Interspike Interval Histograms Research Articles

Altered neural encoding of vowels in noise does not affect behavioral vowel discrimination in gerbils with age-related hearing loss.

Understanding speech in a noisy environment, as opposed to speech in quiet, becomes increasingly more difficult with increasing age. Using the quiet-aged gerbil, we studied the effects of aging on speech-in-noise processing. Specifically, behavioral vowel discrimination and the encoding of these vowels by single auditory-nerve fibers were compared, to elucidate some of the underlying mechanisms of age-related speech-in-noise perception deficits. Young-adult and quiet-aged Mongolian gerbils, of either sex, were trained to discriminate a deviant naturally-spoken vowel in a sequence of vowel standards against a speech-like background noise. In addition, we recorded responses from single auditory-nerve fibers of young-adult and quiet-aged gerbils while presenting the same speech stimuli. Behavioral vowel discrimination was not significantly affected by aging. For both young-adult and quiet-aged gerbils, the behavioral discrimination between /eː/ and /iː/ was more difficult to make than /eː/ vs. /aː/ or /iː/ vs. /aː/, as evidenced by longer response times and lower d' values. In young-adults, spike timing-based vowel discrimination agreed with the behavioral vowel discrimination, while in quiet-aged gerbils it did not. Paradoxically, discrimination between vowels based on temporal responses was enhanced in aged gerbils for all vowel comparisons. Representation schemes, based on the spectrum of the inter-spike interval histogram, revealed stronger encoding of both the fundamental and the lower formant frequencies in fibers of quiet-aged gerbils, but no qualitative changes in vowel encoding. Elevated thresholds in combination with a fixed stimulus level, i.e., lower sensation levels of the stimuli for old individuals, can explain the enhanced temporal coding of the vowels in noise. These results suggest that the altered auditory-nerve discrimination metrics in old gerbils may mask age-related deterioration in the central (auditory) system to the extent that behavioral vowel discrimination matches that of the young adults.

Modeling Pitch Perception With an Active Auditory Model Extended by Octopus Cells.

Pitch is an essential category for musical sensations. Models of pitch perception are vividly discussed up to date. Most of them rely on definitions of mathematical methods in the spectral or temporal domain. Our proposed pitch perception model is composed of an active auditory model extended by octopus cells. The active auditory model is the same as used in the Stimulation based on Auditory Modeling (SAM), a successful cochlear implant sound processing strategy extended here by modeling the functional behavior of the octopus cells in the ventral cochlear nucleus and by modeling their connections to the auditory nerve fibers (ANFs). The neurophysiological parameterization of the extended model is fully described in the time domain. The model is based on latency-phase en- and decoding as octopus cells are latency-phase rectifiers in their local receptive fields. Pitch is ubiquitously represented by cascaded firing sweeps of octopus cells. Based on the firing patterns of octopus cells, inter-spike interval histograms can be aggregated, in which the place of the global maximum is assumed to encode the pitch.

Towards systematic, data-driven validation of a collaborative, multi-scale model of Caenorhabditis elegans.

The OpenWorm Project is an international open-source collaboration to create a multi-scale model of the organism Caenorhabditis elegans At each scale, including subcellular, cellular, network and behaviour, this project employs one or more computational models that aim to recapitulate the corresponding biological system at that scale. This requires that the simulated behaviour of each model be compared with experimental data both as the model is continuously refined and as new experimental data become available. Here we report the use of SciUnit, a software framework for model validation, to attempt to achieve these goals. During project development, each model is continuously subjected to data-driven 'unit tests' that quantitatively summarize model-data agreement, identifying modelling progress and highlighting particular aspects of each model that fail to adequately reproduce known features of the biological organism and its components. This workflow is publicly visible via both GitHub and a web application and accepts community contributions to ensure that modelling goals are transparent and well-informed.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.

