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. The method is applied to neurograms recorded from frog brainstem preparations, where two cycle types, buccal and lung cycles, may alternate. It relies on:•Continues Wavelet Transform (CWT) for time-frequency maps and frequency profiles•Crosscorrelation analysis for amplitude maps and amplitude profiles•Cycle-by-cycle autocorrelation analysis for autocorrelation maps and autocorrelation profilesUsing this method, the maps and profiles have revealed that a common high frequency clock drives both buccal and lung 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. This suggests that a common 'clock' organises both rhythms within an animal. A cross-correlation analysis applied to the buccal and lung burst signals revealed their similar intrinsic oscillation features, occurring at approximately 25 Hz. These observations suggest that a coupling between the lung and buccal oscillators emerges at metamorphosis. This coupling may be related to inter-connectivity between the two oscillators, and to a putative common drive.

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. Simulations are performed with Izhikevich-type neurons. When driven by the buccal follower, the excitatory neurons transmit and reorganize the follower activity pattern along the chain, and when driven by the lung follower, the excitatory and inhibitory neurons of the chain fire in synchrony. The additive effects of synaptic inputs from the pacemakers on the buccal follower account for (1) the low frequency buccal rhythm, (2) the intra-burst high frequency oscillations, and (3) the episodic lung activity. Chemosensitivity to acidosis is implemented by an increase in the firing frequency of one of the pacemakers. This frequency increase leads to both a decrease in the buccal burst frequency and an increase in the lung episode frequency. The rhythmogenic properties of the model are robust against synaptic noise and pacemaker jitter. To validate the rhythm and pattern genesis of this formal CPG, neurograms were built from simulated motoneuron activity, and compared with experimental neurograms. The basic principles of our model account for several experimental observations, and we suggest that these principles may be generic for amphibian ventilation.

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. 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.

An old hypothesis and new tools: Alfred Fessard's approach to the problem of consciousness.

In 1954, a symposium was held in Canada on "Brain Mechanisms and Consciousness." It was a time for the promotion of international and interdisciplinary scientific cooperation, of new technological expectation, and of speculating about complex human behavior. Alfred Fessard's lecture on "Mechanisms of Nervous Integration and Conscious Experience" was one of the outstanding presentations, rich in critical analysis of the then available experimental data and in working hypothesis proposals. Reading the concept expressed by Fessard, it was found that several of his ideas had anticipated data obtained in modern research with new technologies.

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. One of the networks is built with "loop chains" of excitatory and inhibitory neurones producing periodic activities. We define two groups of excitatory neurones whose oscillatory antiphasic sums of activities represent output signals as possible motor commands towards antagonist buccal muscles. The other oscillator is a small network with a self-modulated excitatory input to an excitatory neurone whose episodic firings synchronise some neurones of the first network chains. When this oscillator is silent, the output signals exhibit only regular oscillations, and, when active, the synchronisation process reconfigures the output signals whose new features are representative of lung ventilation motor patterns. The biological interest of this formal model is illustrated by the persistence of the relevant dynamical features when perturbations are introduced in the model, i.e. dynamic noises and architecture modifications. The implementation of the networks with clock-driven continuous time neurones provides simulations with physiological time scales.

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. The dynamic neural filter: a binary model of spatio-temporal coding. Neural Comput. 15 (2), 309-329]. Considering the update time as an intrinsic clock, this "Dynamic Neural Filter" (DNF), which maps regions of input space into spatio-temporal sequences of neuronal activity, is able to produce exact binary codes extracted from the synchronized activities recorded at the level of projection neurons (PN) in the locust antennal lobe (AL) in response to different odors [Wehr, M., Laurent, G., 1996. Odor encoding by temporal sequences of firing in oscillating neural assemblies. Nature 384, 162-166]. Here, in a first step, we separate the populations of PN and local inhibitory neurons (LN) and use the DNF as a guide for simulations based on biological plausible neurons (Hodgkin-Huxley: H-H type). We show that a parsimonious network of 10 H-H neurons generates action potentials whose timing represents the required codes. In a second step, we construct a new type of DNF in order to study the population dynamics when different delays are taken into account. We find synaptic matrices which lead to both the emergence of robust oscillations and spatio-temporal patterns, using a formal criterion, based on a Normalized Euclidian Distance (NED), in order to measure the use of the temporal dimension as a coding dimension by the DNF. Similarly to biological PN, the activity of excitatory neurons in the model can be both phase-locked to different cycles of oscillations which remind local field potential (LFP), and nevertheless exhibit dynamic behavior complex enough to be the basis of spatio-temporal codes.

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).

Neuronal morphology data bases: morphological noise and assesment of data quality.

For technical, instrumental and operator-related reasons, three-dimensional reconstructions of neurons obtained from intracellularly stained neuronal pieces scattered in serial sections are blurred by some morphological noise. This noise may strongly invalidate conclusions drawn from models built using the three-dimensional reconstructions and it must be taken into account when retrieving digitized neurons from available databases. We analyse the main generating sources of the noise and its consequences for the 'quality' of the data. We provide tools for detecting and evaluating the noise in any database providing sufficient information is given in the database. We propose a unified format for submitting data and a new neuron viewer/editor to analyse the digitized neurons with our tools.