Biomedical subjects

G Deco

Publications and source records attributed to G Deco.

15 recordsLinked to original sources

PubMed2001

Temporal clustering with spiking neurons and dynamic synapses: towards technological applications.

We apply spiking neurons with dynamic synapses to detect temporal patterns in a multi-dimensional signal. We use a network of integrate-and-fire neurons, fully connected via dynamic synapses, each of which is given by a biologically plausible dynamical model based on the exact pre- and post-synaptic spike timing. Dependent on their adaptable configuration (learning) the synapses automatically implement specific delays. Hence, each output neuron with its set of incoming synapses works as a detector for a specific temporal pattern. The whole network functions as a temporal clustering mechanism with one output per input cluster. The classification capability is demonstrated by illustrative examples including patterns from Poisson processes and the analysis of speech data.

Cluster Analysis↗

PubMed2000

A hierarchical neural system with attentional top-down enhancement of the spatial resolution for object recognition.

We present a hierarchical neurodynamical system for object recognition based on attentional control of the spatial resolution with which an object is analyzed during an iterative hypothesis testing cycle. Psychophysical evidence strongly suggests that attentional processing results in the enhancement of the spatial resolution in the input region corresponding to the focus of attention. We adopt a computational neuroscience approach in order to analyze this attentional enhancement of the spatial resolution for object recognition. The system consists of a where- and a what-module which include networks with feedforward and feedback interconnections describing the mutual links between different areas of the visual cortex.

Attention↗

PubMed2000

A neuro-cognitive visual system for object recognition based on testing of interactive attentional top-down hypotheses.

We propose an extension of a systemic model for object recognition formulated by Rybak et al (1998 Vision Research 38 2387-2400) which is based on the functional organisation of the visual systems in primate brains. In contrast to the learning and recognition scheme of Rybak et al we do not assume a behavioural paradigm, i.e. a visuomotor programmed scanpath that determines the sequence of foveation on the different parts of the object. As in the basis architecture of Rybak et al, the system modules are separated into 'what'-like subsystems corresponding to the ventral occipito-inferotemporal visual path and 'where'-like complexes analogous to the dorsal occipito-parietal visual path. The 'what' system analyses local features in the actual foveation as in Rybak et al. But, in our case, the 'where' memory, instead of programming a behavioural scanpath, scores the spatial relationship between successive fixation and the spatial relationship between the associated main edges. The recognition is based on the identification of parts and their spatial relationship. This gives the learning and recognition mechanisms more flexibility in the sense that, for recognising an object, several different fixation sequences may be accepted.

Cognition↗

PubMed2000

Neural coding: higher-order temporal patterns in the neurostatistics of cell assemblies.

Recent advances in the technology of multiunit recordings make it possible to test Hebb's hypothesis that neurons do not function in isolation but are organized in assemblies. This has created the need for statistical approaches to detecting the presence of spatiotemporal patterns of more than two neurons in neuron spike train data. We mention three possible measures for the presence of higher-order patterns of neural activation--coefficients of log-linear models, connected cumulants, and redundancies--and present arguments in favor of the coefficients of log-linear models. We present test statistics for detecting the presence of higher-order interactions in spike train data by parameterizing these interactions in terms of coefficients of log-linear models. We also present a Bayesian approach for inferring the existence or absence of interactions and estimating their strength. The two methods, the frequentist and the Bayesian one, are shown to be consistent in the sense that interactions that are detected by either method also tend to be detected by the other. A heuristic for the analysis of temporal patterns is also proposed. Finally, a Bayesian test is presented that establishes stochastic differences between recorded segments of data. The methods are applied to experimental data and synthetic data drawn from our statistical models. Our experimental data are drawn from multiunit recordings in the prefrontal cortex of behaving monkeys, the somatosensory cortex of anesthetized rats, and multiunit recordings in the visual cortex of behaving monkeys.

Action Potentials↗

PubMed1999

Spatiotemporal coding in the cortex: information flow-based learning in spiking neural networks.