Ion shot noise in Hodgkin–Huxley neurons

Ion shot noise, the noise associated to the random passage of ions across the cell membrane, is studied by means of a stochastic model based on the Hodgkin–Huxley equations, which includes gating channels for sodium and potassium cations and leakage channels through the biological membrane. Apart from shot noise, other sources such as extrinsic and channel noise are taken into account. Ion shot noise, of increasing influence for smaller membrane patch sizes S, can lead to the emergence of action potentials in the membrane voltage in the presence of sinusoidal excitation currents below the threshold for the onset of spikes. The spiking activity in the presence of noise has been analyzed in terms of the coefficient of variation CV, the inter-spike interval histogram, the spectrum of membrane voltage fluctuations and the signal-to-noise ratio SNR. CV shows improved coherence in the sequence of randomly generated spikes due to the presence of shot noise. The voltage noise spectra show a common signature of the presence of spikes under different operating conditions, even in the absence of excitation. The SNR exhibits intrinsic stochastic resonance when varying S. For a sinusoidal excitation current with amplitude 1.5 μA/cm2 and frequency 50 Hz, the SNR presents optimal values around 0.2 μm2. When considering the presence of ambient noise in the excitation current, extrinsic stochastic resonance is found for S > 0.6 μm2.

A Line of MEA Signal Analysis Methods for Human Stem Cell-Derived and Other Dynamic Neuronal Cultures

A Line of MEA Signal Analysis Methods for Human Stem Cell-Derived and Other Dynamic Neuronal Cultures

Analysis of a purely conductance-based stochastic nerve fibre model as applied to compound models of populations of human auditory nerve fibres used in cochlear implant simulations.

The study presents the application of a purely conductance-based stochastic nerve fibre model to human auditory nerve fibres within finite element volume conduction models of a semi-generic head and user-specific cochleae. The stochastic, threshold and temporal characteristics of the human model are compared and successfully validated against physiological feline results with the application of a mono-polar, bi-phasic, cathodic first stimulus. Stochastic characteristics validated include: (i) the log(Relative Spread) versus log(fibre diameter) distribution for the discharge probability versus stimulus intensity plots and (ii) the required exponential membrane noise versus transmembrane voltage distribution. Intra-user, and to a lesser degree inter-user, comparisons are made with respect to threshold and dynamic range at short and long pulse widths for full versus degenerate single fibres as well as for populations of degenerate fibres of a single user having distributed and aligned somas with varying and equal diameters. Temporal characteristics validated through application of different stimulus pulse rates and different stimulus intensities include: (i) discharge rate, latency and latency standard deviation versus stimulus intensity, (ii) period histograms and (iii) interspike interval histograms. Although the stochastic population model does not reduce the modelled single deterministic fibre threshold, the simulated stochastic and temporal characteristics show that it could be used in future studies to model user-specific temporally encoded information, which influences the speech perception of CI users.

Manifestation of peripherial coding in the effect of increasing loudness and enhanced discrimination of the intensity of tone bursts before and after tone burst noise

