Irregular Firing Research Articles

916 MHz electromagnetic field exposure affects rat behavior and hippocampal neuronal discharge.

Wistar rats were exposed to a 916 MHz, 10 W/m2 mobile phone electromagnetic field for 6 hours a day, 5 days a week. Average completion times in an eight-arm radial maze were longer in the exposed rats than control rats after 4–5 weeks of exposure. Error rates in the exposed rats were greater than the control rats at 6 weeks. Hippocampal neurons from the exposed rats showed irregular firing patterns during the experiment, and they exhibited decreased spiking activity 6–9 weeks compared with that after 2–5 weeks of exposure. These results indicate that 916 MHz electromagnetic fields influence learning and memory in rats during exposure, but long-term effects are not obvious.

Detection thresholds of macaque otolith afferents.

The vestibular system is our sixth sense and is important for spatial perception functions, yet the sensory detection and discrimination properties of vestibular neurons remain relatively unexplored. Here we have used signal detection theory to measure detection thresholds of otolith afferents using 1 Hz linear accelerations delivered along three cardinal axes. Direction detection thresholds were measured by comparing mean firing rates centered on response peak and trough (full-cycle thresholds) or by comparing peak/trough firing rates with spontaneous activity (half-cycle thresholds). Thresholds were similar for utricular and saccular afferents, as well as for lateral, fore/aft, and vertical motion directions. When computed along the preferred direction, full-cycle direction detection thresholds were 7.54 and 3.01 cm/s(2) for regular and irregular firing otolith afferents, respectively. Half-cycle thresholds were approximately double, with excitatory thresholds being half as large as inhibitory thresholds. The variability in threshold among afferents was directly related to neuronal gain and did not depend on spike count variance. The exact threshold values depended on both the time window used for spike count analysis and the filtering method used to calculate mean firing rate, although differences between regular and irregular afferent thresholds were independent of analysis parameters. The fact that minimum thresholds measured in macaque otolith afferents are of the same order of magnitude as human behavioral thresholds suggests that the vestibular periphery might determine the limit on our ability to detect or discriminate small differences in head movement, with little noise added during downstream processing.

SK channels modulate the excitability and firing precision of projection neurons in the robust nucleus of the arcopallium in adult male zebra finches

Motor control is encoded by neuronal activity. Small conductance Ca(2+)-activated K(+) channels (SK channels) maintain the regularity and precision of firing by contributing to the afterhyperpolarization (AHP) of the action potential in mammals. However, it is not clear how SK channels regulate the output of the vocal motor system in songbirds. The premotor robust nucleus of the arcopallium (RA) in the zebra finch is responsible for the output of song information. The temporal pattern of spike bursts in RA projection neurons is associated with the timing of the acoustic features of birdsong. The firing properties of RA projection neurons were analyzed using patch clamp whole-cell and cell-attached recording techniques. SK channel blockade by apamin decreased the AHP amplitude and increased the evoked firing rate in RA projection neurons. It also caused reductions in the regularity and precision of firing. RA projection neurons displayed regular spontaneous action potentials, while apamin caused irregular spontaneous firing but had no effect on the firing rate. In the absence of synaptic inputs, RA projection neurons still had spontaneous firing, and apamin had an evident effect on the firing rate, but caused no significant change in the firing regularity, compared with apamin application in the presence of synaptic inputs. SK channels contribute to the maintenance of firing regularity in RA projection neurons which requires synaptic activity, and consequently ensures the precision of song encoding.

Comparing the effects of mineral trioxide aggregate and calcium enriched mixture on neuronal cells using an electrophysiological approach.

The main goal of this ex vivo study was to assess and compare the cellular and electrophysiological effects of two dental biomaterials, white mineral trioxide aggregate (WMTA) and calcium enriched mixture (CEM) cement, on neuronal cell excitability and electrical properties. A conventional intracellular current clamp technique was used to study the cellular effects of WMTA and CEM on the excitability, firing and the shape of action potential of neuronal soma membrane of F(1) nerve cells. The dental biomaterials were prepared according to the manufacturers' directions and were applied to the bathing media and 0.05 mL of total mixture of each dental material at a distance of 3 mm from the cells. Findings indicated that exposure to both dental biomaterials shifted the irregular high frequency firing type observed in control conditions to a more regular low frequency firing pattern. Neuronal exposure to WMTA, but not CEM, significantly hyperpolarized the cell resting membrane potential. Both treatments significantly influenced the duration and the amplitude of action potentials. Extracellular application of either CEM or WMTA caused a significant increase in the after hyperpolarization (AHP) amplitude and AHP area, but the potentiating effect of WMTA was more effective than CEM. Treatment with WMTA or CEM resulted in a profound alteration in the firing behaviour of F(1) cells and changed the AP characteristics. Both dental biomaterials reduced the neuronal activity possibly through enhancement of K(+) outward current. This may possibly explain the positive mechanisms of these biomaterials in regenerative endodontics, though further research is needed for such a conclusion.

