Unitary Events in Multiple Single-Neuron Spiking Activity: I. Detection and Significance

2002 ◽  
Vol 14 (1) ◽  
pp. 43-80 ◽  
Author(s):  
Sonja Grün ◽  
Markus Diesmann ◽  
Ad Aertsen
Keyword(s):  
Spike Activity ◽  
Cortical Neurons ◽  
Single Neuron ◽  
Temporal Structure ◽  
Statistical Significance ◽  
Pattern Complexity ◽  
Firing Rates ◽  
Novel Method ◽  
Unitary Events ◽  
Spiking Activity

It has been proposed that cortical neurons organize dynamically into functional groups (cell assemblies) by the temporal structure of their joint spiking activity. Here, we describe a novel method to detect conspicuous patterns of coincident joint spike activity among simultaneously recorded single neurons. The statistical significance of these unitary events of coincident joint spike activity is evaluated by the joint-surprise. The method is tested and calibrated on the basis of simulated, stationary spike trains of independently firing neurons, into which coincident joint spike events were inserted under controlled conditions. The sensitivity and specificity of the method are investigated for their dependence on physiological parameters (firing rate, coincidence precision, coincidence pattern complexity) and temporal resolution of the analysis. In the companion article in this issue, we describe an extension of the method, designed to deal with nonstationary firing rates.

Unitary Events in Multiple Single-Neuron Spiking Activity: II. Nonstationary Data

2002 ◽  
Vol 14 (1) ◽  
pp. 81-119 ◽  
Author(s):  
Sonja Grün ◽  
Markus Diesmann ◽  
Ad Aertsen
Keyword(s):  
Spike Activity ◽  
Cortical Neurons ◽  
Constant Width ◽  
Operational Definition ◽  
Event Analysis ◽  
Moving Window Analysis ◽  
Nonstationary Data ◽  
Underlying Processes ◽  
Unitary Events ◽  
Spiking Activity

In order to detect members of a functional group (cell assembly) in simultaneously recorded neuronal spiking activity, we adopted the widely used operational definition that membership in a common assembly is expressed in near-simultaneous spike activity. Unitary event analysis, a statistical method to detect the significant occurrence of coincident spiking activity in stationary data, was recently developed (see the companion article in this issue). The technique for the detection of unitary events is based on the assumption that the underlying processes are stationary in time. This requirement, however, is usually not fulfilled in neuronal data. Here we describe a method that properly normalizes for changes of rate: the unitary events by moving window analysis (UEMWA). Analysis for unitary events is performed separately in overlapping time segments by sliding a window of constant width along the data. In each window, stationarity is assumed. Performance and sensitivity are demonstrated by use of simulated spike trains of independently firing neurons, into which coincident events are inserted. If cortical neurons organize dynamically into functional groups, the occurrence of near-simultaneous spike activity should be time varying and related to behavior and stimuli. UEMWA also accounts for these potentially interesting nonstationarities and allows locating them in time. The potential of the new method is illustrated by results from multiple single-unit recordings from frontal and motor cortical areas in awake, behaving monkey.

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.

Mitral and Tufted Cells Differ in the Decoding Manner of Odor Maps in the Rat Olfactory Bulb

2004 ◽  
Vol 91 (6) ◽  
pp. 2532-2540 ◽  
Author(s):  
Shin Nagayama ◽  
Yuji K. Takahashi ◽  
Yoshihiro Yoshihara ◽  
Kensaku Mori
Keyword(s):  
Olfactory Bulb ◽  
Spike Activity ◽  
Homologous Series ◽  
Morphological Difference ◽  
Aliphatic Acid ◽  
Mitral Cells ◽  
Firing Rates ◽  
Aliphatic Acids ◽  
Projection Pattern ◽  
Tufted Cells

