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PubMed2004

Nonlinear population codes.

Theoretical and experimental studies of distributed neuronal representations of sensory and behavioral variables usually assume that the tuning of the mean firing rates is the main source of information. However, recent theoretical studies have investigated the effect of cross-correlations in the trial-to-trial fluctuations of the neuronal responses on the accuracy of the representation. Assuming that only the first-order statistics of the neuronal responses are tuned to the stimulus, these studies have shown that in the presence of correlations, similar to those observed experimentally in cortical ensembles of neurons, the amount of information in the population is limited, yielding nonzero error levels even in the limit of infinitely large populations of neurons. In this letter, we study correlated neuronal populations whose higher-order statistics, and in particular response variances, are also modulated by the stimulus. Weask two questions: Does the correlated noise limit the accuracy of the neuronal representation of the stimulus? and, How can a biological mechanism extract most of the information embedded in the higher-order statistics of the neuronal responses? Specifically, we address these questions in the context of a population of neurons coding an angular variable. We show that the information embedded in the variances grows linearly with the population size despite the presence of strong correlated noise. This information cannot be extracted by linear readout schemes, including the linear population vector. Instead, we propose a bilinear readout scheme that involves spatial decorrelation, quadratic nonlinearity, and population vector summation. We show that this nonlinear population vector scheme yields accurate estimates of stimulus parameters, with an efficiency that grows linearly with the population size. This code can be implemented using biologically plausible neurons.

Brain↗
PubMed2004

An algorithm for the detection of faces on the basis of Gabor features and information maximization.

We propose an algorithm for the detection of facial regions within input images. The characteristics of this algorithm are (1) a vast number of Gabor-type features (196,800) in various orientations, and with various frequencies and central positions, which are used as feature candidates in representing the patterns of an image, and (2) an information maximization principle, which is used to select several hundred features that are suitable for the detection of faces from among these candidates. Using only the selected features in face detection leads to reduced computational cost and is also expected to reduce generalization error. We applied the system, after training, to 42 input images with complex backgrounds (Test Set A from the Carnegie Mellon University face data set). The result was a high detection rate of 87.0%, with only six false detections. We compared the result with other published face detection algorithms.

Algorithms↗
PubMed2004

Analysis of sparse representation and blind source separation.

In this letter, we analyze a two-stage cluster-then-l(1)-optimization approach for sparse representation of a data matrix, which is also a promising approach for blind source separation (BSS) in which fewer sensors than sources are present. First, sparse representation (factorization) of a data matrix is discussed. For a given overcomplete basis matrix, the corresponding sparse solution (coefficient matrix) with minimum l(1) norm is unique with probability one, which can be obtained using a standard linear programming algorithm. The equivalence of the l(1)-norm solution and the l(0)-norm solution is also analyzed according to a probabilistic framework. If the obtained l(1)-norm solution is sufficiently sparse, then it is equal to the l(0)-norm solution with a high probability. Furthermore, the l(1)- norm solution is robust to noise, but the l(0)-norm solution is not, showing that the l(1)-norm is a good sparsity measure. These results can be used as a recoverability analysis of BSS, as discussed. The basis matrix in this article is estimated using a clustering algorithm followed by normalization, in which the matrix columns are the cluster centers of normalized data column vectors. Zibulevsky, Pearlmutter, Boll, and Kisilev (2000) used this kind of two-stage approach in underdetermined BSS. Our recoverability analysis shows that this approach can deal with the situation in which the sources are overlapped to some degree in the analyzed domain and with the case in which the source number is unknown. It is also robust to additive noise and estimation error in the mixing matrix. Finally, four simulation examples and an EEG data analysis example are presented to illustrate the algorithm's utility and demonstrate its performance.

Algorithms↗
PubMed2004

A modified algorithm for generalized discriminant analysis.