To find the possible reasons for the midlevel elevation of the Weber fraction in intensity discrimination of a tone burst, a comparison was performed for the complementary distributions of spike activity of an ensemble of space nerves, such as the distribution of time instants when spikes occur, the distribution of interspike intervals, and the autocorrelation function. The distribution properties were detected in a poststimulus histogram, an interspike interval histogram, and an autocorrelation histogram—all obtained from the reaction of an ensemble of model space nerves in response to an auditory noise burst–useful tone burst complex. Two configurations were used: in the first, the peak amplitude of the tone burst was varied and the noise amplitude was fixed; in the other, the tone burst amplitude was fixed and the noise amplitude was varied. Noise could precede or follow the tone burst. The noise and tone burst durations, as well as the interval between them, was 4 kHz and corresponded to the characteristic frequencies of the model space nerves. The profiles of all the mentioned histograms had two maxima. The values and the positions of the maxima in the poststimulus histogram corresponded to the amplitudes and mutual time position of the noise and the tone burst. The maximum that occurred in response to the tone burst action could be a basis for the formation of the loudness of the latter (explicit loudness). However, the positions of the maxima in the other two histograms did not depend on the positions of tone bursts and noise in the combinations. The first maximum fell in short intervals and united intervals corresponding to the noise and tone burst durations. The second maximum fell in intervals corresponding to a tone burst delay with respect to noise, and its value was proportional to the noise amplitude or tone burst amplitude that was smaller in the complex. An increase in tone burst or noise amplitudes was caused by nonlinear variations in the two maxima and the ratio between them. The size of the first maximum in the of interspike interval distribution could be the basis for the formation of the loudness of the masked tone burst (implicit loudness), and the size of the second maximum, for the formation of intensity in the periodicity pitch of the complex. The auditory effect of the midlevel enhancement of tone burst loudness could be the result of variations in the implicit tone burst loudness caused by variations in tone-burst or noise intensity. The reason for the enhancement of the Weber fraction could be competitive interaction between such subjective qualities as explicit and implicit tone-burst loudness and the intensity of the periodicity pitch of the complex.

Dynamics and Physiological Roles of Stochastic Firing Patterns Near Bifurcation Points

Different stochastic neural firing patterns or rhythms that appeared near polarization or depolarization resting states were observed in biological experiments on three nervous systems, and closely matched those simulated near bifurcation points between stable equilibrium point and limit cycle in a theoretical model with noise. The distinct dynamics of spike trains and interspike interval histogram (ISIH) of these stochastic rhythms were identified and found to build a relationship to the coexisting behaviors or fixed firing frequency of four different types of bifurcations. Furthermore, noise evokes coherence resonances near bifurcation points and plays important roles in enhancing information. The stochastic rhythms corresponding to Hopf bifurcation points with fixed firing frequency exhibited stronger coherence degree and a sharper peak in the power spectrum of the spike trains than those corresponding to saddle-node bifurcation points without fixed firing frequency. Moreover, the stochastic firing patterns changed to a depolarization resting state as the extracellular potassium concentration increased for the injured nerve fiber related to pathological pain or static blood pressure level increased for aortic depressor nerve fiber, and firing frequency decreased, which were different from the physiological viewpoint that firing frequency increased with increasing pressure level or potassium concentration. This shows that rhythms or firing patterns can reflect pressure or ion concentration information related to pathological pain information. Our results present the dynamics of stochastic firing patterns near bifurcation points, which are helpful for the identification of both dynamics and physiological roles of complex neural firing patterns or rhythms, and the roles of noise.

Impact of Partial Time Delay on Temporal Dynamics of Watts–Strogatz Small-World Neuronal Networks

In this paper, we mainly discuss effects of partial time delay on temporal dynamics of Watts–Strogatz (WS) small-world neuronal networks by controlling two parameters. One is the time delay [Formula: see text] and the other is the probability of partial time delay [Formula: see text]. Temporal dynamics of WS small-world neuronal networks are discussed with the aid of temporal coherence and mean firing rate. With the obtained simulation results, it is revealed that for small time delay [Formula: see text], the probability [Formula: see text] could weaken temporal coherence and increase mean firing rate of neuronal networks, which indicates that it could improve neuronal firings of the neuronal networks while destroying firing regularity. For large time delay [Formula: see text], temporal coherence and mean firing rate do not have great changes with respect to [Formula: see text]. Time delay [Formula: see text] always has great influence on both temporal coherence and mean firing rate no matter what is the value of [Formula: see text]. Moreover, with the analysis of spike trains and histograms of interspike intervals of neurons inside neuronal networks, it is found that the effects of partial time delays on temporal coherence and mean firing rate could be the result of locking between the period of neuronal firing activities and the value of time delay [Formula: see text]. In brief, partial time delay could have great influence on temporal dynamics of the neuronal networks.

