Spiking Activity Of Neurons Research Articles

Reduced intrinsic excitability of CA1 pyramidal neurons in human immunodeficiency virus (HIV) transgenic rats

The hippocampus is involved in key neuronal circuits that underlie cognition, memory, and anxiety, and it is increasingly recognized as a vulnerable structure that contributes to the pathogenesis of HIV-associated neurocognitive disorder (HAND). However, the mechanisms responsible for hippocampal dysfunction in neuroHIV remain unknown. The present study used HIV transgenic (Tg) rats and patch-clamp electrophysiological techniques to study the effects of the chronic low-level expression of HIV proteins on hippocampal CA1 pyramidal neurons. The dorsal and ventral areas of the hippocampus are involved in different neurocircuits and thus were evaluated separately. We found a significant decrease in the intrinsic excitability of CA1 neurons in the dorsal hippocampus in HIV Tg rats by comparing neuronal spiking induced by current step injections and by dynamic clamp to simulate neuronal spiking activity. The decrease in excitability in the dorsal hippocampus was accompanied by a higher rate of excitatory postsynaptic currents (EPSCs), whereas CA1 pyramidal neurons in the ventral hippocampus in HIV Tg rats had higher EPSC amplitudes. We also observed a reduction of hyperpolarization-activated nonspecific cationic current (Ih) in both the dorsal and ventral hippocampus. Neurotoxic HIV proteins have been shown to increase neuronal excitation. The lower excitability of CA1 pyramidal neurons that was observed herein may represent maladaptive homeostatic plasticity that seeks to stabilize baseline neuronal firing activity but may disrupt neural network function and contribute to HIV-associated neuropsychological disorders, such as HAND and depression.

Post-tetanic Potentiation and Depression in Hippocampal Neurons in a Rat Model of Alzheimer’s Disease: Effects of Teucrium Polium Extract

The plant Teucrium polium (T.p.) possesses a wide range of pharmacological activities due to the presence, in particular, of different phytochemicals, phenols and flavonoids. We examined the effects of the T.p. extract on impulse activity of hippocampal neurons in rats with a model of Alzheimer’s disease. Adult albino rats were divided into three groups (5 animals in each). The control group C obtained intracerebroventricular (i.c.v.) infusions of saline, animals of group Aβ were i.c.v. infused with amyloid β peptide 25–35, Aβ(25–35), and group Aβ+T.p. was treated by both Aβ(25–35) infusions and introductions of the T.p. extract. In group Aβ, a greater proportion of hippocampal neurons with post-tetanic depression after high-frequency stimulation of the entorhinal cortex, lower values of the frequency of background activity generated by the above neurons, and relatively weaker modifications of spike activity (post-tetanic potentiation and depression) were observed. Daily administrations of the T.p. extract partially but considerably reversed the above-mentioned negative shifts in spike activity of hippocampal neurons caused by Aβ(25–35). We suggest that T.p. extract containing important phytochemicals is capable of ameliorating the memory dysfunction caused by Aβ(25–35) via blocking amyloid deposition and a positive influence on the functions of hippocampal neurons.

Large-Scale Communication in the Human Brain Is Rhythmically Modulated through Alpha Coherence.

Recent evidence has suggested that coherent neuronal oscillations may serve as a gating mechanism for flexibly modulating communication between brain regions. For this to occur, such oscillations should be robust and coherent between brain regions that also demonstrate time-locked correlations, with time delays that match the phase delays of the coherent oscillations. Here, by analyzing functional connectivity in both the time and frequency domains, we demonstrate that alpha oscillations satisfy these constraints and are well suited for modulating communication over large spatial scales in the human brain. We examine intracranial EEG in the human temporal lobe and find robust alpha oscillations that are coherent between brain regions with center frequencies that are consistent within each individual participant. Regions demonstrating coherent narrowband oscillations also exhibit time-locked broadband correlations with a consistent time delay, a requirement for an efficient communication channel. The phase delays of the coherent alpha oscillations match the time delays of the correlated components, and importantly, both broadband correlations and neuronal spiking activity are modulated by the phase of the oscillations. These results are specific to the alpha band and build upon emerging evidence suggesting that alpha oscillations may play an active role in cortical function. Our data therefore provide evidence that large-scale communication in the human brain may be rhythmically modulated by alpha oscillations.

