Brigitte Quenet

A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics

The statistical analysis of experimentally recorded brain activity patterns may require comparisons between large sets of complex signals in order to find meaningful similarities and differences between signals with large variability. High-level representations such as time-frequency maps convey a wealth of useful information, but they involve a large number of parameters that make statistical investigations of many signals difficult at present. In this paper, we describe a method that performs drastic reduction in the complexity of time-frequency representations through a modelling of the maps by elementary functions. The method is validated on artificial signals and subsequently applied to electrophysiological brain signals (local field potential) recorded from the olfactory bulb of rats while they are trained to recognize odours. From hundreds of experimental recordings, reproducible time-frequency events are detected, and relevant features are extracted, which allow further information processing, such as automatic classification.

Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators

This paper describes the automatic extraction of the P, Q, R, S and T waves of electrocardiographic recordings (ECGs), through the combined use of a new machine-learning algorithm termed generalized orthogonal forward regression (GOFR) and of a specific parameterized function termed Gaussian mesa function (GMF). GOFR breaks up the heartbeat signal into Gaussian mesa functions, in such a way that each wave is modeled by a single GMF; the model thus generated is easily interpretable by the physician. GOFR is an essential ingredient in a global procedure that locates the R wave after some simple pre-processing, extracts the characteristic shape of each heart beat, assigns P, Q, R, S and T labels through automatic classification, discriminates normal beats (NB) from abnormal beats (AB), and extracts features for diagnosis. The efficiency of the detection of the QRS complex, and of the discrimination of NB from AB, is assessed on the MIT and AHA databases; the labeling of the P and T wave is validated on the QTDB database.

The dynamic neural filter: a binary model of spatiotemporal coding

We describe and discuss the properties of a binary neural network that can serve as a dynamic neural filter (DNF), which maps regions of input space into spatiotemporal sequences of neuronal activity. Both deterministic and stochastic dynamics are studied, allowing the investigation of the stability of spatiotemporal sequences under noisy conditions. We define a measure of the coding capacity of a DNF and develop an algorithm for constructing a DNF that can serve as a source of given codes. On the basis of this algorithm, we suggest using a minimal DNF capable of generating observed sequences as a measure of complexity of spatiotemporal data. This measure is applied to experimental observations in the locust olfactory system, whose reverberating local field potential provides a natural temporal scale allowing the use of a binary DNF. For random synaptic matrices, a DNF can generate very large cycles, thus becoming an efficient tool for producing spatiotemporal codes. The latter can be stabilized by applying to the parameters of the DNF a learning algorithm with suitable margins.

Building meaningful representations for nonlinear modeling of 1d- and 2d-signals: applications to biomedical signals

Abstract The paper addresses two problems that are frequently encountered when modeling data by linear combinations of nonlinear parameterized functions. The first problem is feature selection, when features are sought as functions that are nonlinear in their parameters (e.g. Gaussians with adjustable centers and widths, wavelets with adjustable translations and dilations, etc.). The second problem is the design of an intelligible representation for 1D- and 2D- signals with peaks and troughs that have a definite meaning for experts. To address the first problem, a generalization of the orthogonal forward regression method is described. To address the second problem, a new family of nonlinear parameterized functions, termed Gaussian mesa functions, is defined. It allows the modeling of signals such that each significant peak or trough is modeled by a single, identifiable function. The resulting representation is sparse in terms of adjustable parameters, thereby lending itself easily to automatic analysis and classification, yet it is readily intelligible for the expert. An application of the methodology to the automatic analysis of electrocardiographic (Holter) recordings is described. Applications to the analysis of neurophysiological signals and EEG signals (early detection of Alzheimers disease) are outlined.

Analysis of spatiotemporal patterns in a model of olfaction

Abstract We model spatiotemporal patterns in locust olfaction with the dynamic neural filter, a recurrent network that produces spatiotemporal patterns in reaction to sets of constant inputs. We specify, within the model, inputs corresponding to different odors and different concentrations of the same odor. Then we proceed to analyze the resulting spatiotemporal patterns of the neurons of our model. Using SVD we investigate three kinds of data: global spatiotemporal data consisting of neuronal firing patterns over the period of odor presentation, spatial data, i.e. total spike counts during this period, and local spatiotemporal data which are neuronal spikes in single temporal bins.

Temporal coding in an olfactory oscillatory model

Abstract We propose a model of the glomerular stage of the insect olfactory pathway that exhibits coding of inputs through spatio–temporal patterns of the type observed experimentally in locust. Making use of the temporal bins provided by the oscillatory field potential we find that it suffices to employ simple little-Hopfield dynamics to account for a rich repertoire of patterns. In particular, we show that we are able to reproduce complex activity patterns from electrophysiological recordings in insects. Biologically plausible mechanisms of synaptic adaptation are discussed.

