Search PubMed⌕ Search

Biomedical subjects

A M Aertsen

Publications and source records attributed to A M Aertsen.

At least 19 recordsLinked to original sources
PubMed1993

Rhythmic and patterned neuronal firing in visual cortex.

We present evidence from multi-electrode recordings that the stimulus driven activity in a region of monkey visual cortex displays synchronous, oscillatory behaviour with temporally changing patterns of firing. The spatial-temporal patterns are dependent on cortical layer and on visual stimulus, 'build up and die down' with a period of 70-100 msec, and cannot be factorized into separate spatial and temporal components. This finding supports neural network models which explicitly incorporate spatial-temporal structure, ruling against a purely spatial code for information processing. The new type of analysis presented here is also suitable for direct on-line interactive analysis during multi-electrode recording experiments. Further, it could be modified for use in human EEG or evoked response recordings.

Action Potentials↗
PubMed1989

A low-cost single-board solution for real-time, unsupervised waveform classification of multineuron recordings.

We describe a low-cost single-board system for unsupervised, real-time spike sorting of recordings from a number of neurons on a single microelectrode. The maximum number of spike classes depends on the quality of the recording; it will typically be between 2 and 5. The spike sorter communicates with a conventional microcomputer through a standard serial port (RS232). For typical firing rates as measured in the mammalian central nervous system, this set-up will accommodate up to some 10 parallel spike sorters for as many separate microelectrodes.

Algorithms↗
PubMed1989

Dynamics of neuronal firing correlation: modulation of "effective connectivity".

1. We reexamine the possibilities for analyzing and interpreting the time course of correlation in spike trains simultaneously and separably recorded from two neurons. 2. We develop procedures to quantify and properly normalize the classical joint peristimulus time scatter diagram. These allow separation of the "raw" correlation into components caused by direct stimulus modulations of the single-neuron firing rates and those caused by various types of interaction between the two neurons. 3. A newly developed significance test ("surprise") is applied to evaluate such inferences. 4. Application of the new procedures to simulated spike trains allowed the recovery of the known circuitry. In particular, it proved possible to recover fast stimulus-locked modulations of "effective connectivity," even if they were masked by strong direct stimulus modulations of individual firing rates. These procedures thus present a clearly superior alternative to the commonly used "shift predictor." 5. Adopting a model-based approach, we generalize the classical measures for quantifying a direct interneuronal connection ("efficacy" and "contribution") to include possible stimulus-locked time variations. 6. Application of the new procedures to real spike trains from several different preparations showed that fast stimulus-locked modulations of "effective connectivity" also occur for real neurons.

Animals↗
PubMed1988

On the significance of correlations among neuronal spike trains.

We consider several measures for the correlation of firing activity among different neurons, based on coincidence counts obtained from simultaneously recorded spike trains. We obtain explicit formulae for the probability distributions of these measures. This allows an exact, quantitative assessment of significance levels, and thus a comparison of data obtained in different experimental paradigms. In particular it is possible to compare stimulus-locked, and therefore time dependent correlations for different stimuli and also for different times relative to stimulus onset. This allows to separate purely stimulus-induced correlation from intrinsic interneuronal correlation. It further allows investigation of the dynamic characteristics of the interneuronal correlation. For the display of significance levels or the corresponding probabilities we propose a logarithmic measure, called "surprise".

Animals↗
PubMed1986

Directional hearing in the grassfrog (Rana temporaria L.). II. Acoustics and modelling of the auditory periphery.

In an earlier paper (Vlaming et al., 1984) we reported on optical measurements (laser-doppler interferometry) of the vibrations characteristics of the grassfrog's tympanic membrane. In the present paper these measurements were extended to include acoustic measurements concerning the functional role of the mouth cavity in frog hearing. Based on these measurements a model of the frog's acoustic periphery, consisting of three coupled linear oscillators with three entrance ports for sound, was developed and analyzed mathematically to give the various relevant transfer functions. The model is characterized by six parameters, all of which could be estimated from the available experimental data. For frequencies up to some 1500 Hz the model adequately describes the experimental data, both our own and earlier, seemingly conflicting data in the literature. For higher frequencies deviations occur, possibly due to nonuniform vibrations of the membranes. The model was used to evaluate the monaural directional sensitivity of the frog under free-field stimulation. Essentially it behaves as a combined pressure-gradient receiver, with highly frequency-dependent directional sensitivity. Directional sensitivity of the tympanic membrane could be modulated drastically by changing the resonance properties of the mouth cavity, without affecting the intrinsic membrane properties. This, theoretically, allows the frog to manipulate its direction sensitivity by actively tuning the volume of its mouth cavity. In order to account for discrepancies with known properties of low-frequency auditory nerve fibers an additional, extra-tympanic channel was included into the model. The extended model, the second-channel possibly involving the opercularis complex, provides a good quantitative fit to the available data on tympanic membrane movement as well as auditory nerve activity. Finally, the model enables to simulate a (moving) sound source in space, while stimulating the frog via closed couplers.