Network-Wide Adaptive Burst Detection Depicts Neuronal Activity with Improved Accuracy.

Neuronal networks are often characterized by their spiking and bursting statistics. Previously, we introduced an adaptive burst analysis method which enhances the analysis power for neuronal networks with highly varying firing dynamics. The adaptation is based on single channels analyzing each element of a network separately. Such kind of analysis was adequate for the assessment of local behavior, where the analysis focuses on the neuronal activity in the vicinity of a single electrode. However, the assessment of the whole network may be hampered, if parts of the network are analyzed using different rules. Here, we test how using multiple channels and measurement time points affect adaptive burst detection. The main emphasis is, if network-wide adaptive burst detection can provide new insights into the assessment of network activity. Therefore, we propose a modification to the previously introduced inter-spike interval (ISI) histogram based cumulative moving average (CMA) algorithm to analyze multiple spike trains simultaneously. The network size can be freely defined, e.g., to include all the electrodes in a microelectrode array (MEA) recording. Additionally, the method can be applied on a series of measurements on the same network to pool the data for statistical analysis. Firstly, we apply both the original CMA-algorithm and our proposed network-wide CMA-algorithm on artificial spike trains to investigate how the modification changes the burst detection. Thereafter, we use the algorithms on MEA data of spontaneously active chemically manipulated in vitro rat cortical networks. Moreover, we compare the synchrony of the detected bursts introducing a new burst synchrony measure. Finally, we demonstrate how the bursting statistics can be used to classify networks by applying k-means clustering to the bursting statistics. The results show that the proposed network wide adaptive burst detection provides a method to unify the burst definition in the whole network and thus improves the assessment and classification of the neuronal activity, e.g., the effects of different pharmaceuticals. The results indicate that the novel method is adaptive enough to be usable on networks with different dynamics, and it is especially feasible when comparing the behavior of differently spiking networks, for example in developing networks.

Distribution of transition times in a stochastic model of excitable cell: Insights into the cell-intrinsic mechanisms of randomness in neuronal interspike intervals.

In this paper, we develop an analytical approach to studying random patterns of activity in excitable cells. Our analytical approach uses a two-state stochastic model of excitable system based on the electrophysiological properties of refractoriness and restitution, which characterize cell recovery after excitation. By applying the notion of probability density flux, we derive the distributions of transition times between states and the distribution of interspike interval (ISI) durations for a constant applied stimulus. The derived ISI distribution is unimodal and, provided that the time spent in the excited state is constant, can be approximated by a Rayleigh peak followed by an exponential tail. We then explore the role of the model parameters in determining the shape of the derived distributions and the ISI coefficient of variation. Finally, we use our analytical results to study simulation results from the stochastic Morris-Lecar neuron and from a three-state extension of the proposed stochastic model, which is capable of reproducing multimodal ISI histograms.

Effect of support deprivation on the order of motor unit recruitment

Motor unit (MU) activity in the leg extensors has been tested by maintenance of a small isometric effort under the conditions of support deprivation enabled by dry immersion with simultaneous mechanic stimulation of the foot support zones. The analysis of MU interspike interval (ISI) histograms in the heads of two leg extensors (m. soleus and m. gastrocnemius lat.) revealed that the order of MU recruitment is extremely dependent on the support input activity. In immersion, the order of MU recruitment was rear-ranged during the isometric effort maintenance test, which revealed weaker involvement of small tonic MUs. Large MUs with longer ISIs (up to 260 ms) and high variability replaced small tonic MUs with relatively short ISIs (100 ms) and low variability. Daily support stimulation under the conditions of immersion was favorable for maintaining the normal pattern of MU recruitment.

