Temporal Interaction between Single Spikes and Complex Spike Bursts in Hippocampal Pyramidal Cells

Harris, Kenneth D.; Hirase, Hajime; Leinekugel, Xavier; Henze, Darrell A.; Buzsáki, György

doi:10.1016/s0896-6273(01)00447-0

Cited by 354 publications

(366 citation statements)

References 42 publications

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“…In particular, we would like to single out the deep and important statistical challenges associated with the analysis of multiple simultaneously recorded spike trains . Two conspicuous difficulties are the complexity of spike sorting (e.g., Bar-Gad et al 2001;Harris et al 2001) and the absence of well-developed statistical methods for multiple point process data analysis. In addition, there are algorithmic challenges associated with construction of effective brain-machine interfaces, which is one of the exciting applications of multiple spike-train signal processing.…”

Section: Discussionmentioning

confidence: 99%

Statistical Issues in the Analysis of Neuronal Data

Kass¹,

Ventura²,

Brown³

2005

Journal of Neurophysiology

252

239

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Kass, Robert E., Valérie Ventura, and Emery N. Brown. Statistical issues in the analysis of neuronal data. J Neurophysiol 94: 8 -25, 2005; doi:10.1152/jn.00648.2004. Analysis of data from neurophysiological investigations can be challenging. Particularly when experiments involve dynamics of neuronal response, scientific inference can become subtle and some statistical methods may make much more efficient use of the data than others. This article reviews wellestablished statistical principles, which provide useful guidance, and argues that good statistical practice can substantially enhance results. Recent work on estimation of firing rate, population coding, and time-varying correlation provides improvements in experimental sensitivity equivalent to large increases in the number of neurons examined. Modern nonparametric methods are applicable to data from repeated trials. Many within-trial analyses based on a Poisson assumption can be extended to non-Poisson data. New methods have made it possible to track changes in receptive fields, and to study trial-to-trial variation, with modest amounts of data. I N T R O D U C T I O NTechnical advances have made available new methods for collecting, storing, and manipulating electrophysiological data. Investigations may now not only characterize neuronal activity in anatomically well defined regions, but they can also examine dynamics of neuronal response and their relationship to behavior. Although elementary methods of data analysis [such as t-tests or visual examination of the peristimulus time histogram (PSTH)] remain useful for many purposes, the growing complexity of neuroscientific experiments, often examining subtle changes on a comparatively fine timescale, requires careful attention to statistical methods for data analysis. In this overview we discuss some of the fundamental data analytical issues that face researchers in neurophysiology, illustrating the general points with the problems of describing the evolution of a neuron's firing rate across time, finding accurate population codes, and assessing time-varying correlation between 2 neurons. In each case recent work has provided a statistical technique that outperforms previous methodology, boosting the scientific information as effectively as if the number of experimental trials, or the number of neurons, had been increased by a substantial factor. We also indicate some of the ways modern statistical procedures can accommodate important complexities, such as dynamic changes in temporal and spatial aspects of hippocampal place cell firing and trial-to-trial variability in cortical neurons recorded from behaving animals. Our review supplements the brief and general guidance offered by Curran-Everett and Benos (2004), and may be regarded as an update to the early work of Perkel et al. (1967a,b).The new field of computational neuroscience uses detailed biophysical models and artificial neural networks to study emergent behavior of neural systems and the way neural systems represent and transmit information (e.g...

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Section: Discussionmentioning

confidence: 99%

Statistical Issues in the Analysis of Neuronal Data

Kass¹,

Ventura²,

Brown³

2005

Journal of Neurophysiology

252

239

View full text Add to dashboard Cite

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“…When neurons are strongly activated, their amplitude, rise time, decay time and spike width change as a function of the membrane potential before the spike and as a function of the immediate firing history of the neurons. The spike amplitude variation is most substantial during complex spike burst production, with as much as 80% amplitude reduction, primarily because of Na ϩ channel inactivation (Harris et al, 2001). Therefore, amplitude and waveform-based classification programs will separate action potentials from a single neuron as if they were emitted by many.…”

Section: Isolation and Identification Of Neurons By Extracellular Sigmentioning

confidence: 99%

Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities

Kipke¹,

Shain²,

Buzsáki³

et al. 2008

J. Neurosci.

261

176

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IntroductionTechnological advances in neural interfaces are providing increasingly more powerful "toolkits" of designs, materials, components, and integrated devices for establishing high-fidelity chronic neural interfaces. For a broad class of neuroscience studies, the primary requirements of these interfaces include recording and/or stimulating from a number of discretely sampled volumes at requisite spatial resolutions for specific periods of time. Translational and clinical applications present additional requirements for safety, usability, reliability, patient acceptance, and cost effectiveness. Innovative solutions result from the constructive tension between ever-increasing application requirements and incorporation of technological advances into usable devices. The purpose of this minireview is to present snapshots of the current state-of-the-art in chronic, microscale neural interfaces by highlighting several leading neuroscience applications and discussing their implications for next-generation interface devices.