A New Nonlinear Sparse Component Analysis for a Biologically Plausible Model of Neurons

It is known that brain can create a sparse representation of the environment in both sensory and mnemonic forms (Olshausen & Field, 2004). Such sparse representation can be combined in downstream areas to create rich multisensory responses to support various cognitive and motor functions. Determining the components present in neuronal responses in a given region is key to deciphering its functional role and connection with upstream areas. One approach for parsing out various sources of information in a single neuron is by using linear blind source separation (BSS) techniques. However, applying linear techniques to neuronal spiking activity is likely to be suboptimal due to inherent and unknown nonlinearity of neuronal responses to inputs. This letter proposes a nonlinear sparse component analysis (SCA) method to separate jointly sparse inputs to neurons with post summation nonlinearity, or SCA for post-nonlinear neurons (SCAPL). Specifically, a linear clustering approach followed by principal curve regression (PCR) and a nonlinear curve fitting are used to separate sources. Analysis using simulated data shows that SCAPL accuracy outperforms ones obtained by linear SCA, as well as other separating methods, including linear independent and principal component analyses. In SCAPL, the number of derived sparse components is not limited by the number of neurons, unlike most BSS methods. Furthermore, this method allows for a broad range of post-summation nonlinearities that could differ among neurons. The sensitivity of our method to noise, joint sparseness, degree, and shape of nonlinearity and mixing ill conditions is discussed and compared to existing methods. Our results show that the proposed method can successfully separate input components in a population of neurons provided that they are temporally sparse to some degree. Application of SCAPL should facilitate comparison of functional roles across regions by parsing various elements present in a region.

Theoretical principles of deep brain stimulation induced synaptic suppression

BackgroundDeep brain stimulation (DBS) is a successful clinical therapy for a wide range of neurological disorders; however, the physiological mechanisms of DBS remain unresolved. While many different hypotheses currently exist, our analyses suggest that high frequency (∼100 Hz) stimulation-induced synaptic suppression represents the most basic concept that can be directly reconciled with experimental recordings of spiking activity in neurons that are being driven by DBS inputs. ObjectiveThe goal of this project was to develop a simple model system to characterize the excitatory post-synaptic currents (EPSCs) and action potential signaling generated in a neuron that is strongly connected to pre-synaptic glutamatergic inputs that are being directly activated by DBS. MethodsWe used the Tsodyks-Markram (TM) phenomenological synapse model to represent depressing, facilitating, and pseudo-linear synapses driven by DBS over a wide range of stimulation frequencies. The EPSCs were then used as inputs to a leaky integrate-and-fire neuron model and we measured the DBS-triggered post-synaptic spiking activity. ResultsSynaptic suppression was a robust feature of high frequency stimulation, independent of the synapse type. As such, the TM equations were used to define alternative DBS pulsing strategies that maximized synaptic suppression with the minimum number of stimuli. ConclusionsSynaptic suppression provides a biophysical explanation to the intermittent, but still time-locked, post-synaptic firing characteristics commonly seen in DBS experimental recordings. Therefore, network models attempting to analyze or predict the effects of DBS on neural activity patterns should integrate synaptic suppression into their simulations.

Biophysical Properties of Somatic and Axonal Voltage-Gated Sodium Channels in Midbrain Dopaminergic Neurons.