Distinguishing the Causes of Firing with the Membrane Potential Slope

In this letter, we aim to measure the relative contribution of coincidence detection and temporal integration to the firing of spikes of a simple neuron model. To this end, we develop a method to infer the degree of synchrony in an ensemble of neurons whose firing drives a single postsynaptic cell. This is accomplished by studying the effects of synchronous inputs on the membrane potential slope of the neuron and estimating the degree of response-relevant input synchrony, which determines the neuron's operational mode. The measure is calculated using the normalized slope of the membrane potential prior to the spikes fired by a neuron, and we demonstrate that it is able to distinguish between the two operational modes. By applying this measure to the membrane potential time course of a leaky integrate-and-fire neuron with the partial somatic reset mechanism, which has been shown to be the most likely candidate to reflect the mechanism used in the brain for reproducing the highly irregular firing at high rates, we show that the partial reset model operates as a temporal integrator of incoming excitatory postsynaptic potentials and that coincidence detection is not necessary for producing such high irregular firing.

Complex autonomous firing patterns of striatal low-threshold spike interneurons.

During sensorimotor learning, tonically active neurons (TANs) in the striatum acquire bursts and pauses in their firing based on the salience of the stimulus. Striatal cholinergic interneurons display tonic intrinsic firing, even in the absence of synaptic input, that resembles TAN activity seen in vivo. However, whether there are other striatal neurons among the group identified as TANs is unknown. We used transgenic mice expressing green fluorescent protein under control of neuronal nitric oxide synthase or neuropeptide-Y promoters to aid in identifying low-threshold spike (LTS) interneurons in brain slices. We found that these neurons exhibit autonomous firing consisting of spontaneous transitions between regular, irregular, and burst firing, similar to cholinergic interneurons. As in cholinergic interneurons, these firing patterns arise from interactions between multiple intrinsic oscillatory mechanisms, but the mechanisms responsible differ. Both neurons maintain tonic firing because of persistent sodium currents, but the mechanisms of the subthreshold oscillations responsible for irregular firing are different. In LTS interneurons they rely on depolarization-activated noninactivating calcium currents, whereas those in cholinergic interneurons arise from a hyperpolarization-activated potassium conductance. Sustained membrane hyperpolarizations induce a bursting pattern in LTS interneurons, probably by recruiting a low-threshold, inactivating calcium conductance and by moving the membrane potential out of the activation range of the oscillatory mechanisms responsible for single spiking, in contrast to the bursting driven by noninactivating currents in cholinergic interneurons. The complex intrinsic firing patterns of LTS interneurons may subserve differential release of classic and peptide neurotransmitters as well as nitric oxide.

Electrophysiological, Morphological, and Topological Properties of Two Histochemically Distinct Subpopulations of Cerebellar Unipolar Brush Cells

Unipolar brush cells (UBCs) are excitatory cerebellar granular layer interneurons whose brush-like dendrites receive one-to-one mossy fiber inputs. Subclasses of UBCs differ primarily by expressing metabotropic glutamate receptor (mGluR) 1α or calretinin. We used GENSAT Tg(Grp-EGFP) BAC transgenic mice, which selectively express enhanced green fluorescent protein (EGFP) in mGluR1α-positive UBCs to compare the functional properties of the two subclasses. Compared to EGFP-negative UBCs, which include the calretinin-positive cells, EGFP-positive UBCs had smaller somata (area 48 vs 63 μm(2)), lower specific membrane resistance (6.4 vs. 13.7 KΩ cm(2)), were less prone to intrinsic firing, and showed more irregular firing (in cell-attached ~49 % were firing vs. ~88 %, and the CV was 0.53 vs. 0.32 for EGFP-negative cells). Some of these differences are attributable to higher density of background K(+) currents in EGFP-positive cells (at -120 mV, the barium-sensitive current was 94 vs. 37 pA in EGFP-negative cells); Ih, on the contrary, was more abundantly expressed in EGFP-negative cells (at -140 mV, it was -122 vs. -54 pA in EGFP-positive neurons); furthermore, while group II mGluR modulation of the background potassium current in EGFP-negative UBCs was maintained after intracellular dialysis, mGluR modulation in EGFP-positive UBCs was lost in whole-cell recordings. Finally, cell-attached firing was reversibly abolished by the GABA(B) activation in EGFP-positive, but not in EGFP-negative UBCs. Immunohistochemistry showed that EGFP-negative UBCs express GIRK2 at high density, while mGluR1α UBCs are GIRK2 negative, suggesting that GIRK2 mediates the mGluR-sensitive current in EGFP-negative UBCs. These data suggest that the two subclasses perform different functions in the cerebellar microcircuits.