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“…Recordings in behaving animals indeed show that four to seven spikes usually occur within a Theta wave time window. Each spike can be produced by a separate neuron that in turn could be part of a separate active neuronal network, altogether encoding different contents (O'Keefe and Recce 1993; Harris et al 2001). An increase in Theta amplitude could interfere with the correct timing of individual spikes and could affect WM storage by interfering with the correct order of items.…”

Section: How Can Increased Theta Affect Wm?mentioning

confidence: 99%

Lack of the metabotropic glutamate receptor subtype 7 selectively modulates Theta rhythm and working memory

Hölscher

Schmid²,

Pilz³

et al. 2005

Learn. Mem.

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Metabotropic glutamate receptors (mGluRs) are known to play a role in synaptic plasticity and learning. We have previously shown that mGluR7 deletion in mice produces a selective working memory (WM) impairment, while other types of memory such as reference memory remain unaffected. Since WM has been associated with Theta activity (6-12 Hz) in EEGs, and since EEG abnormalities have been observed in these mice before, we studied the effect of mGluR7 gene ablation on EEG activity in the hippocampus, in particular in the Theta range, during performance of a WM task. In an eight-arm maze with four arms baited, mGluR7 knock-out (KO) and wild-type mice committed the same number of reference memory errors, whereas KOs committed more WM errors. While performing the task, KO mice showed substantially higher Theta amplitudes, and the ratio of Theta to overall EEG power was much increased. No change was seen in the Delta (0-5 Hz), or Gamma (30-40 Hz) EEG bands compared with controls. When recording EEGs during periods of rest in the home cages, no difference was seen between groups. These findings suggest that mGluR7 is important for modulation and control of Theta activity. Since only WM was affected, and only the Theta range of EEG activity was altered, these results show a correlation between Theta rhythm and WM performance, and therefore support the concept that Theta activity in the hippocampus is involved in WM storage.Metabotropic glutamate receptors (mGluRs) are a family of glutamate-sensitive receptors that are coupled to intracellular signal transduction via G-proteins (Pin and Duvoisin 1995). It is known that mGluRs play an important role in the regulation of CNS synaptic plasticity, and also in some processes of memory formation. Pharmacological blockade of mGluR group III receptors (subtypes 4, 6, 7, and 8) has profound effects on the induction of synaptic plasticity (Bashir et al. 1993;Richter-Levin et al. 1994;Hölscher et al. 1997a), synaptic depression (Manahan-Vaughan 2000;Weber et al. 2002), and spatial learning (Hölscher et al. 1996(Hölscher et al. , 1999.In a previous study, we tested the effect of mGluR7 deletion on different forms of learning and memory formation (Hölscher 2003). We showed that mGluR7-deficient (knock-out; KO) mice were not impaired in a non-associative habituation task or in performing reference memory tasks of different complexities (open field, T-maze, four-and eight-arm mazes). However, in tasks that require increased capacity of working memory (WM) performance, mGluR7 KO mice showed clear deficits.WM contains task-related information required to develop plans of action and to guide behavior (Baddeley and Hitch 1974;Olton et al. 1979). In the present paper, we adhere to the definition of WM by Olton et al. (1979), who emphasized the importance of hippocampal activity for supporting WM. How WM is actually encoded and stored in the brain has been under investigation for a long time. The occurrence of oscillations in the EEG of subjects that performed WM tasks had been observed. In pa...

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Temporal Interaction between Single Spikes and Complex Spike Bursts in Hippocampal Pyramidal Cells

Cited by 354 publications

References 42 publications

Statistical Issues in the Analysis of Neuronal Data

Statistical Issues in the Analysis of Neuronal Data

Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities

Lack of the metabotropic glutamate receptor subtype 7 selectively modulates Theta rhythm and working memory

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