We introduce a learning paradigm for networks of integrate-and-fire spiking neurons that is based on an information-theoretic criterion. This criterion can be viewed as a first principle that demonstrates the experimentally observed fact that cortical neurons display synchronous firing for some stimuli and not for others. The principle can be regarded as the postulation of a nonparametric reconstruction method as optimization criteria for learning the required functional connectivity that justifies and explains synchronous firing for binding of features as a mechanism for spatiotemporal coding. This can be expressed in an information-theoretic way by maximizing the discrimination ability between different sensory inputs in minimal time.

Action Potentials↗

PubMed1999

A characterization of HRV's nonlinear hidden dynamics by means of Markov models.

A study of the 24-h heart rate variability's (HRV) hidden dynamic is performed hour by hour, in order to investigate the evolution of the nonlinear structure of the underlying nervous system. A hierarchy of null hypotheses of nonlinear Markov models with increasing order n is tested against the hidden dynamic of the HRV time series. The minimum accepted Markov order supplies information about the nonlinearity of the HRV's hidden dynamic and consequently of the underlying nervous system. The Markov model with minimum order is detected for each hour of the RR time series extracted from seven 24-h electrocardiogram records of patients in different pathophysiological conditions, some including ventricular tachycardia episodes. Heart rate, pNN30, and LF/HF index plots are reported to serve as a reference for the description of the patient's cardiovascular frame during each examined hour. The minimum Markov order shows to be a promising index for quantifying the average nonlinearity of the autonomic nervous system's activity.

Aged↗

PubMed1998

Information maximization and independent component analysis; is there a difference?

This article provides a detailed and rigorous analysis of the two commonly used methods for redundancy reduction: linear independent component analysis (ICA) posed as a direct minimization of a suitably chosen redundancy measure and information maximization (InfoMax) of a continuous stochastic signal transmitted through an appropriate nonlinear network. The article shows analytically that ICA based on the Kullback-Leibler information as a redundancy measure and InfoMax lead to the same solution if the parameterization of the output nonlinear functions in the latter method is sufficiently rich. Furthermore, this work discusses the alternative redundancy measures not based on the Kullback-Leibler information distance. The practical issues of applying ICA and InfoMax are also discussed and illustrated on the problem of extracting statistically independent factors from a linear, pixel-by-pixel mixture of images.

Algorithms↗

PubMed1998

Dynamics extraction in multivariate biomedical time series.

A nonlinear analysis of the underlying dynamics of a biomedical time series is proposed by means of a multi-dimensional testing of nonlinear Markovian hypotheses in the observed time series. The observed dynamics of the original N-dimensional biomedical time series is tested against a hierarchy of null hypotheses corresponding to N-dimensional nonlinear Markov processes of increasing order, whose conditional probability densities are estimated using neural networks. For each of the N time series, a measure based on higher order cumulants quantifies the independence between the past of the N-dimensional time series, and its value r steps ahead. This cumulant-based measure is used as a discriminating statistic for testing the null hypotheses. Experiments performed on artificial and real world examples, including autoregressive models, noisy chaos, and nonchaotic nonlinear processes, show the effectiveness of the proposed approach in modeling multivariate systems, predicting multidimensional time series, and characterizing the structure of biological systems. Electroencephalogram (EEG) time series and heart rate variability trends are tested as biomedical signal examples.

Aged↗

PubMed1998

The coding of information by spiking neurons: an analytical study.

We analyse analytically the coding of information by a spiking neuron. The emphasis is on the question of how many spikes are necessary for the reliable discrimination of two different input signals. The discrimination ability is measured by the second-order Rényi mutual information between the random variable describing the name of the signal and a sequence of n output spikes. Analysing this measure as a function of n, we study the coding strategy of a single spiking neuron, with the following main results. A small number of output spikes is required for efficient discrimination of input signals, i.e. for encoding them, if the separation is easy; a large number of output spikes is required in the difficult case of separation of very similar input signals. Three different versions of the spike response model of a single neuron are studied. The approach presented can be regarded as a non-parametric version of the reconstruction method of Bialek.

Action Potentials↗

PubMed1995