Spiking activities of midbrain dopaminergic neurons are critical for key brain functions including motor control and affective behaviors. Voltage-gated Na+ channels determine neuronal excitability and action potential (AP) generation. Previous studies on dopaminergic neuron excitability mainly focused on Na+ channels at the somatodendritic compartments. Properties of axonal Na+ channels, however, remain largely unknown. Using patch-clamp recording from somatic nucleated patches and isolated axonal blebs from the axon initial segment (AIS) of dopaminergic neurons in mouse midbrain slices, we found that AIS channel density is approximately 4–9 fold higher than that at the soma. Similar voltage dependence of channel activation and inactivation was observed between somatic and axonal channels in both SNc and VTA cells, except that SNc somatic channels inactivate at more hyperpolarized membrane potentials (Vm). In both SNc and VTA, axonal channels take longer time to inactivate at a subthreshold depolarization Vm level, but are faster to recover from inactivation than somatic channels. Moreover, we found that immunosignals of Nav1.2 accumulate at the AIS of dopaminergic neurons. In contrast, Nav1.1 and Nav1.6 immunosignals are not detectible. Together, our results reveal a high density of Na+ channels at the AIS and their molecular identity. In general, somatic and axonal channels of both SNc and VTA dopaminergic neurons share similar biophysical properties. The relatively delayed inactivation onset and faster recovery from inactivation of axonal Na+ channels may ensure AP initiation at high frequencies and faithful signal conduction along the axon.

Stimulus-Specific Adaptation Decreases the Coupling of Spikes to LFP Phase.

Stimulus repetition suppresses the neural activity in different sensory areas of the brain. This mechanism of so-called stimulus-specific adaptation (SSA) has been observed in both spiking activity and local field potential (LFP) responses. However, much remains to be known about the effect of SSA on the spike–LFP relation. In this study, we approached this issue by investigating the spike-phase coupling (SPC) in control and adapting paradigms. For the control paradigm, pure tones were presented in a random unbiased sequence. In the adapting paradigm, the same stimuli were presented in a random pattern but it was biased to an adapter stimulus. In fact, the adapter occupied 80% of the adapting sequence. During the tasks, LFP and multi-unit activity were recorded simultaneously from the primary auditory cortex of 15 anesthetized rats. To clarify the effect of adaptation on the relation between spike and LFP responses, the SPC of the adapter stimulus in these two paradigms was evaluated. Here, we employed phase locking value method for calculating the SPC. Our data show a strong coupling of spikes to LFP phase most prominently in beta band. This coupling was observed to decrease in the adapting condition compared to the control one. Importantly, we found that adaptation reduces spikes dominantly from the preferred phase of LFP in which spikes are more likely to be present there. As a result, the preferred phase of LFP may play a key role in coordinating neuronal spiking activity in neural adaptation mechanism. This finding is important for interpretation of the underlying neural mechanism of adaptation and also can be used in the context of the network and related connectivity models.

Novel metrics to measure the effect of additive inputs on the activity of sensory system neurons.

Sensory systems, such as the visual or auditory system, are highly non linear. It is therefore not easy to predict the effect of additive inputs on the spiking activity of related brain structures. Here, we propose two metrics to study the effect of additive covariates on the spiking activity of neurons. These metrics are directly obtained from a generalized linear model. We apply these metrics to the study of the effect of additive input audio noise on the spiking activity of neurons in the auditory system. To do so, we combine clean vocalisations with natural stationary or non-stationary noises and record activity in the auditory system while presenting the noisy vocalisations. We found that non-stationary noise has a greater effect on the neural activity than stationary noise. We observe that the results, obtained using the proposed metrics, is more consistent with current knowledge in auditory neuroscience than the results obtained when using a common metric from the literature, the extraction index.

Modeling Responses to Peripheral Nerve Stimulation in the Dorsal Horn.

