOPs), despite the fact that they have the same initiating mechanism. These results highlight the fact that computational approaches, besides their well known confirmatory and predictive interests in neurophysiology, may also be useful to resolve apparent discrepancies between experimental results. © 2011 Drion et al.

Quantitative structure-property relationships for octanol-air partition coefficients of polychlorinated naphthalenes, chlorobenzenes and p,p '-DDT

The octanol-air partition coefficient (K-OA) is a key descriptor of chemicals partitioning between the atmosphere and environmental organic phases. Quantitative structure-property relationships (QSPR) are necessary to model and predict KOA from molecular structures. Based on 12 quantum chemical descriptors computed by the PM3 Hamiltonian, using partial least squares (PLS) analysis, a QSPR model for logarithms of K-OA to base 10 (log K-OA) for polychlorinated naphthalenes (PCNs), chlorobenzenes and p,p'-DDT was obtained. The cross-validated Q(cum)(2) value of the model is 0.973, indicating a good predictive ability of the model. The main factors governing log K-OA of the PCNs, chlorobenzenes, and p,p'-DDT are, in order of decreasing importance, molecular size and molecular ability of donating/accepting electrons to participate in intermolecular interactions. The intermolecular dispersive interactions play a leading role in governing log K-OA. The more chlorines in PCN and chlorobenzene molecules, the greater the log K-OA values. Increasing E-LUMO (the energy of the lowest unoccupied molecular orbital) of the molecules leads to decreasing log K-OA values, implying possible intermolecular interactions between the molecules under study and octanol molecules. (C) 2002 Elsevier Science Ltd. All rights reserved.

Substitution Matrices of Residue Triplets Derived from Protein Blocks.

In protein sequence alignment, residue similarity is usually evaluated by substitution matrix, which scores all possible exchanges of one amino acid with another. Several matrices are widely used in sequence alignment, including PAM matrices derived from homologous sequence and BLOSUM matrices derived from aligned segments of BLOCKS. However, most matrices have not addressed the high-order residue-residue interactions that are vital to the bioproperties of protein.With consideration for the inherent correlation in residue triplet, we present a new scoring scheme for sequence alignment. Protein sequence is treated as overlapping and successive 3-residue segments. Two edge residues of a triplet are clustered into hydrophobic or polar categories, respectively. Protein sequence is then rewritten into triplet sequence with 2 · 20 · 2 = 80 alphabets. Using a traditional approach, we construct a new scoring scheme named TLESUMhp (TripLEt SUbstitution Matrices with hydropobic and polar information) for pairwise substitution of triplets, which characterizes the similarity of residue triplets. The applications of this matrix led to marked improvements in multiple sequence alignment and in searching structurally alike residue segments. The reason for the occurrence of the ‘‘twilight zone,’’ i.e., structure explosion of lowidentity sequences, is also discussed.

Potential energy landscape and robustness of a gene regulatory network: Toggle switch

Finding a multidimensional potential landscape is the key for addressing important global issues, such as the robustness of cellular networks. We have uncovered the underlying potential energy landscape of a simple gene regulatory network: a toggle switch. This was realized by explicitly constructing the steady state probability of the gene switch in the protein concentration space in the presence of the intrinsic statistical fluctuations due to the small number of proteins in the cell. We explored the global phase space for the system. We found that the protein synthesis rate and the unbinding rate of proteins to the gene were small relative to the protein degradation rate; the gene switch is monostable with only one stable basin of attraction. When both the protein synthesis rate and the unbinding rate of proteins to the gene are large compared with the protein degradation rate, two global basins of attraction emerge for a toggle switch. These basins correspond to the biologically stable functional states. The potential energy barrier between the two basins determines the time scale of conversion from one to the other. We found as the protein synthesis rate and protein unbinding rate to the gene relative to the protein degradation rate became larger, the potential energy barrier became larger. This also corresponded to systems with less noise or the fluctuations on the protein numbers.