Mechanisms of Gamma Oscillations

Gamma rhythms are commonly observed in many brain regions during both waking and sleep states, yet their functions and mechanisms remain a matter of debate. Here we review the cellular and synaptic mechanisms underlying gamma oscillations and outline empirical questions and controversial conceptual issues. Our main points are as follows: First, gamma-band rhythmogenesis is inextricably tied to perisomatic inhibition. Second, gamma oscillations are short-lived and typically emerge from the coordinated interaction of excitation and inhibition, which can be detected as local field potentials. Third, gamma rhythm typically concurs with irregular firing of single neurons, and the network frequency of gamma oscillations varies extensively depending on the underlying mechanism. To document gamma oscillations, efforts should be made to distinguish them from mere increases of gamma-band power and/or increased spiking activity. Fourth, the magnitude of gamma oscillation is modulated by slower rhythms. Such cross-frequency coupling may serve to couple active patches of cortical circuits. Because of their ubiquitous nature and strong correlation with the "operational modes" of local circuits, gamma oscillations continue to provide important clues about neuronal population dynamics in health and disease.

Effects of long-term electromagnetic field exposure on spatial learning and memory in rats

With the development of communications industry, mobile phone plays an important role in daily life. Whether or not the electromagnetic radiation emitted by mobile phone causes any adverse effects on brain function has become of a great concern. This paper investigated the effect of electromagnetic field on spatial learning and memory in rats. 32 trained Wistar rats were divided into two groups: exposure group and control group. The exposure group was exposed to 916 MHz, 10w/m2 mobile phone electromagnetic field (EMF) 6 h a day, 5 days a week, 10 weeks. The completion time, number of total errors and the neuron discharge signals were recorded while the rats were searching for food in an eight-arm radial maze at every weekend. The neuron signals of one exposed rat and one control rat in the maze were obtained by the implanted microelectrode arrays in their hippocampal regions. It can be seen that during the weeks 4-5 of the experiment, the average completion time and error rate of the exposure group were longer and larger than that of control group (p < 0.05). During the weeks 1-3 and 6-9, they were close to each other. The hippocampal neurons showed irregular firing patterns and more spikes with shorter interspike interval during the whole experiment period. It indicates that the 916 MHz EMF influence learning and memory in rats to some extent in a period during exposure, and the rats can adapt to long-term EMF exposure.

The splicing regulator Rbfox2 is required for both cerebellar development and mature motor function.

The Rbfox proteins (Rbfox1, Rbfox2, and Rbfox3) regulate the alternative splicing of many important neuronal transcripts and have been implicated in a variety of neurological disorders. However, their roles in brain development and function are not well understood, in part due to redundancy in their activities. Here we show that, unlike Rbfox1 deletion, the CNS-specific deletion of Rbfox2 disrupts cerebellar development. Genome-wide analysis of Rbfox2(-/-) brain RNA identifies numerous splicing changes altering proteins important both for brain development and mature neuronal function. To separate developmental defects from alterations in the physiology of mature cells, Rbfox1 and Rbfox2 were deleted from mature Purkinje cells, resulting in highly irregular firing. Notably, the Scn8a mRNA encoding the Na(v)1.6 sodium channel, a key mediator of Purkinje cell pacemaking, is improperly spliced in RbFox2 and Rbfox1 mutant brains, leading to highly reduced protein expression. Thus, Rbfox2 protein controls a post-transcriptional program required for proper brain development. Rbfox2 is subsequently required with Rbfox1 to maintain mature neuronal physiology, specifically Purkinje cell pacemaking, through their shared control of sodium channel transcript splicing.

Balancing feed-forward excitation and inhibition via Hebbian inhibitory synaptic plasticity.