Pain is a protective physiological system essential for survival. However, it can malfunction and create a debilitating disease known as chronic pain (CP). CP is primarily treated with drugs that can have negative side effects (e.g., opioid addiction), and lose efficacy after long-term use. Electrical stimulation of the spinal cord or peripheral nerves is an alternative therapy that has great potential to reduce the need for drugs and has fewer negative side effects; but has been associated with suboptimal efficacy because its modulation mechanisms are unknown. Critical to advancing CP treatment is a deeper understanding of how pain is processed in the superficial and deep layers of the dorsal horn (DH), which is the first central relay station for pain processing in the spinal cord. Mechanistic models of the DH have been developed to investigate modulation mechanisms but are non-linear and high-dimensional and thus difficult to analyze. In this paper, we construct a tractable computational model of the DH in rats from LFP recordings of the superficial layer network and spiking activity of WDR neurons in the deep layer. By combining a deterministic linear time-invariant model with a stochastic point process model, we can accurately predict responses of the DH circuit to electrical stimulation of the peripheral nerve. The model is computationally efficient, low-dimensional, and able to capture the stochastic nature of neuronal dynamics in the DH; and is a first step in developing new therapies for CP.

Pathogenic Processes Underlying Alzheimer's Disease: Modeling the Effects of Amyloid Beta on Synaptic Transmission.

The molecular mechanisms underlying Alzheimer's disease (AD) have been and are still under heavy scrutiny to better understand what leads to the onset and progression of the disease, and to design and develop efficacious therapeutic strategies. These decade-long studies have taught us a lot regarding the various molecular pathways involved in the pathology, but a complete dynamic picture of the underlying pathological mechanisms is still missing.We propose to provide a technological answer to fill this gap by developing and using a computational approach that integrates AD-related experimental findings and their effects on multiple aspects of neuronal function. The present study focuses on implementing one known pathogenic process: the binding of amyloid beta, the hallmark of AD, on NMDA receptors, receptors present in the main type of excitatory synapses in the brain, thereby affecting synaptic transmission and downstream pathways. We describe model implementation and calibration; we then quantify the downstream effects of this disruption both in terms of electrical activity (changes in short-term spiking activity of the postsynaptic neuron), and biochemical pathways activation through changes in calcium dynamics (an important trigger to longer-term changes). The computational approach outlined constitutes an insightful instrument to examine the downstream consequences of multiple pathogenic dysfunctions on higher level observables and sets the path for in-silico discovery and testing of therapeutic agents.

Spike-field Granger causality for hybrid neural data analysis.

Neurotechnological innovations allow for simultaneous recording at various scales, ranging from spiking activity of individual neurons to large neural populations' local field potentials (LFPs). This capability necessitates developing multiscale analysis of spike-field activity. A joint analysis of the hybrid neural data is crucial for bridging the scales between single neurons and local networks. Granger causality is a fundamental measure to evaluate directional influences among neural signals. However, it is mainly limited to inferring causal influence between the same type of signals-either LFPs or spike trains-and not well developed between two different signal types. Here we propose a model-free, nonparametric spike-field Granger causality measure for hybrid data analysis. Our measure is distinct from existing methods in that we use "binless" spikes (precise spike timing) rather than "binned" spikes (spike counts within small consecutive time windows). The latter clearly distort the information in the mixed analysis of spikes and LFP. Therefore, our spectral estimate of spike trains is directly applied to the neural point process itself, i.e., sequences of spike times rather than spike counts. Our measure is validated by an extensive set of simulated data. When the measure is applied to LFPs and spiking activity simultaneously recorded from visual areas V1 and V4 of monkeys performing a contour detection task, we are able to confirm computationally the long-standing experimental finding of the input-output relationship between LFPs and spikes. Importantly, we demonstrate that spike-field Granger causality can be used to reveal the modulatory effects that are inaccessible by traditional methods, such that spike→LFP Granger causality is modulated by the behavioral task, whereas LFP→spike Granger causality is mainly related to the average synaptic input.NEW & NOTEWORTHY It is a pressing question to study the directional interactions between local field potential (LFP) and spiking activity. In this report, we propose a model-free, nonparametric spike-field Granger causality measure that can be used to reveal directional influences between spikes and LFPs. This new measure is crucial for bridging the scales between single neurons and neural networks; hence it represents an important step to explicate how the brain orchestrates information processing.

