51,933 research outputs found

    Modeled channel distributions explain extracellular recordings from cultured neurons sealed to microelectrodes

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    Amplitudes and shapes of extracellular recordings from single neurons cultured on a substrate embedded microelectrode depend not only on the volume conducting properties of the neuron-electrode interface, but might also depend on the distribution of voltage-sensitive channels over the neuronal membrane. In this paper, finite-element modeling is used to quantify the effect of these channel distributions on the neuron-electrode contact. Slight accumulation or depletion of voltage-sensitive channels in the sealing membrane of the neuron results in various shapes and amplitudes of simulated extracellular recordings. However, estimation of channel-specific accumulation factors from extracellular recordings can be obstructed by co-occuring ion currents and defect sealing. Experimental data from cultured neuron-electrode interfaces suggest depletion of sodium channels and accumulation of potassium channels

    Modulation of neural cell membrane conductance by the herbal anxiolytic and antiepileptic drug aswal

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    To evaluate the effects of aswal on ionic fluxes and neuronal excitation, we performed extracellular and whole cell patch clamp recordings on CA1 pyramidal neurons of guinea pigs and Long-Evans rats. Aswal (100-250 mg/l) was administered systemically, and its effects on the rate of synchronized extracellular field potentials (EFP), membrane parameters, action potentials and postsynaptic potentials were recorded. The extracellular results obtained are consistent with calcium antagonistic properties. Intracellular recordings suggest that a direct sodium antagonistic effect as seen in many antiepileptic drugs plays no significant role. Further effects on ligand gated ion channels are discussed controversially. In summary, the cellular action of aswal appears heterogeneous with calcium antagonism playing a prominent role in counteracting excitation which may be a common feature in epilepsy and different psychiatric conditions as mood and anxiety disorder. Copyright (C) 2000 S. Karger AG, Basel

    A control algorithm for autonomous optimization of extracellular recordings

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    This paper develops a control algorithm that can autonomously position an electrode so as to find and then maintain an optimal extracellular recording position. The algorithm was developed and tested in a two-neuron computational model representative of the cells found in cerebral cortex. The algorithm is based on a stochastic optimization of a suitably defined signal quality metric and is shown capable of finding the optimal recording position along representative sampling directions, as well as maintaining the optimal signal quality in the face of modeled tissue movements. The application of the algorithm to acute neurophysiological recording experiments and its potential implications to chronic recording electrode arrays are discussed

    Transcranial Electric Stimulation Entrains Cortical Neuronal Populations in Rats

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    Low intensity electric fields have been suggested to affect the ongoing neuronal activity in vitro and in human studies. However, the physiological mechanism of how weak electrical fields affect and interact with intact brain activity is not well understood. We performed in vivo extracellular and intracellular recordings from the neocortex and hippocampus of anesthetized rats and extracellular recordings in behaving rats. Electric fields were generated by sinusoid patterns at slow frequency (0.8, 1.25 or 1.7 Hz) via electrodes placed on the surface of the skull or the dura. Transcranial electric stimulation (TES) reliably entrained neurons in widespread cortical areas, including the hippocampus. The percentage of TES phase-locked neurons increased with stimulus intensity and depended on the behavioral state of the animal. TES-induced voltage gradient, as low as 1 mV/mm at the recording sites, was sufficient to phase-bias neuronal spiking. Intracellular recordings showed that both spiking and subthreshold activity were under the combined influence of TES forced fields and network activity. We suggest that TES in chronic preparations may be used for experimental and therapeutic control of brain activity

    Signal detection in extracellular neural ensemble recordings using higher criticism

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    Information processing in the brain is conducted by a concerted action of multiple neural populations. Gaining insights in the organization and dynamics of such populations can best be studied with broadband intracranial recordings of so-called extracellular field potential, reflecting neuronal spiking as well as mesoscopic activities, such as waves, oscillations, intrinsic large deflections, and multiunit spiking activity. Such signals are critical for our understanding of how neuronal ensembles encode sensory information and how such information is integrated in the large networks underlying cognition. The aforementioned principles are now well accepted, yet the efficacy of extracting information out of the complex neural data, and their employment for improving our understanding of neural networks, critically depends on the mathematical processing steps ranging from simple detection of action potentials in noisy traces - to fitting advanced mathematical models to distinct patterns of the neural signal potentially underlying intra-processing of information, e.g. interneuronal interactions. Here, we present a robust strategy for detecting signals in broadband and noisy time series such as spikes, sharp waves and multi-unit activity data that is solely based on the intrinsic statistical distribution of the recorded data. By using so-called higher criticism - a second-level significance testing procedure comparing the fraction of observed significances to an expected fraction under the global null - we are able to detect small signals in correlated noisy time-series without prior filtering, denoising or data regression. Results demonstrate the efficiency and reliability of the method and versatility over a wide range of experimental conditions and suggest the appropriateness of higher criticism to characterize neuronal dynamics without prior manipulation of the data

    Spike Clustering and Neuron Tracking over Successive Time Windows

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    This paper introduces a new methodology for tracking signals from individual neurons over time in multiunit extracellular recordings. The core of our strategy relies upon an extension of a traditional mixture model approach, with parameter optimization via expectation-maximimization (EM), to incorporate clustering results from the preceding time period in a Bayesian manner. EM initialization is also achieved by utilizing these prior clustering results. After clustering, we match the current and prior clusters to track persisting neurons. Applications of this spike sorting method to recordings from macaque parietal cortex show that it provides significantly more consistent clustering and tracking results

    Spike detection using the continuous wavelet transform

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    This paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution
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