Mitral and tufted cells in the mammalian olfactory bulb are principal neurons, each type having distinct projection pattern of their dendrites and axons. The morphological difference suggests that mitral and tufted cells are functionally distinct and may process different aspects of olfactory information. To examine this possibility, we recorded odorant-evoked spike responses from mitral and middle tufted cells in the aliphatic acid- and aldehyde-responsive cluster at the dorsomedial part of the rat olfactory bulb. Homologous series of aliphatic acids and aldehydes were used for odorant stimulation. In response to adequate odorants, mitral cells showed spike responses with relatively low firing rates, whereas middle tufted cells responded with higher firing rates. Examination of the molecular receptive range (MRR) indicated that most mitral cells exhibited a robust inhibitory MRR, whereas a majority of middle tufted cells showed no or only a weak inhibitory MRR. In addition, structurally different odorants that activated neighboring clusters inhibited the spike activity of mitral cells, whereas they caused no or only a weak inhibition in the middle tufted cells. Furthermore, responses of mitral cells to an adequate excitatory odorant were greatly inhibited by mixing the odorant with other odorants that activated neighboring glomeruli. In contrast, odorants that activated neighboring glomeruli did not significantly inhibit the responses of middle tufted cells to the adequate excitatory odorant. These results indicate a clear difference between mitral and middle tufted cells in the manner of decoding the glomerular odor maps.

scholarly journals Dissociation between sustained single-neuron spiking and transient β-LFP oscillations in primate motor cortex

2017 ◽  
Vol 117 (4) ◽  
pp. 1524-1543 ◽  
Author(s):  
Michael E. Rule ◽  
Carlos E. Vargas-Irwin ◽  
John P. Donoghue ◽  
Wilson Truccolo
Keyword(s):  
Steady State ◽  
Motor Cortex ◽  
Local Field ◽  
Single Neuron ◽  
Field Potential ◽  
Movement Preparation ◽  
Phase Coupling ◽  
Firing Rates ◽  
Sources Of Variation ◽  
Transient Events

Determining the relationship between single-neuron spiking and transient (20 Hz) β-local field potential (β-LFP) oscillations is an important step for understanding the role of these oscillations in motor cortex. We show that whereas motor cortex firing rates and beta spiking rhythmicity remain sustained during steady-state movement preparation periods, β-LFP oscillations emerge, in contrast, as short transient events. Single-neuron mean firing rates within and outside transient β-LFP events showed no differences, and no consistent correlation was found between the beta oscillation amplitude and firing rates, as was the case for movement- and visual cue-related β-LFP suppression. Importantly, well-isolated single units featuring beta-rhythmic spiking (43%, 125/292) showed no apparent or only weak phase coupling with the transient β-LFP oscillations. Similar results were obtained for the population spiking. These findings were common in triple microelectrode array recordings from primary motor (M1), ventral (PMv), and dorsal premotor (PMd) cortices in nonhuman primates during movement preparation. Although beta spiking rhythmicity indicates strong membrane potential fluctuations in the beta band, it does not imply strong phase coupling with β-LFP oscillations. The observed dissociation points to two different sources of variation in motor cortex β-LFPs: one that impacts single-neuron spiking dynamics and another related to the generation of mesoscopic β-LFP signals. Furthermore, our findings indicate that rhythmic spiking and diverse neuronal firing rates, which encode planned actions during movement preparation, may naturally limit the ability of different neuronal populations to strongly phase-couple to a single dominant oscillation frequency, leading to the observed spiking and β-LFP dissociation. NEW & NOTEWORTHY We show that whereas motor cortex spiking rates and beta (~20 Hz) spiking rhythmicity remain sustained during steady-state movement preparation periods, β-local field potential (β-LFP) oscillations emerge, in contrast, as transient events. Furthermore, the β-LFP phase at which neurons spike drifts: phase coupling is typically weak or absent. This dissociation points to two sources of variation in the level of motor cortex beta: one that impacts single-neuron spiking and another related to the generation of measured mesoscopic β-LFPs.

scholarly journals Local Cerebral Blood Flow with Fentanyl-Induced Seizures

1984 ◽  
Vol 4 (1) ◽  
pp. 88-95 ◽  
Author(s):  
Tsuyoshi Maekawa ◽  
Concezione Tommasino ◽  
Harvey M. Shapiro
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Spike Activity ◽  
Seizure Activity ◽  
Statistical Significance ◽  
Brain Structures ◽  
Local Cerebral Blood Flow ◽  
Progressive Development ◽  
Suppression Phase ◽  
Autoradiographic Technique