Autaptic pacemaker mediated propagation of weak rhythmic activity across small-world neuronal networks

We study the effects of an autapse, which is mathematically described as a self-feedback loop, on the propagation of weak, localized pacemaker activity across a Newman–Watts small-world network consisting of stochastic Hodgkin–Huxley neurons. We consider that only the pacemaker neuron, which is stimulated by a subthreshold periodic signal, has an electrical autapse that is characterized by a coupling strength and a delay time. We focus on the impact of the coupling strength, the network structure, the properties of the weak periodic stimulus, and the properties of the autapse on the transmission of localized pacemaker activity. Obtained results indicate the existence of optimal channel noise intensity for the propagation of the localized rhythm. Under optimal conditions, the autapse can significantly improve the propagation of pacemaker activity, but only for a specific range of the autaptic coupling strength. Moreover, the autaptic delay time has to be equal to the intrinsic oscillation period of the Hodgkin–Huxley neuron or its integer multiples. We analyze the inter-spike interval histogram and show that the autapse enhances or suppresses the propagation of the localized rhythm by increasing or decreasing the phase locking between the spiking of the pacemaker neuron and the weak periodic signal. In particular, when the autaptic delay time is equal to the intrinsic period of oscillations an optimal phase locking takes place, resulting in a dominant time scale of the spiking activity. We also investigate the effects of the network structure and the coupling strength on the propagation of pacemaker activity. We find that there exist an optimal coupling strength and an optimal network structure that together warrant an optimal propagation of the localized rhythm.

Spontaneous activity does not predict morphological type in cerebellar interneurons.

The effort to determine morphological and anatomically defined neuronal characteristics from extracellularly recorded physiological signatures has been attempted with varying success in different brain areas. Recent studies have attempted such classification of cerebellar interneurons (CINs) based on statistical measures of spontaneous activity. Previously, such efforts in different brain areas have used supervised clustering methods based on standard parameterizations of spontaneous interspike interval (ISI) histograms. We worried that this might bias researchers toward positive identification results and decided to take a different approach. We recorded CINs from anesthetized cats. We used unsupervised clustering methods applied to a nonparametric representation of the ISI histograms to identify groups of CINs with similar spontaneous activity and then asked how these groups map onto different cell types. Our approach was a fuzzy C-means clustering algorithm applied to the Kullbach-Leibler distances between ISI histograms. We found that there is, in fact, a natural clustering of the spontaneous activity of CINs into six groups but that there was no relationship between this clustering and the standard morphologically defined cell types. These results proved robust when generalization was tested to completely new datasets, including datasets recorded under different anesthesia conditions and in different laboratories and different species (rats). Our results suggest the importance of an unsupervised approach in categorizing neurons according to their extracellular activity. Indeed, a reexamination of such categorization efforts throughout the brain may be necessary. One important open question is that of functional differences of our six spontaneously defined clusters during actual behavior.

Delayed feedback and detection of weak periodic signals in a stochastic Hodgkin–Huxley neuron

We study the effect of the delayed feedback loop on the weak periodic signal detection performance of a stochastic Hodgkin–Huxley neuron. We consider an electrical autapse characterized by its coupling strength and delay time. The stochastic Hodgkin–Huxley neuron exhibits subthreshold oscillations, and thus has an intrinsic time scale with the subthreshold oscillations. Therefore, we investigate the interplay of the subthreshold oscillations, coupling strength and delay time on the weak periodic signal detection. Results indicate that the delayed feedback either enhances or suppresses the weak signal detection depending on its parameters, when compared to that without the feedback. The delayed feedback augments the weak periodic signal detection for the optimal values of the intrinsic noise and the coupling strength when the delay time is close to the integer multiples of the period of the intrinsic oscillations, due to the multiple resonance among the weak signal, the intrinsic oscillations, and the delayed feedback.We analyze the interspike interval histograms and show that the delayed feedback enhances or suppresses the weak periodic signal detection by increasing or decreasing the phase locking (synchronization) between the spiking and the weak periodic signal. We also show that an optimal phase locking is obtained when the delay time is close to the period of the intrinsic oscillations, leading a single dominant time scale in the spike trains.

