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Biomedical subjects

Z Ghahramani

Publications and source records attributed to Z Ghahramani.

15 recordsLinked to original sources
PubMed2005

Bilinear dynamical systems.

In this paper, we propose the use of bilinear dynamical systems (BDS)s for model-based deconvolution of fMRI time-series. The importance of this work lies in being able to deconvolve haemodynamic time-series, in an informed way, to disclose the underlying neuronal activity. Being able to estimate neuronal responses in a particular brain region is fundamental for many models of functional integration and connectivity in the brain. BDSs comprise a stochastic bilinear neurodynamical model specified in discrete time, and a set of linear convolution kernels for the haemodynamics. We derive an expectation-maximization (EM) algorithm for parameter estimation, in which fMRI time-series are deconvolved in an E-step and model parameters are updated in an M-Step. We report preliminary results that focus on the assumed stochastic nature of the neurodynamic model and compare the method to Wiener deconvolution.

Algorithms↗
PubMed2004

The status of structural genomics defined through the analysis of current targets and structures.

Structural genomics--large-scale macromolecular 3-dimenional structure determination--is unique in that major participants report scientific progress on a weekly basis. The target database (TargetDB) maintained by the Protein Data Bank (http://targetdb.pdb.org) reports this progress through the status of each protein sequence (target) under consideration by the major structural genomics centers worldwide. Hence, TargetDB provides a unique opportunity to analyze the potential impact that this major initiative provides to scientists interested in the sequence-structure-function-disease paradigm. Here we report such an analysis with a focus on: (i) temporal characteristics--how is the project doing and what can we expect in the future? (ii) target characteristics--what are the predicted functions of the proteins targeted by structural genomics and how biased is the target set when compared to the PDB and to predictions across complete genomes? (iii) structures solved--what are the characteristics of structures solved thus far and what do they contribute? The analysis required a more extensive database of structure predictions using different methods integrated with data from other sources. This database, associated tools and related data sources are available from http://spam.sdsc.edu.

Computational Biology↗
PubMed2004

Clustering protein sequence and structure space with infinite Gaussian mixture models.

We describe a novel approach to the problem of automatically clustering protein sequences and discovering protein families, subfamilies etc., based on the theory of infinite Gaussian mixtures models. This method allows the data itself to dictate how many mixture components are required to model it, and provides a measure of the probability that two proteins belong to the same cluster. We illustrate our methods with application to three data sets: globin sequences, globin sequences with known three-dimensional structures and G-protein coupled receptor sequences. The consistency of the clusters indicate that our method is producing biologically meaningful results, which provide a very good indication of the underlying families and subfamilies. With the inclusion of secondary structure and residue solvent accessibility information, we obtain a classification of sequences of known structure which both reflects and extends their SCOP classifications. A supplementray web site containing larger versions of the figures is available at http://public.kgi.edu/approximately wid/PSB04/index.html

Amino Acid Sequence↗
PubMed2002

A Bayesian network model for protein fold and remote homologue recognition.

MOTIVATION: The Bayesian network approach is a framework which combines graphical representation and probability theory, which includes, as a special case, hidden Markov models. Hidden Markov models trained on amino acid sequence or secondary structure data alone have been shown to have potential for addressing the problem of protein fold and superfamily classification. RESULTS: This paper describes a novel implementation of a Bayesian network which simultaneously learns amino acid sequence, secondary structure and residue accessibility for proteins of known three-dimensional structure. An awareness of the errors inherent in predicted secondary structure may be incorporated into the model by means of a confusion matrix. Training and validation data have been derived for a number of protein superfamilies from the Structural Classification of Proteins (SCOP) database. Cross validation results using posterior probability classification demonstrate that the Bayesian network performs better in classifying proteins of known structural superfamily than a hidden Markov model trained on amino acid sequences alone.

Amino Acid Sequence↗
PubMed2000

Computational principles of movement neuroscience.

Unifying principles of movement have emerged from the computational study of motor control. We review several of these principles and show how they apply to processes such as motor planning, control, estimation, prediction and learning. Our goal is to demonstrate how specific models emerging from the computational approach provide a theoretical framework for movement neuroscience.