Generalized discriminant analysis (GDA) is an extension of the classical linear discriminant analysis (LDA) from linear domain to a nonlinear domain via the kernel trick. However, in the previous algorithm of GDA, the solutions may suffer from the degenerate eigenvalue problem (i.e., several eigenvectors with the same eigenvalue), which makes them not optimal in terms of the discriminant ability. In this letter, we propose a modified algorithm for GDA (MGDA) to solve this problem. The MGDA method aims to remove the degeneracy of GDA and find the optimal discriminant solutions, which maximize the between-class scatter in the subspace spanned by the degenerate eigenvectors of GDA. Theoretical analysis and experimental results on the ORL face database show that the MGDA method achieves better performance than the GDA method.

Algorithms↗
PubMed2005

A computational model of the functional role of the ventral-striatal D2 receptor in the expression of previously acquired behaviors.

The functional role of dopamine has attracted a great deal of interest ever since it was empirically discovered that dopamine-blocking drugs could be used to treat psychosis. Specifically, the D2 receptor and its expression in the ventral striatum have emerged as pivotal in our understanding of the complex role of the neuromodulator in schizophrenia, reward, and motivation. Our departure from the ubiquitous temporal difference (TD) model of dopamine neuron firing allows us to account for a range of experimental evidence suggesting that ventral striatal dopamine D2 receptor manipulation selectively modulates motivated behavior for distal versus proximal outcomes. Whether an internal model or the TD approach (or a mixture) is better suited to a comprehensive exposition of tonic and phasic dopamine will have important implications for our understanding of reward, motivation, schizophrenia, and impulsivity. We also use the model to help unite some of the leading cognitive hypotheses of dopamine function under a computational umbrella. We have used the model ourselves to stimulate and focus new rounds of experimental research.

Animals↗
PubMed2006

Magnification control in self-organizing maps and neural gas.

We consider different ways to control the magnification in self-organizing maps (SOM) and neural gas (NG). Starting from early approaches of magnification control in vector quantization, we then concentrate on different approaches for SOM and NG. We show that three structurally similar approaches can be applied to both algorithms that are localized learning, concave-convex learning, and winner-relaxing learning. Thereby, the approach of concave-convex learning in SOM is extended to a more general description, whereas the concave-convex learning for NG is new. In general, the control mechanisms generate only slightly different behavior comparing both neural algorithms. However, we emphasize that the NG results are valid for any data dimension, whereas in the SOM case, the results hold only for the one-dimensional case.

Algorithms↗
PubMed1999

The relationship between synchronization among neuronal populations and their mean activity levels.

In the past decade the importance of synchronized dynamics in the brain has emerged from both empirical and theoretical perspectives. Fast dynamic synchronous interactions of an oscillatory or nonoscillatory nature may constitute a form of temporal coding that underlies feature binding and perceptual synthesis. The relationship between synchronization among neuronal populations and the population firing rates addresses two important issues: the distinction between rate coding and synchronization coding models of neuronal interactions and the degree to which empirical measurements of population activity, such as those employed by neuroimaging, are sensitive to changes in synchronization. We examined the relationship between mean population activity and synchronization using biologically plausible simulations. In this article, we focus on continuous stationary dynamics. (In a companion article, Chawla (forthcoming), we address the same issue using stimulus-evoked transients.) By manipulation parameters such as extrinsic input, intrinsic noise, synaptic efficacy, density of extrinsic connections, the voltage-sensitive nature of postsynaptic mechanisms, the number of neurons, and the laminar structure within the populations, we were able to introduce variations in both mean activity and synchronization under a variety of simulated neuronal architectures. Analyses of the simulated spike trains and local field potentials showed that in nearly every domain of the model's parameter space, mean activity and synchronization were tightly coupled. This coupling appears to be mediated by an increase in synchronous gain when effective membrane time constants are lowered by increased activity. These observations show that under the assumptions implicit in our models, rate coding and synchrony coding in neural systems with reciprocal interconnections are two perspectives on the same underlying dynamic. This suggests that in the absence of specific mechanisms decoupling changes in synchronization from firing levels, indexes of brain activity that are based purely on synaptic activity (e.g., functional magnetic resonance imaging) may also be sensitive to changes in synchronous coupling.

