Biomedical subjects

Gérard Dreyfus

Publications and source records attributed to Gérard Dreyfus.

5 recordsLinked to original sources

PubMed2006

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.

Algorithms↗

PubMed2004

Reply to the comments on "Local Overfitting Control via Leverages" in "Jacobian Conditioning Analysis for Model Validation" by I. Rivals and L. Personnaz.

"Jacobian Conditioning Analysis for Model Validation" by Rivals and Personnaz in this issue is a comment on Monari and Dreyfus (2002). In this reply, we disprove their claims. We point to flawed reasoning in the theoretical comments and to errors and inconsistencies in the numerical examples. Our replies are substantiated by seven counterexamples, inspired by actual data, which show that the comments on the accuracy of the computation of the leverages are unsupported and that following the approach they advocate leads to discarding valid models or validating overfitted models.

Action Potentials↗

PubMed2002

Local overfitting control via leverages.

We present a novel approach to dealing with overfitting in black box models. It is based on the leverages of the samples, that is, on the influence that each observation has on the parameters of the model. Since overfitting is the consequence of the model specializing on specific data points during training, we present a selection method for nonlinear models based on the estimation of leverages and confidence intervals. It allows both the selection among various models of equivalent complexities corresponding to different minima of the cost function (e.g., neural nets with the same number of hidden units) and the selection among models having different complexities (e.g., neural nets with different numbers of hidden units). A complete model selection methodology is derived.

Journal Article↗

PubMedDate not supplied

Formal modeling with multistate neurones and multidimensional synapses.

Multistate neurones, a generalization of the popular McCulloch-Pitts binary neurones, are described; they are intended to model the fact that neurones may be in several different states of activity, while McCulloch-Pitts neurones model two states only: active or inactive. We show that as a consequence, multidimensional synapses are necessary to describe the dynamics of the model. As an illustration, we show how to derive the parameters of formal multistate neurones and their associated multidimensional synapses from simulations involving Hodgkin-Huxley neurones. Our approach opens the way to solve in a more biologically plausible way, two problems that were addressed previously: (1) the resolution of 'inverse problems', i.e. the construction of formal networks, whose dynamics follows a pre-defined spatio-temporal binary sequence, (2) the generation of spatio-temporal patterns that reproduce exactly the 'code' extracted from experimental recordings (olfactory codes at the glomerular level).

Models, Neurological↗

PubMedDate not supplied

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.

Models, Neurological↗