Animals↗
PubMed2000

SMEM algorithm for mixture models.

We present a split-and-merge expectation-maximization (SMEM) algorithm to overcome the local maxima problem in parameter estimation of finite mixture models. In the case of mixture models, local maxima often involve having too many components of a mixture model in one part of the space and too few in another, widely separated part of the space. To escape from such configurations, we repeatedly perform simultaneous split-and-merge operations using a new criterion for efficiently selecting the split-and-merge candidates. We apply the proposed algorithm to the training of gaussian mixtures and mixtures of factor analyzers using synthetic and real data and show the effectiveness of using the split-and-merge operations to improve the likelihood of both the training data and of held-out test data. We also show the practical usefulness of the proposed algorithm by applying it to image compression and pattern recognition problems.

Algorithms↗
PubMed2000

Variational learning for switching state-space models.

We introduce a new statistical model for time series that iteratively segments data into regimes with approximately linear dynamics and learnsthe parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time-series models -- hidden Markov models and linear dynamical systems -- and is closely related to models that are widely used in the control and econometrics literatures. It can also be derived by extending the mixture of experts neural network (Jacobs, Jordan, Nowlan, & Hinton, 1991) to its fully dynamical version, in which both expert and gating networks are recurrent. Inferring the posterior probabilities of the hidden states of this model is computationally intractable, and therefore the exact expectation maximization (EM) algorithm cannot be applied. However, we present a variational approximation that maximizes a lower bound on the log-likelihood and makes use of both the forward and backward recursions for hidden Markov models and the Kalman filter recursions for linear dynamical systems. We tested the algorithm on artificial data sets and a natural data set of respiration force from a patient with sleep apnea. The results suggest that variational approximations are a viable method for inference and learning in switching state-space models.

Algorithms↗
PubMed1999

A unifying review of linear gaussian models.

Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised learning under a single basic generative model. This is achieved by collecting together disparate observations and derivations made by many previous authors and introducing a new way of linking discrete and continuous state models using a simple nonlinearity. Through the use of other nonlinearities, we show how independent component analysis is also a variation of the same basic generative model. We show that factor analysis and mixtures of gaussians can be implemented in autoencoder neural networks and learned using squared error plus the same regularization term. We introduce a new model for static data, known as sensible principal component analysis, as well as a novel concept of spatially adaptive observation noise. We also review some of the literature involving global and local mixtures of the basic models and provide pseudocode for inference and learning for all the basic models.

Algorithms↗
PubMed1997

Generative models for discovering sparse distributed representations.

We describe a hierarchical, generative model that can be viewed as a nonlinear generalization of factor analysis and can be implemented in a neural network. The model uses bottom-up, top-down and lateral connections to perform Bayesian perceptual inference correctly. Once perceptual inference has been performed the connection strengths can be updated using a very simple learning rule that only requires locally available information. We demonstrate that the network learns to extract sparse, distributed, hierarchical representations.

Algorithms↗
PubMed1997

Modular decomposition in visuomotor learning.

The principle of 'divide-and-conquer' the decomposition of a complex task into simpler subtasks each learned by a separate module, has been proposed as a computational strategy during learning. We explore the possibility that the human motor system uses such a modular decomposition strategy to learn the visuomotor map, the relationship between visual inputs and motor outputs. Using a virtual reality system, subjects were exposed to opposite prism-like visuomotor remappings-discrepancies between actual and visually perceived hand locations- for movements starting from two distinct locations. Despite this conflicting pairing between visual and motor space, subjects learned the two starting-point-dependent visuomotor mappings and the generalization of this learning to intermediate starting locations demonstrated an interpolation of the two learned maps. This interpolation was a weighted average of the two learned visuomotor mappings, with the weighting sigmoidally dependent on starting location, a prediction made by a computational model of modular learning known as the "mixture of experts". These results provide evidence that the brain may employ a modular decomposition strategy during learning.