It has been suggested that excitatory and inhibitory inputs to cortical cells are balanced, and that this balance is important for the highly irregular firing observed in the cortex. There are two hypotheses as to the origin of this balance. One assumes that it results from a stable solution of the recurrent neuronal dynamics. This model can account for a balance of steady state excitation and inhibition without fine tuning of parameters, but not for transient inputs. The second hypothesis suggests that the feed forward excitatory and inhibitory inputs to a postsynaptic cell are already balanced. This latter hypothesis thus does account for the balance of transient inputs. However, it remains unclear what mechanism underlies the fine tuning required for balancing feed forward excitatory and inhibitory inputs. Here we investigated whether inhibitory synaptic plasticity is responsible for the balance of transient feed forward excitation and inhibition. We address this issue in the framework of a model characterizing the stochastic dynamics of temporally anti-symmetric Hebbian spike timing dependent plasticity of feed forward excitatory and inhibitory synaptic inputs to a single post-synaptic cell. Our analysis shows that inhibitory Hebbian plasticity generates ‘negative feedback’ that balances excitation and inhibition, which contrasts with the ‘positive feedback’ of excitatory Hebbian synaptic plasticity. As a result, this balance may increase the sensitivity of the learning dynamics to the correlation structure of the excitatory inputs.

Recurrent coupling improves discrimination of temporal spike patterns

Despite the ubiquitous presence of recurrent synaptic connections in sensory neuronal systems, their general functional purpose is not well understood. A recent conceptual advance has been achieved by theories of reservoir computing in which recurrent networks have been proposed to generate short-term memory as well as to improve neuronal representation of the sensory input for subsequent computations. Here, we present a numerical study on the distinct effects of inhibitory and excitatory recurrence in a canonical linear classification task. It is found that both types of coupling improve the ability to discriminate temporal spike patterns as compared to a purely feed-forward system, although in different ways. For a large class of inhibitory networks, the network’s performance is optimal as long as a fraction of roughly 50% of neurons per stimulus is active in the resulting population code. Thereby the contribution of inactive neurons to the neural code is found to be even more informative than that of the active neurons, generating an inherent robustness of classification performance against temporal jitter of the input spikes. Excitatory couplings are found to not only produce a short-term memory buffer but also to improve linear separability of the population patterns by evoking more irregular firing as compared to the purely inhibitory case. As the excitatory connectivity becomes more sparse, firing becomes more variable, and pattern separability improves. We argue that the proposed paradigm is particularly well-suited as a conceptual framework for processing of sensory information in the auditory pathway.

What Does Galvanic Vestibular Stimulation Actually Activate?

What Does Galvanic Vestibular Stimulation Actually Activate?

Blockade of mGluR5 Reverses Abnormal Firing of Subthalamic Nucleus Neurons in 6-Hydroxydopamine Partially Lesioned Rats

Activation of metabotropic glutamate receptor 5 (mGluRs) in the subthalamic nucleus (STN) results in burst-firing activity of STN neurons, which is similar to that observed in Parkinson's disease (PD). We examined the effects of chronic and systemic treatment with 2-methyl-6-(phenylethynyl)-pyridine (MPEP), a selective mGluR5 antagonist, in firing activity of STN neurons in partially lesioned rats by 6-hydroxydopamine (6-OHDA). In 6-OHDA-lesioned rats treated with vehicle, injection of 6-OHDA (4 microg) into the medial forebrain bundle produced a partial lesion causing 36% loss of tyrosine hydroxylase-immunoreactive (TH-ir) neurons in the substantia nigra pars compacta (SNpc). The 6-OHDA lesion in vehicle-treated rats showed an increasing firing rate and a more irregular firing pattern of STN neurons. Whereas chronic, systemic treatment of MPEP (3 mg/kg/day, 14 days) produced neuroprotecive effects on the TH-ir neurons and normalized the hyperactive firing activity of STN neurons in 6-OHDA partially lesioned rats. These data demonstrate that partial lesion of the nigrostriatal pathway increases firing activity of STN neurons in the rat, and chronic, systemic MPEP treatment has the neuroprotective effect and reverses the abnormal firing activity of STN neurons, suggesting that MPEP has an important implication for the treatment of PD.