Neural effects of transcranial magnetic stimulation at the single-cell level

Transcranial magnetic stimulation (TMS) can non-invasively modulate neural activity in humans. Despite three decades of research, the spatial extent of the cortical area activated by TMS is still controversial. Moreover, how TMS interacts with task-related activity during motor behavior is unknown. Here, we applied single-pulse TMS over macaque parietal cortex while recording single-unit activity at various distances from the center of stimulation during grasping. The spatial extent of TMS-induced activation is remarkably restricted, affecting the spiking activity of single neurons in an area of cortex measuring less than 2 mm in diameter. In task-related neurons, TMS evokes a transient excitation followed by reduced activity, paralleled by a significantly longer grasping time. Furthermore, TMS-induced activity and task-related activity do not summate in single neurons. These results furnish crucial experimental evidence for the neural effects of TMS at the single-cell level and uncover the neural underpinnings of behavioral effects of TMS.

Assessing the Impact of Single-Cell Stimulation on Local Networks in Rat Barrel Cortex—A Feasibility Study

In contrast to the long-standing notion that the role of individual neurons in population activity is vanishingly small, recent studies have shown that electrical activation of only a single cortical neuron can have measurable effects on global brain state, movement, and perception. Although highly important for understanding how neuronal activity in cortex is orchestrated, the cellular and network mechanisms underlying this phenomenon are unresolved. Here, we first briefly review the current state of knowledge regarding the phenomenon of single-cell induced network modulation and discuss possible underpinnings. Secondly, we show proof of principle for an experimental approach to elucidate the mechanisms of single-cell induced changes in cortical activity. The setup allows simultaneous recordings of the spiking activity of multiple neurons across all layers of the cortex using a multi-electrode array, while manipulating the activity of one individual neuron in close proximity to the array. We demonstrate that single cells can be recorded and stimulated reliably for hundreds of trials, conferring high statistical power even for expectedly small effects of single-neuron spiking on network activity. Preliminary results suggest that single-cell stimulation on average decreases the firing rate of local network units. We expect that characterization of the spatiotemporal spread of single-cell evoked activity across layers and columns will yield novel insights into intracortical processing.

High-order coordination of cortical spiking activity modulates perceptual accuracy.

The accurate relay of electrical signals within cortical networks is key to perception and cognitive function. Theoretically, it has long been proposed that temporal coordination of neuronal spiking activity controls signal transmission and behavior. However, whether and how temporally precise neuronal coordination in population activity influences perception is unknown. Here, we recorded from neuronal populations in early and mid-level visual cortex (areas V1 and V4) simultaneously to discover that the precise temporal coordination between the spiking activity of three or more cells carries information about visual perception in the absence of firing rate modulation. The accuracy of perceptual responses was correlated with high-order spiking coordination within V4, but not V1, and with feedforward coordination between V1 and V4. These results indicate that while visual stimuli are encoded in the discharge rates of neurons, perceptual accuracy is related to the temporally precise spiking coordination within and between cortical networks.

Place cell assemblies remain intact, despite reduced phase precession, after cholinergic disruption.

The hippocampal theta rhythm is frequently viewed as a clocking mechanism that coordinates the spiking activity of neurons across the hippocampus to form coherent neural assemblies. Phase precession is a form of temporal coding evidencing this mechanism and is degraded following systemic pharmacological disruption of cholinergic signaling. However, whether neural assemblies are commensurately degraded, as would be predicted from a clocking mechanism hypothesis, remains unknown. To address this, we recorded the spiking activity of hippocampal place cells as rats completed laps on a circle track for chocolate drink before versus during the influence of a systemic muscarinic acetylcholine receptor antagonist. We compared the integrity of hippocampal ensembles using three approaches. The first approach used cross-correlogram (CCG) analyses to ask if the relative spike-timing between pairs of cells became less reliable. The second used a general linear model based analysis to ask whether the activity of simultaneously recorded neurons became any less predictive of the spiking activity of single neurons. Finally, the third approach used a reconstruction analysis to ask if the population activity was any less informative regarding the environmental position of the animal and whether theta sequences were impaired. The results of all three analyses paint a consistent picture: systemic cholinergic disruption did not degrade assembly integrity. These data demonstrate that place cell assemblies do not depend upon high quality phase precession.