Acoustics↗
PubMed1985

Evaluation of neuronal connectivity: sensitivity of cross-correlation.

Cross-correlation analysis of separable multi-unit activity is one of the most commonly used methods to investigate connectivity in neural networks. In the course of development of new analysis techniques which go beyond the study of pairs or triplets of neurons, the need arose for a simple yet versatile simulator to generate spike trains from networks of specified structure. The present paper describes such a simulator and presents some examples of its performance as analyzed by cross-correlation. We noted a distinct asymmetry in the sensitivity of cross-correlation for the presence of excitatory vs inhibitory connections. A theoretical analysis is given from which quantitative criteria for detectability were derived. It appears that indeed the sensitivity of cross-correlation for excitation is larger to an order of magnitude than it is for inhibition. Possible consequences of this finding are indicated, and the relation to commonly used methods to measure strength of interaction are discussed.

Action Potentials↗
PubMed1985

Representation of cooperative firing activity among simultaneously recorded neurons.

Simultaneous and separable extracellular recording of substantial populations of neurons under chronic and behavioral conditions is becoming experimentally feasible. We have recently described a conceptual transformation of such multiple spike train data that allows the experimenter to analyze the entire network of observed neurons as an entity rather than as a summation of neuron pairs. The basic transformation represents each of N neurons as a particle in an N-space. Each particle is given a "charge" that is related to the spike train of the corresponding neuron. The resulting forces on the N particles cause aggregation of those particles that represent neurons with time-related firing. The present paper extends the visualization and possibilities of this way of analyzing properties of neuronal assemblies. Data are taken from computer-simulated neuronal networks in order to provide known properties. We demonstrate projection of particle positions from the N space to a plane. Under the right conditions the spatial arrangement of the particles forms a Venn diagram of functional relationships in the entire neural network. We introduce revised force rules in the transformation that allow detection and study of inhibitory connections among the observed neurons. Sensitivity is lower than for excitatory connections. We introduce revised "charge" rules that improve "signal-to-noise" properties and in addition allow inference of directed connectivity. The original transformation only allows identification of neurons with time-related firing. The two-charge transformation allows explicit identification of presynaptic and postsynaptic neurons. Finally we examine sensitivity of the transformation to individual and near-coincident firing rates. Some criteria are presented for choice of charge normalization rules in the transformation.

Action Potentials↗
PubMed1984

Directional hearing in the grass frog (Rana temporaria L.): I. Mechanical vibrations of tympanic membrane.

The vibration characteristics (amplitude and phase as a function of frequency) of the tympanic membrane in the grass frog were measured using a laser-doppler velocity meter. It was tested to what extent the frog's acoustic system behaves as a pressure gradient receiver. This might clarify how the frog localizes sound. Using a closed sound system the membrane was stimulated at three different entrances: in front of the membrane, at the contralateral ear and from inside the mouth. A combination of these can describe the motion of the membrane under free field conditions. It is found that the sound entrance from inside the mouth will give almost identical vibration characteristics as stimulation in front of the membrane. This can yield a perfect gradient receiver mechanism, when the frog opens its mouth. It is doubted however whether the frog in nature needs to open its mouth for localization of sound. With mouth closed the effectiveness of the gradient receiver will be determined by the transmission characteristics of sound across the tissues of the mouth. The entrance of sound via the contralateral ear is only effective at frequencies between 800 and 1600 Hz. At those frequencies crosstalk between the membranes is however not more than -4 to -8 dB. This is subject to changes in the acoustic properties of the mouth cavity and can possibly be altered by the frog in free nature.

Acoustic Stimulation↗
PubMed1983

Stimulus dependent neural correlations in the auditory midbrain of the grassfrog (Rana temporaria L.).

Few-unit recordings were obtained using metal microelectrodes. Separation into single-unit spike trains was based on differences in spike amplitude and spike waveform. For that purpose a hardware microprocessor based spike waveform analyser was designed and built. Spikes are filtered by four matched filters and filter outputs at the moments of spike occurrence are read by a computer and used for off-line separation and spike waveform reconstruction. Thirty-one double unit recordings were obtained and correlation between the separated spike trains was determined. After stimulus correction correlation remained in only 8 of the double unit records. It appeared that in most cases this neural correlation was stimulus dependent. Continuous noise stimulation resulted in the strongest neural correlation remaining after correction for stimulus coupling, stimulation with 48 ms duration tonepips presented once per second generally did not result in a significant neural correlation after the correction procedure for stimulus lock. The usefulness of the additive model for neural correlation and the correction procedure based thereupon is discussed.