Conductance-Based Neuron Models and the Slow Dynamics of Excitability

In recent experiments, synaptically isolated neurons from rat cortical culture, were stimulated with periodic extracellular fixed-amplitude current pulses for extended durations of days. The neuron’s response depended on its own history, as well as on the history of the input, and was classified into several modes. Interestingly, in one of the modes the neuron behaved intermittently, exhibiting irregular firing patterns changing in a complex and variable manner over the entire range of experimental timescales, from seconds to days. With the aim of developing a minimal biophysical explanation for these results, we propose a general scheme, that, given a few assumptions (mainly, a timescale separation in kinetics) closely describes the response of deterministic conductance-based neuron models under pulse stimulation, using a discrete time piecewise linear mapping, which is amenable to detailed mathematical analysis. Using this method we reproduce the basic modes exhibited by the neuron experimentally, as well as the mean response in each mode. Specifically, we derive precise closed-form input-output expressions for the transient timescale and firing rates, which are expressed in terms of experimentally measurable variables, and conform with the experimental results. However, the mathematical analysis shows that the resulting firing patterns in these deterministic models are always regular and repeatable (i.e., no chaos), in contrast to the irregular and variable behavior displayed by the neuron in certain regimes. This fact, and the sensitive near-threshold dynamics of the model, indicate that intrinsic ion channel noise has a significant impact on the neuronal response, and may help reproduce the experimentally observed variability, as we also demonstrate numerically. In a companion paper, we extend our analysis to stochastic conductance-based models, and show how these can be used to reproduce the details of the observed irregular and variable neuronal response.

Modeling and Formalization of Fuzzy Finite Automata for Detection of Irregular Fire Flames

Fire-flame detection using a video camera is difficult because a flame has irregular characteristics, i.e., vague shapes and color patterns. Therefore, in this paper, we propose a novel fire-flame detection method using fuzzy finite automata (FFA) with probability density functions based on visual features, thereby providing a systemic approach to handling irregularity in computational systems and the ability to handle continuous spaces by combining the capabilities of automata with fuzzy logic. First, moving regions are detected via background subtraction, and the candidate flame regions are then identified by applying flame color models. In general, flame regions have a continuous irregular pattern; therefore, probability density functions are generated for the variation in intensity, wavelet energy, and motion orientation and applied to the FFA. The proposed algorithm is successfully applied to various fire/non-fire videos, and its detection performance is better than that of other methods.

Learning optimisation by high firing irregularity

In a network of leaky integrate-and-fire (LIF) neurons, we investigate the functional role of irregular spiking at high rates. Irregular spiking is produced by either employing the partial somatic reset mechanism on every LIF neuron of the network or by using temporally correlated inputs. In both the benchmark problem of XOR (exclusive-OR) and in a general-sum game, it is shown that irrespective of the mechanism that is used to produce it, high firing irregularity enhances the learning capability of the spiking neural network trained with reward-modulated spike-timing-dependent plasticity. These results suggest that the brain may be utilising high firing irregularity for the purposes of learning optimisation.

Fast and richly structured activity in cortical networks with local inhibition

Spiking models of small areas of the neocortex have so far mainly been studied under the assumption that neurons are sparsely and randomly connected. The absence of structure allows for the use of powerful mean-field approximation techniques, whereby statistical properties of spike trains such as the average firing rate and the interspike interval coefficient of variation are described in terms of their distributions across neurons [1,2]. Such approaches cannot offer a more detailed description as to what patterns of activity the network spontaneously produces. The macroscopic state is unstructured with predictable mean, while during the asynchronous irregular firing the microscopic dynamics is chaotic. It therefore becomes hard to think of the representation of information in cortex if its activity is neither structured nor reproducible. Connectivity in cortex is not purely random: published data on synaptic connectivity suggests a model of cortical connectivity in which excitatory neurons make long-range patchy projections while inhibitory neurons only target their close neighbours [3]. How does this structure affect the dynamics of the network? Can we hope to describe it in terms of activity patterns, therefore going beyond the mean-field predictions mentioned above? Recent theoretical considerations [4] have suggested a positive answer, by showing that balanced networks should be expected to transiently amplify certain activity patterns without slowing the dynamics down (“balanced amplification”). Underlying this theory is the nature of the connectivity matrix, “nonnormal” in the mathematical sense [5]. The amplified patterns depend on the details of the connectivity and are best revealed by a Schur decomposition of the connectivity matrix. We examine the dynamics of linear networks with patchy connectivity structures. With long-range patchy inhibition, about 25% of the eigenvalues of the connectivity matrix have strictly positive real parts. Since real parts must be smaller than one for stability, the overall magnitude of the connections, and therefore also the amount of transient amplification, need to be constrained. The presence of eigenvalues with real parts close to one also predicts dynamical slowing with a small number of modes (typically < 5) completely dominating the dynamics and limiting the representational power. If inhibition is kept local, as it seems to be in nature, the connectivity matrix has the nice property that all its eigenvalues have negative real parts. The network can thus be arbitrarily rescaled and yet remain stable. Strong amplification can coexist with fast network dynamics that yields a rich repertoire of transient spatial states. Extensive simulations of the noise-driven linear networks confirm the theoretical insights. Importantly, we also ran large-scale simulations of conductance-based spiking neurons with sparse connectivity spatially organized as mentioned above. Despite the strong deviation from linearity, the network activity matches the linear predictions remarkably well, in terms of both speed and amplification of the Schur modes. We conclude that the activity in spatially organised networks is highly structured and can be described on the level of two-dimensional activity patterns. Local inhibition, but not long-range inhibition, enables rapid state switching on the time scale of a few milliseconds.