Feedback↗
PubMed1996

Generalization to local remappings of the visuomotor coordinate transformation.

During visually guided movement, visual representations of target location must be transformed into coordinates appropriate for movement. To investigate the representation and plasticity of the visuomotor coordinate transformation, we examined the changes in pointing behavior after local visuomotor remappings. The visual feedback of finger position was limited to one or two locations in the workspace, at which a discrepancy was introduced between the actual and visually perceived finger position. These remappings induced changes in pointing, which were largest near the locus of remapping and decreased away from it. This pattern of spatial generalization highly constrains models of the computation of the visuomotor transformation in the CNS. A simple model, in which the transformation is computed via the population activity of a set of units with large sensory receptive fields, is shown to capture the observed pattern.

Adolescent↗
PubMed1995

An internal model for sensorimotor integration.

On the basis of computational studies it has been proposed that the central nervous system internally simulates the dynamic behavior of the motor system in planning, control, and learning; the existence and use of such an internal model is still under debate. A sensorimotor integration task was investigated in which participants estimated the location of one of their hands at the end of movements made in the dark and under externally imposed forces. The temporal propagation of errors in this task was analyzed within the theoretical framework of optimal state estimation. These results provide direct support for the existence of an internal model.

Brain↗
PubMed1995

Are arm trajectories planned in kinematic or dynamic coordinates? An adaptation study.

There are several invariant features of point-to-point human arm movements: trajectories tend to be straight, smooth, and have bell-shaped velocity profiles. One approach to accounting for these data is via optimization theory; a movement is specified implicitly as the optimum of a cost function, e.g., integrated jerk or torque change. Optimization models of trajectory planning, as well as models not phrased in the optimization framework, generally fall into two main groups-those specified in kinematic coordinates and those specified in dynamic coordinates. To distinguish between these two possibilities we have studied the effects of artificial visual feedback on planar two-joint arm movements. During self-paced point-to-point arm movements the visual feedback of hand position was altered so as to increase the perceived curvature of the movement. The perturbation was zero at both ends of the movement and reached a maximum at the midpoint of the movement. Cost functions specified by hand coordinate kinematics predict adaptation to increased curvature so as to reduce the visual curvature, while dynamically specified cost functions predict no adaptation in the underlying trajectory planner, provided the final goal of the movement can still be achieved. We also studied the effects of reducing the perceived curvature in transverse movements, which are normally slightly curved. Adaptation should be seen in this condition only if the desired trajectory is both specified in kinematic coordinates and actually curved. Increasing the perceived curvature of normally straight sagittal movements led to significant (P < 0.001) corrective adaptation in the curvature of the actual hand movement; the hand movement became curved, thereby reducing the visually perceived curvature. Increasing the curvature of the normally curved transverse movements produced a significant (P < 0.01) corrective adaptation; the hand movement became straighter, thereby again reducing the visually perceived curvature. When the curvature of naturally curved transverse movements was reduced, there was no significant adaptation (P > 0.05). The results of the curvature-increasing study suggest that trajectories are planned in visually based kinematic coordinates. The results of the curvature-reducing study suggest that the desired trajectory is straight in visual space. These results are incompatible with purely dynamic-based models such as the minimum torque change model. We suggest that spatial perception--as mediated by vision--plays a fundamental role in trajectory planning.

Adaptation, Physiological↗
PubMed1994

Perceptual distortion contributes to the curvature of human reaching movements.

Unconstrained point-to-point human arm movements are generally gently curved, a fact which has been used to assess the validity of models of trajectory formation. In this study we examined the relationship between curvature perception and movement curvature for planar sagittal and transverse arm movements. We found a significant correlation (P < 0.0001, n = 16) between the curvature perceived as straight and the curvature of actual arm movements. We suggest that subjects try to make straight-line movements, but that actual movements are curved because visual perceptual distortion makes the movements appear to be straighter than they really are. We conclude that perceptual distortion of curvature contributes to the curvature seen in human point-to-point arm movements and that this must be taken into account in the assessment of models of trajectory formation.

Arm↗