Methods for frequency and correlation analyses of neurograms.

Many physiological functions are based on motor rhythmic activities, among them breathing is a vital issue. The method presented here, or 'temporal grid extraction', aims at characterizing the temporal organization of such an activity. Beyond the measurement of the fundamental frequency, defining the successive cycles, some signal processing tools are helpful in order to look for the presence of higher frequency components that potentially structure these cycles.

Is there a common drive for buccal movements associated with buccal and lung 'breath' in Lithobates catesbeianus?

In amphibians, there is some evidence that (1) anatomically separate brainstem respiratory oscillators are involved in rhythm generation, one for the buccal rhythm and another for the lung rhythm and (2) they become functionally coupled during metamorphosis. The present analysis, performed on neurograms recorded using brainstem preparations from Lithobates catesbeianus, aims to investigate the temporal organisation of lung and buccal burst types. Continuous Wavelet Transfom applied to the separated buccal and lung signals of a neurogram revealed that both buccal and lung frequency profiles exhibited the same low frequency peak around 1 Hz.

Neural network model of an amphibian ventilatory central pattern generator.

The neuronal multiunit model presented here is a formal model of the central pattern generator (CPG) of the amphibian ventilatory neural network, inspired by experimental data from Pelophylax ridibundus. The kernel of the CPG consists of three pacemakers and two follower neurons (buccal and lung respectively). This kernel is connected to a chain of excitatory and inhibitory neurons organized in loops.

New insights into sucking, swallowing and breathing central generators: A complexity analysis of rhythmic motor behaviors.

Sucking, swallowing and breathing are dynamic motor behaviors. Breathing displays features of chaos-like dynamics, in particular nonlinearity and complexity, which take their source in the automatic command of breathing. In contrast, buccal/gill ventilation in amphibians is one of the rare motor behaviors that do not display nonlinear complexity.

New insights in gill/buccal rhythm spiking activity and CO(2) 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.

Neural model of frog ventilatory rhythmogenesis.

In the adult frog respiratory system, periods of rhythmic movements of the buccal floor are interspersed by lung ventilation episodes. The ventilatory activity results from the interaction of two hypothesized oscillators in the brainstem. Here, we model these oscillators with two coupled neural networks, whose co-activation results in the emergence of new dynamics.

The locust olfactory system as a case study for modeling dynamics of neurobiological networks: from discrete time neurons to continuous time neurons.

Both chaotic and periodic activities are observed in networks of the central nervous systems. We choose the locust olfactory system as a good case study to analyze the relationships between networks' structure and the types of dynamics involved in coding mechanisms. In our modeling approach, we first build a fully connected recurrent network of synchronously updated McCulloch and Pitts neurons (MC-P type).

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.

Oscillation and coding in a formal neural network considered as a guide for plausible simulations of the insect olfactory system.

For the analysis of coding mechanisms in the insect olfactory system, a fully connected network of synchronously updated McCulloch and Pitts neurons (MC-P type) was developed [Quenet, B., Horn, D., 2003.

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.

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.

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.

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.

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.

Functionality of divergence and convergence in a model of the insect olfactory system.

Recent studies have shown that the insect olfactory system uses a spatio-temporal encoding of odours in the population of projection neurons in the antennal lobe, and suggest that the information thus coded is spread across a large population of Kenyon cells in the mushroom bodies. At this stage, the temporal part of the code might be transformed into a spatial code, especially via the temporally sensitive mechanisms of paired-pulse facilitation and feedback inhibition with its possible associated rebound. We explore here a simple model of the olfactory system using a three-layer network of formal neurons, comprising a fixed number (three) of projection and inhibitory neurons, but a variable number of Kenyon cells.

Statistical analysis and parsimonious modelling of dendrograms of in vitro neurones.

The processes whereby developing neurones acquire morphological features that are common to entire populations (thereby allowing the definition of neuronal types) are still poorly understood. A mathematical model of neuronal arborizations may be useful to extract basic parameters or organization rules, hence helping to achieve a better understanding of the underlying growth processes. We present a parsimonious statistical model, intended to describe the topological organization of neuritic arborizations with a minimal number of parameters.

Classification of fusiform neocortical interneurons based on unsupervised clustering.

A classification of fusiform neocortical interneurons (n = 60) was performed with an unsupervised cluster analysis based on the comparison of multiple electrophysiological and molecular parameters studied by patch-clamp and single-cell multiplex reverse transcription-PCR in rat neocortical acute slices. The multiplex reverse transcription-PCR protocol was designed to detect simultaneously the expression of GAD65, GAD67, calbindin, parvalbumin, calretinin, neuropeptide Y, vasoactive intestinal peptide (VIP), somatostatin (SS), cholecystokinin, alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid, kainate, N-methyl-d-aspartate, and metabotropic glutamate receptor subtypes. Three groups of fusiform interneurons with distinctive features were disclosed by the cluster analysis.

A morphometric classification of pupal honeybee antennal lobe neurones in culture.

Both intrinsic (programmed) and extrinsic (non-programmed) factors are thought to play a role in the morphogenesis of neurones in the honeybee antennal lobe (the first relay station in the olfactory pathway) during development. We present here a morphometric and statistical analysis of a large population of pupal honeybee antennal lobe neurones grown in primary culture. Quantitative parameters were used to characterize neuronal shapes.