Robustness vs. flexibility: how do external inputs shape the activity in a data-based layered cortical network model?

We developed a model of the layered cortical network that integrates a major body of the available physiological and anatomical connectivity data on the cortical microcircuit [1]. The model extents the well-known balanced random network model [2,3] which reproduces the asynchronous irregular firing regime observed in vivo. The data-based multi-layered extension, in addition, yields cell-type specific firing rates and activation patterns after transient stimulation in accordance with in vivo recordings in awake animals, see e.g. [4]. In mono-layered balanced random network models the external input can be characterized by a single parameter, the external background firing rate. In contrast, the multi-layered network requires the specification of background inputs for every layer. We therefore distinguish the number of inputs a neuron of a given cell type receives and the background firing rate per external synapse. The experimentally observed cell-type specific firing rates are more accurately reproduced by a data-based estimate of the number of external inputs than by a layer-independent input parameterization. Still, it remains unclear how the overall contributions of background activity to the different layers shape the activity of the network. Here, we analyze the dependence of the activity in the full-scale local cortical network model on the external inputs. We apply large-scale parameter scans in NEST [5] to study the effect of changes in the overall excitation of the network as well as of layer-specific modifications. Firstly, we find that, confronted with increasing overall excitation, the excitatory populations in the different layers react partly contrary, with rate increases or decreases. Secondly, we test for the robustness of the cell-type specific distribution of spontaneous firing rates: we consider randomly selected numbers of external inputs, constraint by data-based estimates, and reveal that the experimental activity measurements –low firing rates in layer 2/3 and 6, intermediate rates in layer 4 and highest activity in layer 5– are reproduced in around 80% of the trials. We test whether this remarkable robustness comes at the price of a reduced flexibility of the network, but find that in the same parameter setting the activity of principal cells strongly depends on the balance of the excitation in a given layer, reflecting a selective activity gain. Furthermore, we investigate the transmission properties to transient inputs and their dependence on the network state.

Intraoperative microelectrode recording for the delineation of subthalamic nucleus topography in Parkinson’s disease

The subthalamic nucleus (STN) as an effective target for deep brain stimulation (DBS) in advanced Parkinson's disease is functionally divided into the dorsolateral sensorimotor and the ventromedial limbic and associative parts. To implant electrodes for DBS close to the sensorimotor region is considered crucial for optimal motor benefit and for avoidance of potential cognitive and behavioral side effects. The aim of this study was to determine whether the functional segregation of the STN is associated with distinct and region-specific neuronal activity patterns and action potential properties obtained by intraoperative microelectrode recordings. In 12 Parkinson's disease patients, stepwise intraoperative microelectrode recordings were performed using five concentrically configured electrodes starting 10 mm above the calculated target point until the dorsal border of the substantia nigra. Based on autocorrelogram analysis of a total of 329 single units, we found a higher occurrence of oscillatory (P < 0.01) and bursty (P = 0.058) spike pattern in the dorsal versus the ventral STN. In contrast the ventral region was characterized by irregular firing neurons (P < 0.01). There were no significant differences in firing frequency, coefficient of variance, asymmetry index as well as spike form, duration, and amplitude. Among all parameters analyzed in the study, spike pattern is the only convenient electrophysiologic parameter for the differentiation of STN subregions in patients with Parkinson's disease. The autocorrelogram-based analysis of spike activity seems to be of certain value for the delineation of the dorsolateral STN and might therefore facilitate the precise electrode implantation for DBS.