The ISI distribution of the stochastic Hodgkin-Huxley neuron.

The simulation of ion-channel noise has an important role in computational neuroscience. In recent years several approximate methods of carrying out this simulation have been published, based on stochastic differential equations, and all giving slightly different results. The obvious, and essential, question is: which method is the most accurate and which is most computationally efficient? Here we make a contribution to the answer. We compare interspike interval histograms from simulated data using four different approximate stochastic differential equation (SDE) models of the stochastic Hodgkin-Huxley neuron, as well as the exact Markov chain model simulated by the Gillespie algorithm. One of the recent SDE models is the same as the Kurtz approximation first published in 1978. All the models considered give similar ISI histograms over a wide range of deterministic and stochastic input. Three features of these histograms are an initial peak, followed by one or more bumps, and then an exponential tail. We explore how these features depend on deterministic input and on level of channel noise, and explain the results using the stochastic dynamics of the model. We conclude with a rough ranking of the four SDE models with respect to the similarity of their ISI histograms to the histogram of the exact Markov chain model.

Graded defragmentation of cortical neuronal firing during recovery of consciousness in rats

State-dependent neuronal firing patterns reflect changes in ongoing information processing and cortical function. A disruption of neuronal coordination has been suggested as the neural correlate of anesthesia. Here, we studied the temporal correlation patterns of ongoing spike activity, during a stepwise reduction of the volatile anesthetic desflurane, in the cerebral cortex of freely moving rats. We hypothesized that the recovery of consciousness from general anesthesia is accompanied by specific changes in the spatiotemporal pattern and correlation of neuronal activity. Sixty-four contact microelectrode arrays were chronically implanted in the primary visual cortex (contacts spanning 1.4-mm depth and 1.4-mm width) for recording of extracellular unit activity at four steady-state levels of anesthesia (8–2% desflurane) and wakefulness. Recovery of consciousness was defined as the regaining of the righting reflex (near 4%). High-intensity firing (HI) periods were segmented using a threshold (200-ms) representing the minimum in the neurons’ bimodal interspike interval histogram under anesthesia. We found that the HI periods were highly fragmented in deep anesthesia and gradually transformed to a near-continuous firing pattern at wakefulness. As the anesthetic was withdrawn, HI periods became longer and increasingly correlated among the units both locally and across remote recording sites. Paradoxically, in 4 of 8 animals, HI correlation was also high at the deepest level of anesthesia (8%) when local field potentials (LFP) were burst-suppressed. We conclude that recovery from desflurane anesthesia is accompanied by a graded defragmentation of neuronal activity in the cerebral cortex. Hypersynchrony during deep anesthesia is an exception that occurs only with LFP burst suppression.

Neurons of the A5 region are required for the tachycardia evoked by electrical stimulation of the hypothalamic defence area in anaesthetized rats

In order to assess the possible interactions between the pontine A5 region and the hypothalamic defence area (HDA), we have examined the pattern of double staining for c-Fos protein immunoreactivity (c-Fos-ir) and tyrosine hydroxylase, throughout the rostrocaudal extent of the A5 region in spontaneously breathing anaesthetized male Sprague-Dawley rats during electrical stimulation of the HDA. Activation of the HDA elicited a selective increase in c-Fos-ir with an ipsilateral predominance in catecholaminergic and non-catecholaminergic A5 somata (P < 0.001 in both cases). A second group of experiments was done to examine the importance of the A5 region in modulating the cardiorespiratory response evoked from the HDA. Cardiorespiratory changes were analysed in response to electrical stimulation of the HDA before and after ipsilateral microinjection of muscimol within the A5 region. Stimulation of the HDA evoked an inspiratory facilitatory response, consisting of an increase in respiratory rate (P < 0.001) due to a decrease in expiratory time (P < 0.01). The respiratory response was accompanied by a pressor response (P < 0.001) and tachycardia (P < 0.001). After muscimol microinjection within the A5 region, pressor and heart rate responses to HDA stimulation were reduced (P < 0.01 and P < 0.001, respectively). The respiratory response persisted unchanged. Finally, to confirm functional interactions between the HDA and the A5 region, extracellular recordings of putative A5 neurones were obtained during HDA stimulation. Seventy-five A5 cells were recorded, 35 of which were affected by the HDA (47%). These results indicate that neurones of the A5 region participate in the cardiovascular response evoked from the HDA. The possible mechanisms involved in these interactions are discussed.

