Multiunit Normalized Cross Correlation Differs from the Average Single-Unit Normalized Correlation

1997 ◽  
Vol 9 (6) ◽  
pp. 1265-1275 ◽  
Author(s):  
Purvis Bedenbaugh ◽  
George L. Gerstein
Keyword(s):  
Single Unit ◽  
Cross Correlation ◽  
Single Neuron ◽  
Spike Trains ◽  
Normalized Cross Correlation ◽  
Neuron Spike ◽  
Rat Somatosensory Cortex ◽  
Small Clusters ◽  
The Relationship ◽  
Unit Spike

As the technology for simultaneously recording from many brain locations becomes more available, more and more laboratories are measuring the cross-correlation between single-neuron spike trains, and between composite spike trains derived from several undiscriminated cells recorded on a single electrode (multiunit clusters). The relationship between single-unit correlations and multiunit cluster correlations has not yet been fully explored. We calculated the normalized cross-correlation (NCC) between single unit spike trains and between small clusters of units recorded in the rat somatosensory cortex. The NCC between small clusters of units was larger than the NCC between single units. To understand this result, we investigated the scaling of the NCC with the number of units in a cluster. Multiunit cross-correlation can be a more sensitive detector of neuronal relationship than single-unit cross-correlation. However, changes in multiunit cross-correlation are difficult to interpret uniquely because they depend on the number of cells recorded on each electrode and because they can arise from changes in the correlation between cells recorded on a single electrode or from changes in the correlation between cells recorded on two electrodes.

Effect of stimulation on burst firing in cat primary auditory cortex

1995 ◽  
Vol 74 (5) ◽  
pp. 1841-1855 ◽  
Author(s):  
D. M. Bowman ◽  
J. J. Eggermont ◽  
G. M. Smith
Keyword(s):  
Auditory Cortex ◽  
Single Unit ◽  
Primary Auditory Cortex ◽  
Stimulus Presentation ◽  
Spike Trains ◽  
Burst Firing ◽  
Interspike Intervals ◽  
Click Train ◽  
Train Stimulation ◽  
Unit Spike

1. Neural activity was recorded extracellularly with two independent microelectrodes aligned in parallel and advanced perpendicular to isofrequency sheets in cat primary auditory cortex. Multiunit activity was separated into single-unit spike trains using a maximum variance spike sorting algorithm. Only units that demonstrated a high quality of sorting and a minimum spontaneous firing rate of 0.2 spikes/s were considered for analysis. The primary aim of this study was to describe the effect of periodic click train and broadband noise stimulation on short-time-scale (< or = 50 ms) bursts in the spike trains of single auditory cortical units and to determine whether stimulation influenced the occurrence, spike count, and/or temporal structure of burst firing relative to a spontaneous baseline. 2. Extracellular recordings were made in 20 juvenile and adult cats from 69 single auditory cortical units during click train stimulation and silence, and from 30 single units during noise stimulation and in silence. In an additional 15 single units the effect of both click train and noise stimulation was investigated. The incidence, spike count, and temporal structure of short-time-scale burst firing in the first 100 ms following stimulus presentation was compared with burst firing in the period starting 500 ms after stimulus presentation and with spontaneous burst firing. In addition, the serial dependence of interspike intervals within a burst was tested during periods of stimulation. 3. Burst firing was present in the stimulation, poststimulation, and spontaneous conditions. Longer bursts (consisting of > or = 3 spikes) were more commonly observed in the poststimulation and spontaneous conditions than in the stimulation condition. This effect was most pronounced during click stimulation. A period of elevated firing activity was present in a subset of units 0.5-1.5 s after stimulus presentation, indicating prolonged effects of stimulation on single-unit firing behavior. 4. For both stimuli, the proportion of single-unit responses composed of bursts was significantly greater in poststimulation and spontaneous periods than during stimulation. Burst rate was higher in post-click-train stimulation and spontaneous periods than during periods of click stimulation. The isolated spike rate was significantly higher during periods of noise and click stimulation than in the poststimulation and spontaneous periods. 5. An examination of the autocorrelograms and higher-order interspike interval histograms of single-unit responses during click train stimulation indicated that 25% of single-unit spike trains contained an excess of brief first-order intervals and 14% of spike trains contained a shortage of long higher-order interspike intervals relative to a spontaneous baseline. During noise stimulation, 10% of single-unit responses contained an excess of short intervals relative to baseline. Interspike intervals of short-duration bursts were not serially dependent during periods of stimulation. 6. A comparison of the autocorrelograms and higher-order interval histograms of single-unit responses in the poststimulation and spontaneous conditions indicated that 20% of single-unit spike trains contained an excess of short first-, second-, and third-order intervals following stimulation. This subgroups of single units could not be distinguished on the basis of the age of the animal or the depth at which the recording was made. 7. The low incidence of burst firing during stimulation opposes the view that bursts serve as a mechanism to emphasize or amplify particular stimulus-related responses in the presence of ongoing spontaneous activity in the primary auditory cortex. Moreover, there is little evidence to support the notion that brief bursts represent neural codes, because intraburst intervals are not serially dependent. It is suggested that pyramidal burst firing may be an effective way to evoke postsynaptic firing in inhibitory interneurons and subsequ

scholarly journals Spectrally specific temporal analyses of spike-train responses to complex sounds: A unifying framework