Acoustic Stimulation↗
PubMed1983

Quantitative characterisation procedure for auditory neurons based on the spectro-temporal receptive field.

Studies dealing with auditory information processing often present the dynamic spectrum of the sound stimulus (sonogram) in addition to the stimulus waveform. The sonogram, presenting the spectral and temporal properties of the sound in a combined way, reflects properties that are assumed relevant in central information processing. For 12 neurons recorded from the midbrain of the grass frog the sonogram of a Gaussian wide-band noise stimulus was correlated with the output of the neuron to that noise. From this input-output correlogram the spectro-temporal receptive field (STRF) was calculated. The STRF reflects those spectral and temporal properties of the stimulus that influence the firing probability of the neuron. A quantitative procedure was developed to calculate the neuron's response as far as it could be derived from the STRF. This procedure basically consisted of a convolution between STRF and the sonogram of the stimulus followed by a summation over the various frequency bands. In this way it proved possible to estimate to what extent the STRF characterised the neuron's firing behaviour. Heuristic approaches, in which the neuron was modelled to a parallel series of band-pass filters, a summator and a static nonlinearity, representing a spike-generating mechanism, resulted in a considerable improvement of the characterisation.

Animals↗
PubMed1983

Prediction of the responses of auditory neurons in the midbrain of the grass frog based on the spectro-temporal receptive field.

The spectro-temporal receptive field (STRF) of an auditory neuron represents those characteristics of the sound stimulus in both the time and frequency domain that affect the firing probability of the neuron. The STRF is determined under stationary stimulus conditions for Gaussian wide-band noise. It has been demonstrated that for some neurons the response to that noise could to a considerable extent be derived from the STRF. In the present study the usefulness of the STRF is tested to predict responses to other stimuli such as noise with different frequency content and to species-specific vocalisations. It appears that the predicted response to vocalisations is at best in qualitative agreement with the actual response.

Adaptation, Physiological↗
PubMed1982

Spectro-temporal characteristics of single units in the auditory midbrain of the lightly anaesthetised grass frog (Rana temporaria L.) investigated with tonal stimuli.

Responses were obtained from 112 auditory neurons in the midbrain of the grass frog in response to sequences of tones. Their spectro-temporal sensitivities (STS) were determined by a second-order cross-correlation technique. For the majority of units the shape of their action potentials, the degree of timelock to the stimulus and the recording sites were obtained. Two stages of information processing could be distinguished. One was characterized by short latencies (less than 30 ms), strong timelock to the stimulus and many of these units had axon-like action potential waveforms. They were localised in the ventral part of the principal nucleus from the torus semicircularis and in the transition region between laminar and principal nucleus. The other stage comprised units, found all over the torus, with longer latencies, and a weaker timelock to the stimulus. Several units which were predominantly found in the central part of the torus, especially the magnocellular nucleus, showed a broad or multiple STS. Within the principal nucleus a weak tonotopy was found, the dorsoposterior part being sensitive to lower frequencies, the ventroanterior part to the higher frequencies. Binaural-interaction properties are discussed with respect to the eardrum coupling through the mouth cavity. An organisational plan for the torus semicircularis is proposed.

Acoustic Stimulation↗
PubMed1981

The spectro-temporal receptive field. A functional characteristic of auditory neurons.

The Spectro-Temporal Receptive Field (STRF) of an auditory neuron has been introduced experimentally on the base of the average spectro-temporal structure of the acoustic stimuli which precede the occurrence of action potentials (Aertsen et al., 1980, 1981). In the present paper the STRF is considered in the general framework of nonlinear system theory, especially in the form of the Volterra integral representation. The STRF is proposed to be formally identified with a linear functional of the second order Volterra kernel. The experimental determination of the STRF leads to a formulation in terms of the Wiener expansion where the kernels can be identified by evaluation of the system's input-output correlations. For a Gaussian stimulus ensemble and a nonlinear system with no even order contributions of order higher than two, it is shown that the second order cross correlation of stimulus and response, normalized with respect to the spectral contents of the stimulus ensemble, leads to the stimulus-invariant spectro-temporal receptive field. The investigation of stimulus-invariance of the STRF for more general nonlinear systems and for stimulus ensembles which can be generated by nonlinear transformations of Gaussian noise involve the evaluation of higher order stimulus-response correlation functions.