Hippocampal CA1 pyramidal neurons exhibit type 1 phase-response curves and type 1 excitability

Phase-resetting properties of neurons determine their functionality as integrators (type 1) vs. resonators (type 2), as well as their synchronization tendencies. We introduce a novel, bias-correction method to estimate the infinitesimal phase-resetting curve (iPRC) and confirm type 1 excitability in hippocampal pyramidal CA1 neurons in vitro by two independent methods. First, PRCs evoked using depolarizing pulses consisted only of advances, consistent with type 1. Second, the frequency/current (f/I) plots showed no minimum frequency, again consistent with type 1. Type 1 excitability was also confirmed by the absence of a resonant peak in the interspike interval histograms derived from the f/I data. The PRC bias correction assumed that the distribution of noisy phase resetting is truncated, because an input cannot advance a spike to a point in time before the input (the causal limit) and successfully removed the statistical bias for delays in the null PRC in response to zero-magnitude input by computing the phase resetting as the mean of the untruncated distribution. The PRC for depolarization peaked at late phases and decreased to zero by the end of the cycle, whereas delays observed in response to hyperpolarization increased monotonically. The bias correction did not affect this difference in shape, which was due instead to the causal limit obscuring the iPRC for depolarization but not hyperpolarization. Our results suggest that weak periodic hyperpolarizing drive can theoretically entrain CA1 pyramidal neurons at any phase but that strong excitation will preferentially phase-lock them with zero time lag.

Short- and long-term unilateral 6-hydroxydopamine lesions in rats show different changes in characteristics of spontaneous firing of substantia nigra pars reticulata neurons

The unilateral 6-hydroxydopamine (6-OHDA) lesion of the medial forebrain bundle induces hemiparkinsonism in rats and is a well established animal model of Parkinson's disease. In this study, we assessed the spontaneous activity of substantia nigra pars reticulata (SNr) neurons in unilateral 6-OHDA- or sham-treated rats. Extracellular single cell recordings revealed a bilaterally decreased firing rate in short-term 6-OHDA-lesioned rats (8-10weeks post lesion) while no rate differences were evident in long-term lesioned animals (5-8months post lesion) in vivo under chloral hydrate anaesthesia. However, firing pattern of the SNr neurons (indicated by interspike interval (ISI) histogram parameters: coefficient of variation, skewness and kurtosis) was significantly altered only after long-term lesion: 53.8% of the recorded cells in the ipsilateral 6-OHDA-lesioned SNr fired in a bursting pattern (compared to 5.9-16.7% in contralateral SNr or sham controls). Additionally, behavioural effects of the lesion were assessed 4weeks post lesion by the forelimb adjusting stepping test. A decreased number of adjusting steps with the contralateral forepaw, as well as an increased performance with the ipsilateral paw was found for the 6-OHDA-lesioned rats as compared to sham controls. Furthermore, stepping values were negatively correlated with the ISI parameters after long-term lesion, while there were no correlations with the short-term groups. Firing rate was not correlated regardless of the time frame. In conclusion, long-term changes in firing pattern may represent a neuronal correlate of the 6-OHDA-induced hemiparkinsonism and may be useful for the interpretation of 6-OHDA-induced motor deficits and compensatory mechanisms as well.