Local cerebral blood flow (LCBF) was evaluated with the [14C]iodoantipyrine quantitative autoradiographic technique in 29 brain structures in conscious control rats and during fentanyl-induced electroencephalographic (EEG) spike and/or seizure activity and in the postseizure EEG suppression phase. During spike activity, LCBF increased in all structures; the increase reached statistical significance (p < 0.05) in the superior colliculus, sensorimotor cortex, and pineal body (+ 130%, + 187%, and + 185% from control, respectively). With progressive development of seizure activity, LCBF significantly increased in 24 brain structures (range, +58% to +231% from control). During the postseizure EEG suppression phase, LCBF remained elevated in all structures (+80% to +390% from control). The local cerebrovascular resistance (LCVR) significantly decreased in 10 of 29 structures with the onset of spike activity (range, –24% to –64%), and remained decreased in all brain structures during seizure activity (range, –34% to –67%) and during the EEG suppression phase (range, –24% to –74%). This reduction of LCVR represents a near maximal state of cerebrovasodilation during fentanyl-induced EEG seizure or postseizure suppression activity. The global nature of the LCBF elevation indicates that factors other than local metabolic control are responsible for CBF regulation during local seizure activity.

scholarly journals Comparative Study of Contact Repulsion in Control and Mutant Macrophages Using a Novel Interaction Detection

2020 ◽  
Vol 6 (5) ◽  
pp. 36
Author(s):  
José Alonso Solís-Lemus ◽  
Besaiz J Sánchez-Sánchez ◽  
Stefania Marcotti ◽  
Mubarik Burki ◽  
Brian Stramer ◽  
...  
Keyword(s):  
Computational Analysis ◽  
Statistical Significance ◽  
Contact Dynamics ◽  
Drosophila Embryo ◽  
Interaction Detection ◽  
Future Developments ◽  
Novel Method ◽  
Candidate Protein ◽  
Motion Characteristics ◽  
Mutant Cells

In this paper, a novel method for interaction detection is presented to compare the contact dynamics of macrophages in the Drosophila embryo. The study is carried out by a framework called macrosight, which analyses the movement and interaction of migrating macrophages. The framework incorporates a segmentation and tracking algorithm into analysing the motion characteristics of cells after contact. In this particular study, the interactions between cells is characterised in the case of control embryos and Shot mutants, a candidate protein that is hypothesised to regulate contact dynamics between migrating cells. Statistical significance between control and mutant cells was found when comparing the direction of motion after contact in specific conditions. Such discoveries provide insights for future developments in combining biological experiments with computational analysis.

Pattern Filtering for Detection of Neural Activity, with Examples from HVc Activity During Sleep in Zebra Finches

2003 ◽  
Vol 15 (10) ◽  
pp. 2307-2337 ◽  
Author(s):  
Zhiyi Chi ◽  
Peter L. Rauske ◽  
Daniel Margoliash
Keyword(s):  
Neural Coding ◽  
Point Processes ◽  
Statistical Significance ◽  
Test Procedure ◽  
Surrogate Data ◽  
Likelihood Ratios ◽  
Alignment Procedure ◽  
Auditory Evoked Response ◽  
The Individual ◽  
Spiking Activity

The detection of patterned spiking activity is important in the study of neural coding. A pattern filtering approach is developed for pattern detection under the framework of point processes, which offers flexibility in combining temporal details and firing rates. The detection combines multiple steps of filtering in a coarse-to-fine manner. Under some conditional Poisson assumptions on the spiking activity, each filtering step is equivalent to classifying by likelihood ratios all the data segments as targets or as background sequences. Unlike previous studies, where global surrogate data were used to evaluate the statistical significance of the detected patterns, a localizedp-test procedure is developed, which better accounts for firing modulation and nonstationarity in spiking activity. Common temporal structures of patterned activity are learned using an entropy-based alignment procedure, without relying on metrics or pair-wise alignment. Applications of pattern filtering to single, presumptive interneurons recorded in the nucleus HVc of zebra finch are illustrated. These demonstrate a match between the auditory-evoked response to playback of the individual bird's own song and spontaneous activity during sleep. Small temporal compression or expansion, or both, is required for optimal matching of spontaneous patterns to stimulus-evoked activity.