Quasiperiodic rhythms of the inferior olive.

Inferior olivary activity causes both short-term and long-term changes in cerebellar output underlying motor performance and motor learning. Many of its neurons engage in coherent subthreshold oscillations and are extensively coupled via gap junctions. Studies in reduced preparations suggest that these properties promote rhythmic, synchronized output. However, the interaction of these properties with torrential synaptic inputs in awake behaving animals is not well understood. Here we combine electrophysiological recordings in awake mice with a realistic tissue-scale computational model of the inferior olive to study the relative impact of intrinsic and extrinsic mechanisms governing its activity. Our data and model suggest that if subthreshold oscillations are present in the awake state, the period of these oscillations will be transient and variable. Accordingly, by using different temporal patterns of sensory stimulation, we found that complex spike rhythmicity was readily evoked but limited to short intervals of no more than a few hundred milliseconds and that the periodicity of this rhythmic activity was not fixed but dynamically related to the synaptic input to the inferior olive as well as to motor output. In contrast, in the long-term, the average olivary spiking activity was not affected by the strength and duration of the sensory stimulation, while the level of gap junctional coupling determined the stiffness of the rhythmic activity in the olivary network during its dynamic response to sensory modulation. Thus, interactions between intrinsic properties and extrinsic inputs can explain the variations of spiking activity of olivary neurons, providing a temporal framework for the creation of both the short-term and long-term changes in cerebellar output.

Modulations in Oscillatory Activity of Globus Pallidus Internus Neurons During a Directed Hand Movement Task-A Primary Mechanism for Motor Planning.

Globus pallidus internus (GPi) neurons in the basal ganglia are traditionally thought to play a significant role in the promotion and suppression of movement via a change in firing rates. Here, we hypothesize that a primary mechanism of movement control by GPi neurons is through specific modulations in their oscillatory patterns. We analyzed neuronal spiking activity of 83 GPi neurons recorded from two healthy nonhuman primates executing a radial center-out motor task. We found that, in directionally tuned neurons, the power in the gamma band is significantly (p < 0.05) greater than that in the beta band (a “cross-over” effect), during the planning stages of movements in their preferred direction. This cross-over effect is not observed in the non-directionally tuned neurons. These data suggest that, during movement planning, information encoding by GPi neurons may be governed by a sudden emergence and suppression of oscillatory activities, rather than simply by a change in average firing rates.

Neonatal Nicotine Exposure Primes Midbrain Neurons to a Dopaminergic Phenotype and Increases Adult Drug Consumption

BackgroundNicotine intake induces addiction through neuroplasticity of the reward circuitry, altering the activity of dopaminergic neurons of the ventral tegmental area. Prior work demonstrated that altered circuit activity can change neurotransmitter expression in the developing and adult brain. Here we investigated the effects of neonatal nicotine exposure on the dopaminergic system and nicotine consumption in adulthood. MethodsMale and female mice were used for two-bottle-choice test, progressive ratio breakpoint test, immunohistochemistry, RNAscope, quantitative polymerase chain reaction, calcium imaging, and DREADD (designer receptor exclusively activated by designer drugs)-mediated chemogenic activation/inhibition experiments. ResultsNeonatal nicotine exposure potentiates drug preference in adult mice, induces alterations in calcium spike activity of midbrain neurons, and increases the number of dopamine-expressing neurons in the ventral tegmental area. Specifically, glutamatergic neurons are first primed to express transcription factor Nurr1, then acquire the dopaminergic phenotype following nicotine re-exposure in adulthood. Enhanced neuronal activity combined with Nurr1 expression is both necessary and sufficient for the nicotine-mediated neurotransmitter plasticity to occur. ConclusionsOur findings illuminate a new mechanism of neuroplasticity by which early nicotine exposure primes the reward system to display increased susceptibility to drug consumption in adulthood.