Acoustic Stimulation↗
PubMed1981

A comparison of the spectro-temporal sensitivity of auditory neurons to tonal and natural stimuli.

The spectro-temporal sensitivity of auditory neurons has been investigated experimentally by averaging the spectrograms of stimuli preceding the occurrence of action potentials or neural events ( the APES : Aertsen et al., 1980, 1981). The properties of the stimulus ensemble are contained in this measure of neural selectivity. The spectro-temporal receptive field (STRF) has been proposed as a theoretical concept which should give a stimulus-invariant representation of the second order characteristics of the neuron's system function (Aertsen and Johannesma, 1981). The present paper investigates the relation between the experimental and the theoretical description of the neuron's spectro-temporal sensitivity for sound. The aim is to derive a formally based stimulus-normalization procedure for the results of the experimental averaging procedure. Under particular assumptions, regarding both the neuron and the stimulus ensemble, an integral equation connecting the APES and the STRF is derived. This integral expression enables to calculate the APES from the STRF by taking into account the stimulus spectral composition and the characteristics of the spectrogram analysis. The inverse relation, i.e. starting from the experimental results and by application of a formal normalization procedure arriving at the theoretical STRF, is effectively hindered by the nature of the spectrogram analysis. An approximative "normalization" procedure, based on intuitive manipulation of the integral equation, has been applied to a number of single unit recordings from the grassfrog's auditory midbrain area to tonal and natural stimulus ensembles. The results indicate tha spectrogram analysis, while being a useful real-time tool in investigating the spectro-temporal transfer properties of auditory neurons, shows fundamental shortcomings for a theoretical treatment of the questions of interest.

Acoustic Stimulation↗
PubMed1981

Spectro-temporal receptive fields of auditory neurons in the grassfrog. III. Analysis of the stimulus-event relation for natural stimuli.

The stimulus-event relation of single units in the auditory midbrain area, the torus semicircularis, of the anaesthetized grassfrog (Rana temporaria L.) during stimulation with a wide ensemble of natural stimuli, was analysed using first and second order statistical analysis techniques. The average stimulus preceding the occurrence of action potentials, in general, did not prove to give very informative results. The second order procedure consisted in the determination of the average dynamic power spectrum of the pre-event stimuli, following procedures as described elsewhere (Aertsen and Johannesma, 1980' Aertsen et al., 1980). The outcome of this analysis was filtered with the overall power spectrum of the complete stimulus ensemble in order to correct for its non-uniform spectral composition. The "stimulus-filtered" average pre-event dynamic spectrum gives a first indication of the "spectro-temporal receptive field" of a neuron under natural stimulus conditions. Results for a limited number of recordings are presented and, globally, compared to the outcome of an analogous analysis of experiments with tonal stimuli.

Acoustic Stimulation↗
PubMed1981

Spectro-temporal characterization of auditory neurons: redundant or necessary.

For neurons in the auditory midbrain of the grass frog the use of a combined spectro-temporal characterization has been evaluated against the separate characterizations of frequency-sensitivity and temporal response properties. By factoring the joint density function of stimulus intensity, I (f, t), preceding a spike, into two marginal density functions I1(f) and I2(t) one may under the assumption of statistical independence reconstruct the joint density by multiplication: I1(f).I2(t). The reconstructed I(f, t) is compared to the original I(f, t) for 83 neurons: in 23% thereof the I(f, t) appeared to be vastly different from I(f, t). These units appeared to be located dominantly in the ventral parts of the auditory midbrain and had a latency exceeding 30 ms. On the basis of the action-potential wave forms the absence of non-separable I(f, t) in the incoming nerve fiber population is concluded. A spectro-temporal characterization of auditory neurons seems mandatory for investigations in and central from the auditory midbrain.

Action Potentials↗
PubMed1981

Spectro-temporal characteristics of single units in the auditory midbrain of the lightly anaesthetised grass frog (Rana temporaria L) investigated with noise stimuli.

About 30% of the auditory units in the midbrain of the lightly anaesthetised grass frog respond in a sustained way to stationary pseudorandom noise. This response is described by the spectro-temporal receptive field (STRF), the regions in the spectro-temporal domain where the average second-order functional of those parts of the stimulus ensemble that precede the action potentials differ from the average second-order functional of the stimulus ensemble. By means of the STRF frequency selectivity, postactivation suppression and lateral suppression can quantitatively be studied under one and the same experimental condition. Auditory units that respond to stationary noise are localised in those parts of the torus where fibres enter from the olivary nucleus. They are characterised by relatively short latencies to tones and probably represent the first information-processing stage in the torus semicircularis.

Acoustic Stimulation↗