Brain↗
PubMed1997

Recent advances in evolvable systems--ICES 96 (International Conference on Evolvable Systems).

This paper reviews the developments in evolvable hardware systems presented at the First International Conference on Evolvable Systems (ICES 96). The main body of the review gives an overview of the 34 papers presented orally, splitting them into three broad groups according to whether they involve (1) evolving a fit solution to a problem as a member of a population of competing candidates, (2) evolving solutions that can individually learn from and adapt to their environments, or (3) the embryonic growth of solutions. We also review the discussion sessions of the conference and give pointers to related upcoming events.

Algorithms↗
PubMed1996

A simple spike train decoder inspired by the sampling theorem.

Reconstructing a time-varying stimulus estimate from a spike train (Bialek's "decoding" of a spike train) has become an important way to study neural information processing. In this paper, we describe a simple method for reconstructing a time-varying current injection signal from the simulated spike train it produces. This technique extracts most of the information from the spike train, provided that the input signal is appropriately matched to the spike generator. To conceptualize this matching, we consider spikes as instantaneous "samples" of the somatic current. The Sampling Theorem is then applicable, and it suggests that the bandwidth of the injected signal not exceed half the spike generator's average firing rate. The average firing rate, in turn, depends on the amplitude range and DC bias of the injected signal. We hypothesize that nature faces similar problems and constraints when transmitting a time-varying waveform from the soma of one neuron to the dendrite of the postsynaptic cell.

Action Potentials↗
PubMed2000

Learning in brains and machines.

The problem of learning is arguably at the very core of the problem of intelligence, both biological and artificial. In this paper we sketch some of our work over the last ten years in the area of supervised learning, focusing on three interlinked directions of research: theory, engineering applications (that is, making intelligent software) and neuroscience (that is, understanding the brain's mechanisms of learning).

Brain↗
PubMed2001

Support vector machines for predicting protein structural class.

BACKGROUND: We apply a new machine learning method, the so-called Support Vector Machine method, to predict the protein structural class. Support Vector Machine method is performed based on the database derived from SCOP, in which protein domains are classified based on known structures and the evolutionary relationships and the principles that govern their 3-D structure. RESULTS: High rates of both self-consistency and jackknife tests are obtained. The good results indicate that the structural class of a protein is considerably correlated with its amino acid composition. CONCLUSIONS: It is expected that the Support Vector Machine method and the elegant component-coupled method, also named as the covariant discrimination algorithm, if complemented with each other, can provide a powerful computational tool for predicting the structural classes of proteins.

Algorithms↗
PubMed2006

Learning interpretable SVMs for biological sequence classification.

BACKGROUND: Support Vector Machines (SVMs)--using a variety of string kernels--have been successfully applied to biological sequence classification problems. While SVMs achieve high classification accuracy they lack interpretability. In many applications, it does not suffice that an algorithm just detects a biological signal in the sequence, but it should also provide means to interpret its solution in order to gain biological insight. RESULTS: We propose novel and efficient algorithms for solving the so-called Support Vector Multiple Kernel Learning problem. The developed techniques can be used to understand the obtained support vector decision function in order to extract biologically relevant knowledge about the sequence analysis problem at hand. We apply the proposed methods to the task of acceptor splice site prediction and to the problem of recognizing alternatively spliced exons. Our algorithms compute sparse weightings of substring locations, highlighting which parts of the sequence are important for discrimination. CONCLUSION: The proposed method is able to deal with thousands of examples while combining hundreds of kernels within reasonable time, and reliably identifies a few statistically significant positions.

Algorithms↗
PubMed2007

Overview of informatics for high content screening.