Dynamic proximity of spatio-temporal sequences

Recurrent networks can generate spatio-temporal neural sequences of very large cycles, having an apparent random behavior. Nonetheless a proximity measure between these sequences may be defined through comparison of the synaptic weight matrices that generate them. Following the dynamic neural filter (DNF) formalism we demonstrate this concept by comparing teacher and student recurrent networks of binary neurons. We show that large sequences, providing a training set well exceeding the Cover limit, allow for good determination of the synaptic matrices. Alternatively, assuming the matrices to be known, very fast determination of the biases can be achieved. Thus, a spatio-temporal sequence may be regarded as spatio-temporal encoding of the bias vector. We introduce a linear support vector machine (SVM) variant of the DNF in order to specify an optimal weight matrix. This approach allows us to deal with noise. Spatio-temporal sequences generated by different DNFs with the same number of neurons may be compared by calculating correlations of the synaptic matrices of the reconstructed DNFs. Other types of spatio-temporal sequences need the introduction of hidden neurons, and/or the use of a kernel variant of the SVM approach. The latter is being defined as a recurrent support vector network (RSVN).

FROM COMPLEX SIGNAL TO ADAPTED BEHAVIOR A THEORETICAL APPROACH OF THE HONEYBEE OLFACTORY BRAIN

The honeybee olfactory pathway is an attractive system for modeling: it is relatively simple, and it is well described functionally and morphologically. Moreover, due to the conservation of the olfactory structure through phylogeny, models may bring information of generic interest. From the point of view of behavior, this system has the ability of encoding the sensory messages into stable representations, and extracting key features from them. The neural bases of these mechanisms are still largely unknown; the purpose of the present paper is to present three different models of the same system, which make use of the same corpus of morphological and electrophysiological data, but which incorporate these data with different levels of details. We show the interrelations between these models and the specific contribution of each of them to the modeling of the olfactory pathway. We show that the design of the simplest model capitalized on the results of the previous ones, and that it suggests mechanisms for simultaneous generation of stable internal representations and key feature extraction.

Modelling spatiotemporal olfactory data in two steps: from binary to Hodgkin-Huxley neurones

Network models of synchronously updated McCulloch-Pitts neurones exhibit complex spatiotemporal patterns that are similar to activities of biological neurones in phase with a periodic local field potential, such as those observed experimentally by Wehr and Laurent (1996, Nature 384, 162-166) in the locust olfactory pathway. Modelling biological neural nets with networks of simple formal units makes the dynamics of the model analytically tractable. It is thus possible to determine the constraints that must be satisfied by its connection matrix in order to make its neurones exhibit a given sequence of activity (see, for instance, Quenet et al., 2001, Neurocomputing 38-40, 831-836). In the present paper, we address the following question: how can one construct a formal network of Hodgkin-Huxley (HH) type neurones that reproduces experimentally observed neuronal codes? A two-step strategy is suggested in the present paper: first, a simple network of binary units is designed, whose activity reproduces the binary experimental codes; second, this model is used as a guide to design a network of more realistic formal HH neurones. We show that such a strategy is indeed fruitful: it allowed us to design a model that reproduces the Wehr-Laurent olfactory codes, and to investigate the robustness of these codes to synaptic noise.

New insights in gill/buccal rhythm spiking activity and CO2 sensitivity in pre- and postmetamorphic tadpoles (Pelophylax ridibundus)

Central CO(2) chemosensitivity is crucial for all air-breathing vertebrates and raises the question of its role in ventilatory rhythmogenesis. In this study, neurograms of ventilatory motor outputs recorded in facial nerve of premetamorphic and postmetamorphic tadpole isolated brainstems, under normo- and hypercapnia, are investigated using Continuous Wavelet Transform spectral analysis for buccal activity and computation of number and amplitude of spikes during buccal and lung activities. Buccal bursts exhibit fast oscillations (20-30Hz) that are prominent in premetamorphic tadpoles: they result from the presence in periodic time windows of high amplitude spikes. Hypercapnia systematically decreases the frequency of buccal rhythm in both pre- and postmetamorphic tadpoles, by a lengthening of the interburst duration. In postmetamorphic tadpoles, hypercapnia reduces buccal burst amplitude and unmasks small fast oscillations. Our results suggest a common effect of the hypercapnia on the buccal part of the Central Pattern Generator in all tadpoles and a possible effect at the level of the motoneuron recruitment in postmetamorphic tadpoles.