2021 ◽  
Vol 17 (2) ◽  
pp. e1008155
Author(s):  
Satyabrata Parida ◽  
Hari Bharadwaj ◽  
Michael G. Heinz
Keyword(s):  
Animal Models ◽  
Spike Train ◽  
Single Unit ◽  
Spike Trains ◽  
Temporal Coding ◽  
Evoked Responses ◽  
Complex Sounds ◽  
Neural Data ◽  
Preclinical Animal Models ◽  
Unit Spike

Significant scientific and translational questions remain in auditory neuroscience surrounding the neural correlates of perception. Relating perceptual and neural data collected from humans can be useful; however, human-based neural data are typically limited to evoked far-field responses, which lack anatomical and physiological specificity. Laboratory-controlled preclinical animal models offer the advantage of comparing single-unit and evoked responses from the same animals. This ability provides opportunities to develop invaluable insight into proper interpretations of evoked responses, which benefits both basic-science studies of neural mechanisms and translational applications, e.g., diagnostic development. However, these comparisons have been limited by a disconnect between the types of spectrotemporal analyses used with single-unit spike trains and evoked responses, which results because these response types are fundamentally different (point-process versus continuous-valued signals) even though the responses themselves are related. Here, we describe a unifying framework to study temporal coding of complex sounds that allows spike-train and evoked-response data to be analyzed and compared using the same advanced signal-processing techniques. The framework uses a set of peristimulus-time histograms computed from single-unit spike trains in response to polarity-alternating stimuli to allow advanced spectral analyses of both slow (envelope) and rapid (temporal fine structure) response components. Demonstrated benefits include: (1) novel spectrally specific temporal-coding measures that are less confounded by distortions due to hair-cell transduction, synaptic rectification, and neural stochasticity compared to previous metrics, e.g., the correlogram peak-height, (2) spectrally specific analyses of spike-train modulation coding (magnitude and phase), which can be directly compared to modern perceptually based models of speech intelligibility (e.g., that depend on modulation filter banks), and (3) superior spectral resolution in analyzing the neural representation of nonstationary sounds, such as speech and music. This unifying framework significantly expands the potential of preclinical animal models to advance our understanding of the physiological correlates of perceptual deficits in real-world listening following sensorineural hearing loss.

Statistics of Midbrain Dopamine Neuron Spike Trains in the Awake Primate

2007 ◽  
Vol 98 (3) ◽  
pp. 1428-1439 ◽  
Author(s):  
Hannah M. Bayer ◽  
Brian Lau ◽  
Paul W. Glimcher
Keyword(s):  
Prediction Error ◽  
Low Frequency ◽  
Spike Trains ◽  
Dopamine Neuron ◽  
Dopamine Neurons ◽  
Neuron Spike ◽  
The Difference ◽  
Midbrain Dopamine ◽  
Gamma Processes ◽  
The Relationship

Work in behaving primates indicates that midbrain dopamine neurons encode a prediction error, the difference between an obtained reward and the reward expected. Studies of dopamine action potential timing in the alert and anesthetized rat indicate that dopamine neurons respond in tonic and phasic modes, a distinction that has been less well characterized in the primates. We used spike train models to examine the relationship between the tonic and burst modes of activity in dopamine neurons while monkeys were performing a reinforced visuo-saccadic movement task. We studied spiking activity during four task-related intervals; two of these were intervals during which no task-related events occurred, whereas two were periods marked by task-related phasic activity. We found that dopamine neuron spike trains during the intervals when no events occurred were well described as tonic. Action potentials appeared to be independent, to occur at low frequency, and to be almost equally well described by Gaussian and Poisson-like (gamma) processes. Unlike in the rat, interspike intervals as low as 20 ms were often observed during these presumptively tonic epochs. Having identified these periods of presumptively tonic activity, we were able to quantitatively define phasic modulations (both increases and decreases in activity) during the intervals in which task-related events occurred. This analysis revealed that the phasic modulations of these neurons include both bursting, as has been described previously, and pausing. Together bursts and pauses seemed to provide a continuous, although nonlinear, representation of the theoretically defined reward prediction error of reinforcement learning.

scholarly journals Stochastic optimal control of single neuron spike trains

2014 ◽  
Vol 11 (4) ◽  
pp. 046004 ◽  
Author(s):  
Alexandre Iolov ◽  
Susanne Ditlevsen ◽  
André Longtin
Keyword(s):  
Optimal Control ◽  
Stochastic Optimal Control ◽  
Single Neuron ◽  
Spike Trains ◽  
Neuron Spike

Central Versus Peripheral Determinants of Patterned Spike Activity in Rat Vibrissa Cortex During Whisking