Weak electric fields promote resonance in neuronal spiking activity: Analytical results from two-compartment cell and network models.

Transcranial brain stimulation and evidence of ephaptic coupling have sparked strong interests in understanding the effects of weak electric fields on the dynamics of neuronal populations. While their influence on the subthreshold membrane voltage can be biophysically well explained using spatially extended neuron models, mechanistic analyses of neuronal spiking and network activity have remained a methodological challenge. More generally, this challenge applies to phenomena for which single-compartment (point) neuron models are oversimplified. Here we employ a pyramidal neuron model that comprises two compartments, allowing to distinguish basal-somatic from apical dendritic inputs and accounting for an extracellular field in a biophysically minimalistic way. Using an analytical approach we fit its parameters to reproduce the response properties of a canonical, spatial model neuron and dissect the stochastic spiking dynamics of single cells and large networks. We show that oscillatory weak fields effectively mimic anti-correlated inputs at the soma and dendrite and strongly modulate neuronal spiking activity in a rather narrow frequency band. This effect carries over to coupled populations of pyramidal cells and inhibitory interneurons, boosting network-induced resonance in the beta and gamma frequency bands. Our work contributes a useful theoretical framework for mechanistic analyses of population dynamics going beyond point neuron models, and provides insights on modulation effects of extracellular fields due to the morphology of pyramidal cells.

Quantal Release of Dopamine and Action Potential Firing Detected in Midbrain Neurons by Multifunctional Diamond-Based Microarrays.

Micro-Graphitic Single Crystal Diamond Multi Electrode Arrays (μG-SCD-MEAs) have so far been used as amperometric sensors to detect catecholamines from chromaffin cells and adrenal gland slices. Besides having time resolution and sensitivity that are comparable with carbon fiber electrodes, that represent the gold standard for amperometry, μG-SCD-MEAs also have the advantages of simultaneous multisite detection, high biocompatibility and implementation of amperometric/potentiometric protocols, aimed at monitoring exocytotic events and neuronal excitability. In order to adapt diamond technology to record neuronal activity, the μG-SCD-MEAs in this work have been interfaced with cultured midbrain neurons to detect electrical activity as well as quantal release of dopamine (DA). μG-SCD-MEAs are based on graphitic sensing electrodes that are embedded into the diamond matrix and are fabricated using MeV ion beam lithography. Two geometries have been adopted, with 4 × 4 and 8 × 8 microelectrodes (20 μm × 3.5 μm exposed area, 200 μm spacing). In the amperometric configuration, the 4 × 4 μG-SCD-MEAs resolved quantal exocytosis from midbrain dopaminergic neurons. KCl-stimulated DA release occurred as amperometric spikes of 15 pA amplitude and 0.5 ms half-width, at a mean frequency of 0.4 Hz. When used as potentiometric multiarrays, the 8 × 8 μG-SCD-MEAs detected the spontaneous firing activity of midbrain neurons. Extracellularly recorded action potentials (APs) had mean amplitude of ∼-50 μV and occurred at a mean firing frequency of 0.7 Hz in 67% of neurons, while the remaining fired at 6.8 Hz. Comparable findings were observed using conventional MEAs (0.9 and 6.4 Hz, respectively). To test the reliability of potentiometric recordings with μG-SCD-MEAs, the D2-autoreceptor modulation of firing was investigated by applying levodopa (L-DOPA, 20 μM), and comparing μG-SCD-MEAs, conventional MEAs and current-clamp recordings. In all cases, L-DOPA reduced the spontaneous spiking activity in most neurons by 70%, while the D2-antagonist sulpiride reversed this effect. Cell firing inhibition was generally associated with increased APs amplitude. A minority of neurons was either insensitive to, or potentiated by L-DOPA, suggesting that AP recordings originate from different midbrain neuronal subpopulations and reveal different modulatory pathways. Our data demonstrate, for the first time, that μG-SCD-MEAs are multi-functional biosensors suitable to resolve real-time DA release and AP firing in in vitro neuronal networks.