Variability in somatosensory cortical neuron discharge: effects on capacity to signal different stimulus conditions using a mean rate code

1978 ◽  
Vol 41 (2) ◽  
pp. 338-349 ◽  
Author(s):  
R. C. Schreiner ◽  
G. K. Essick ◽  
B. L. Whitsel
Keyword(s):  
Firing Rate ◽  
Linear Equation ◽  
Cortical Neuron ◽  
Single Neuron ◽  
Rate Code ◽  
Firing Rates ◽  
Discrimination Criterion ◽  
The Mean ◽  
Change In Mean ◽  
The Relationship

1. The present study is based on the demonstration (8, 9) that the relationship between mean interval (MI) and standard deviation (SD) for stimulus-driven activity recorded from SI neurons is well fitted by the linear equation SD = a X MI + b and on the observations that the values of the slope (a) and y intercept (b) parameters of this relationship are independent of stimulus conditions and may vary widely from one neuron to the next (8). 2. A criterion for the discriminability of two different mean firing rates requiring that the mean intervals of their respective interspike interval (ISI) distributions be separated by a fixed interval (expressed in SD units) is developed and, on the basis of this criterion, a graphical display of the capacity of a neuron with a known SD-MI relationship to reflect a change in stimulus conditions with a change in mean firing rate is derived. Using this graphical approach, it is shown that the parameters of the SD-MI relationship for a single neuron determine a range of firing frequencies, within which that neuron exhibits the greatest capacity to signal differences in stimulus conditions using a frequency code. 3. The discrimination criterion is modified to incorporate the changes in the symmetry of the ISI distribution observed to accompany changes in mean firing rate. It is shown that, although the observed symmetry changes do influence the capacity of a cortical neuron to signal a change in stimulus conditions with a change in mean firing rate, they do not alter the range of firing rates (determined by the parameters of the SD-MI relationship) within which the capacity for discrimination is maximal. 4. The maximal number of firing levels that can be distinguished by a somatosensory cortical neuron (using the same discrimination criterion described above) discharging within a specified range of mean frequencies also is demonstrated to depend on the parameters of the linear equation which relates SD to MI. 5. Two approaches based on the t test for differences between two means are developed in an attempt to ascertain the minimum separation of the mean intervals of the ISI distributions necessary for two different mean firing rates to be discriminated with 80% certainty.

scholarly journals Computing and Stability in Cortical Networks

2004 ◽  
Vol 16 (7) ◽  
pp. 1385-1412 ◽  
Author(s):  
Peter E. Latham ◽  
Sheila Nirenberg
Keyword(s):  
Cortical Neurons ◽  
Unstable State ◽  
Stable States ◽  
Attractor Networks ◽  
Firing Rates ◽  
The Face ◽  
Multiple Stable States ◽  
Background State ◽  
Do So

Cortical neurons are predominantly excitatory and highly interconnected. In spite of this, the cortex is remarkably stable: normal brains do not exhibit the kind of runaway excitation one might expect of such a system. How does the cortex maintain stability in the face of this massive excitatory feedback? More importantly, how does it do so during computations, which necessarily involve elevated firing rates? Here we address these questions in the context of attractor networks—networks that exhibit multiple stable states, or memories. We find that such networks can be stabilized at the relatively low firing rates observed in vivo if two conditions are met: (1) the background state, where all neurons are firing at low rates, is inhibition dominated, and (2) the fraction of neurons involved in a memory is above some threshold, so that there is sufficient coupling between the memory neurons and the background. This allows “dynamical stabilization” of the attractors, meaning feedback from the pool of background neurons stabilizes what would otherwise be an unstable state. We suggest that dynamical stabilization may be a strategy used for a broad range of computations, not just those involving attractors.

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