1997 ◽  
Vol 78 (2) ◽  
pp. 1144-1149 ◽  
Author(s):  
Michale S. Fee ◽  
Partha P. Mitra ◽  
David Kleinfeld
Keyword(s):  
Spike Activity ◽  
Single Unit ◽  
Motor Nerve ◽  
Reference Signal ◽  
Positional Information ◽  
Oscillatory Component ◽  
Single Spike ◽  
Sensory Activation ◽  
The Relationship ◽  
Unit Spike

Fee, Michale S., Partha P. Mitra, and David Kleinfeld. Central versus peripheral determinants of patterned spike activity in rat vibrissa cortex during whisking. J. Neurophysiol. 78: 1144–1149, 1997. We report on the relationship between single-unit activity in primary somatosensory vibrissa cortex of rat and the rhythmic movement of vibrissae. Animals were trained to whisk freely in air in search of food. Electromyographic (EMG) recordings from the mystatial pads served as a reference for the position of the vibrissae. A fast, oscillatory component in single-unit spike trains is correlated with vibrissa position within the whisk cycle. The phase of the correlation for different units is broadly distributed. A second, slowly varying component of spike activity correlates with the amplitude of the whisk cycle. For some units, the phase and amplitude correlations were of sufficient strength to allow the position of the whiskers to be accurately predicted from a single spike train. To determine whether the observed patterned spike activity was driven by motion of the vibrissae, as opposed to central pathways, we reversibly blocked the contralateral facial motor nerve during the behavioral task so that the rat whisked only on the ipsilateral side. The ipsilateral EMG served as a reliable reference signal. The fast, oscillatory component of the spike-EMG correlation disappears when the facial motor nerve is blocked. This implies that the position of vibrissae within a cycle is encoded through direct sensory activation. The slowly varying component of the spike-EMG correlation is unaffected by the block. This implies that the amplitude of whisking is likely to be mediated by corollary discharge. Our results suggest that motor cortex does not relay a reference signal to sensory cortex for positional information of the vibrissae during whisking.

The relationship of IGE receptor topography to signaling activity in RBL-2H3 mast cells

1991 ◽  
Vol 49 ◽  
pp. 236-237
Author(s):  
J.R. Pfeiffer ◽  
J.C. Seagrave ◽  
C. Wofsy ◽  
J.M. Oliver
Keyword(s):  
Mast Cells ◽  
Protein A ◽  
Scattered Electron ◽  
Ige Receptor ◽  
Receptor Complexes ◽  
Ige Binding ◽  
Electron Imaging ◽  
Small Clusters ◽  
Relationship Of ◽  
The Relationship

In RBL-2H3 rat leukemic mast cells, crosslinking IgE-receptor complexes with anti-IgE antibody leads to degranulation. Receptor crosslinking also stimulates the redistribution of receptors on the cell surface, a process that can be observed by labeling the anti-IgE with 15 nm protein A-gold particles as described in Stump et al. (1989), followed by back-scattered electron imaging (BEI) in the scanning electron microscope. We report that anti-IgE binding stimulates the redistribution of IgE-receptor complexes at 37“C from a dispersed topography (singlets and doublets; S/D) to distributions dominated sequentially by short chains, small clusters and large aggregates of crosslinked receptors. These patterns can be observed (Figure 1), quantified (Figure 2) and analyzed statistically. Cells incubated with 1 μg/ml anti-IgE, a concentration that stimulates maximum net secretion, redistribute receptors as far as chains and small clusters during a 15 min incubation period. At 3 and 10 μg/ml anti-IgE, net secretion is reduced and the majority of receptors redistribute rapidly into clusters and large aggregates.

scholarly journals Information diversity in individual auditory cortical neurons is associated with functionally distinct coordinated neuronal ensembles

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jermyn Z. See ◽  
Natsumi Y. Homma ◽  
Craig A. Atencio ◽  
Vikaas S. Sohal ◽  
Christoph E. Schreiner
Keyword(s):  
Cortical Neurons ◽  
Single Neuron ◽  
Receptive Fields ◽  
Acoustic Feature ◽  
Neuronal Ensembles ◽  
Neuron Spike ◽  
Information Diversity ◽  
Feature Selectivity ◽  
Stimulus Features ◽  
Time Specific

AbstractNeuronal activity in auditory cortex is often highly synchronous between neighboring neurons. Such coordinated activity is thought to be crucial for information processing. We determined the functional properties of coordinated neuronal ensembles (cNEs) within primary auditory cortical (AI) columns relative to the contributing neurons. Nearly half of AI cNEs showed robust spectro-temporal receptive fields whereas the remaining cNEs showed little or no acoustic feature selectivity. cNEs can therefore capture either specific, time-locked information of spectro-temporal stimulus features or reflect stimulus-unspecific, less-time specific processing aspects. By contrast, we show that individual neurons can represent both of those aspects through membership in multiple cNEs with either high or absent feature selectivity. These associations produce functionally heterogeneous spikes identifiable by instantaneous association with different cNEs. This demonstrates that single neuron spike trains can sequentially convey multiple aspects that contribute to cortical processing, including stimulus-specific and unspecific information.

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