With the growing use of high content screening (HCS) and analysis in drug discovery and systems biology, informatics has come to the forefront as a critical technology to effectively utilize the massive volumes of high content data and images being generated. Informatics technologies are required to transform HCS data and images into useful information and then into knowledge to drive decision making in an efficient and cost effective manner. In this chapter, we provide an overview of informatics tools and technologies for HCS, discuss some of the challenges of harnessing the huge and growing volumes of HCS data, and provide insight to help toward implementing or selecting, and utilizing a high content informatics solution to meet your organization's needs.

Computers↗
PubMed2003

Design for a brain revisited: the neuromorphic design and functionality of the interactive space 'Ada'.

While much is now known about the operation and organisation of the brain at the neuronal and microcircuit level, we are still some way from understanding it as a complete system from the lowest to the highest levels of description. One way to gain such an integrative understanding of neural systems is to construct them. We have built the largest neuromorphic system yet known, an interactive space called 'Ada' that is able to interact with many people simultaneously using a wide variety of sensory and behavioural modalities. 'She' received 553,700 visitors over 5 months during the Swiss Expo.02 in 2002. In this paper we present the broad motivations, design and technologies behind Ada, and discuss the construction and analysis of the system.

Behavior↗
PubMed2006

Application of self-organizing maps in compounds pattern recognition and combinatorial library design.

In the computer-aided drug design, in order to find some new leads from a large library of compounds, the pattern recognition study of the diversity and similarity assessment of the chemical compounds is required; meanwhile in the combinatorial library design, more attention is given to design target focusing library along with diversity and drug-likeness criteria. This review presents the current state-of-art applications of Kohonen self-organizing maps (SOM) for studying the compounds pattern recognition, comparing the property of molecular surfaces, distinguishing drug-like and nondrug-like molecules, splitting a dataset into the proper training and test sets before constructing a QSAR (Quantitative Structural-Activity Relationship) model, and also for the combinatorial libraries comparison and the combinatorial library design. The Kohonen self-organizing map will continue to play an important role in drug discovery and library design.

Computer Simulation↗
PubMed2001

View from the boundary.

Re-implementing biological mechanisms on robots not only has technological application but can provide a unique perspective on the nature of sensory processing in animals. To make a robot work, we need to understand the function as part of an embodied, behaving system. I argue that this perspective suggests that the terms "representation" and "information processing" can be misleading when we seek to understand how neurobiological mechanisms carry out perceptual processes. This argument is presented here with reference to a robot model of cricket behavior, which has demonstrated competence comparable to that of the insect, but utilizes surprisingly simple central processing. Instead it depends on sensory interfaces that are well matched to the task, and on the link between environment, action, and perception.

Animals↗
PubMedDate not supplied

[Classification of cytological smears of the cervix with neuronal methods].

BACKGROUND: Cytological smears obtained from the cervix are routinely examined under the microscope as part of screening programs for the early detection of cervical cancer. The aim of the present study was to investigate whether a simple feature extraction approach using only standard image processing techniques combined with a neural classifier would lead to acceptable results that might serve as a starting point for the development of a fully automated screening system. MATERIALS AND METHODS: Gray-value images of 106 cervical smears (512 x 512 pixels) divided into two groups--inconspicuous (57) and atypical (49)--by an experienced pathologist on the basis of the original smears were employed to evaluate the method. From these images, 31 features quantifying properties of either the cell nucleus or the cytoplasm were extracted. These features were categorized with three different architectures of a neural classifier: learning vector quantization (LVQ), multilayer perceptron (MLP) and a single perceptron. CONCLUSIONS: The results show a reclassification accuracy of about 91% for all three algorithms. Sensitivity was uniform at approximately 78%, and specificity varied between 75% and 91% in the leave-one-out evaluation. These very good results provide strong encouragement for further studies involving PAP scores and colour images.

Artificial Intelligence↗